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5G Use Cases

31 May

Self-protection Use Case

GOAL: Detect and mitigate effects of cyber-attacks and restore 5G network traffic to a steady state of security.
HOW: VNFs (virtual Traffic monitor/DPI, virtual Threat Management System, virtual honeynets, virtual Intrusion Protection System) deployed and chained at different locations of the network (e.g., at the mobile access, PoP or in the core).
• New way of deploying multi-tenant security services distributed across edge and core 5G networks.
• New business opportunities for network and service providers (security as a service).

Self-healing Use Case

GOAL: Detect and predict common failures/malfunctioning in 5G network infrastructure (hw/sw failures, infrastructure/operation vulnerabilities or power supply interruptions) to apply reactive or preventive recovery.
HOW: Self-healing analyzer to infer Health of Network metrics coupled with self-healing diagnosis intelligence to derive potential problems. Decision making intelligence to realize proactive healing responses.
• Intelligent management capabilities to improve the QoE/QoS of 5G systems.
• Infrastructure metrics and SLAs indicators to infer HoN metrics and implement context-aware decisions in 5G Control Plane.

Self-optimisation Use Case

GOAL: Autonomic behaviors to automatically respond to degradation of QoE levels (either actual or predicted), coupled with end-to-end proactive energy management for optimized resource deployment across the 5G network.
HOW: SELFNET monitoring and analysis tools to either observe or predict massive video traffic loads; self-adjusting traffic management mechanisms for reduction of delay and loss in video, placing intelligent encoding and packet marking schemes.
• Sensors, actuators and decision making logic to realize QoE-based video streaming.
• Novel energy monitoring sensors to develop a global view of energy usage across the network.

Hybrid Use Case

This use case aims to provide a complex hybrid use case in which all the previous self-organising functionalities will be integrated and will interwork together to present a complete scenario where the SELFNET Apps act vertically in solving problems in a coordinated fashion.

Source: 31 05 20

Is Mobile Network Future Already Written?

25 Aug

5G, the new generation of mobile communication systems with its well-known ITU 2020 triangle of new capabilities, which not only include ultra-high speeds but also ultra-low latency, ultra-high reliability, and massive connectivity promise to expand the applications of mobile communications to entirely new and previously unimagined “vertical industries” and markets such as self-driving cars, smart cities, industry 4.0, remote robotic surgery, smart agriculture, and smart energy grids. The mobile communications system is already one of the most complex engineering systems in the history of mankind. As 5G network penetrates deeper and deeper into the fabrics of the 21st century society, we can also expect an exponential increase in the level of complexity in design, deployment, and management of future mobile communication networks which, if not addressed properly, have the potential of making 5G the victim of its own early successes.

Breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML), including deep neural networks and probability models, are creating paths for computing technology to perform tasks that once seemed out of reach. Taken for granted today, speech recognition and instant translation once appeared intractable, and the board game ‘Go’ had long been regarded as a case testing the limits of AI. With the recent win of Google’s ‘AlphaGo’ machine over world champion Lee Sedol — a solution considered by some experts to be at least a decade further away — was achieved using a ML-based process trained both from human and computer play. Self-driving cars are another example of a domain long considered unrealistic even just a few years ago — and now this technology is among the most active in terms of industry investment and expected success. Each of these advances is a demonstration of the coming wave of as-yet-unrealized capabilities. AI, therefore, offers many new opportunities to meet the enormous new challenges of design, deployment, and management of future mobile communication networks in the era of 5G and beyond, as we illustrate below using a number of current and emerging scenarios.

Network Function Virtualization Design with AI

Network Function Virtualization (NFV) [1] has recently attracted telecom operators to migrate network functionalities from expensive bespoke hardware systems to virtualized IT infrastructures where they are deployed as software components. A fundamental architectural aspect of the 5G network is the ability to create separate end-to-end slices to support 5G’s heterogeneous use cases. These slices are customised virtual network instances enabled by NFV. As the use cases become well-defined, the slices need to evolve to match the changing users’ requirements, ideally in real time. Therefore, the platform needs not only to adapt based on feedback from vertical applications, but also do so in an intelligent and non-disruptive manner. To address this complex problem, we have recently proposed the 5G NFV “microservices” concept, which decomposes a large application into its sub-components (i.e., microservices) and deploys them in a 5G network. This facilitates a more flexible, lightweight system, as smaller components are easier to process. Many cloud-computing companies, such as Netflix and Amazon, are deploying their applications using the microservice approach benefitting from its scalability, ease of upgrade, simplified development, simplified testing, less vulnerability to security attacks, and fault tolerance [6]. Expecting the potential significant benefits of such an approach in future mobile networks, we are developing machine-learning-aided intelligent and optimal implementation of the microservices and DevOps concepts for software-defined 5G networks. Our machine learning engine collects and analyse a large volume of real data to predict Quality of Service (QoS) and security effects, and take decisions on intelligently composing/decomposing services, following an observe-analyse-learn- and act cognitive cycle.

We define a three-layer architecture, as depicted in Figure 1, composing of service layer, orchestration layer, and infrastructure layer. The service layer will be responsible for turning user’s requirements into a service function chain (SFC) graph and giving the SFC graph output to the orchestration layer to deploy it into the infrastructure layer. In addition to the orchestration layer, components specified by NFV MANO [1], the orchestration layer will have the machine learning prediction engine which will be responsible for analysing network conditions/data and decompose the SFC graph or network functions into a microservice graph depending on future predictions. The microservice graph is then deployed into the infrastructure layer using the orchestration framework proposed by NFV-MANO.

Figure 1: Machine learning based network function decomposition and composition architecture.

Figure 1: Machine learning based network function decomposition and composition architecture.

Physical Layer Design Beyond-5G with Deep-Neural Networks

Deep learning (DL) based auto encoder (AE) has been proposed recently as a promising, and potentially disruptive Physical Layer (PHY) design for beyond-5G communication systems. DL based approaches offer a fundamentally new and holistic approach to the physical layer design problem and hold the promise for performance enhancement in complex environments that are difficult to characterize with tractable mathematical models, e.g., for the communication channel [2]. Compared to a traditional communication system, as shown in Figure 2 (top) with a multiple-block structure, the DL based AE, as shown in Figure 2 (bottom), provides a new PHY paradigm with a pure data-driven and end-to-end learning based solution which enables the physical layer to redesign itself through the learning process in order to optimally perform in different scenarios and environment. As an example, time evolution of the constellations of two auto encoder transmit-receiver pairs are shown in Figure 3 which starting from an identical set of constellations use DL-based learning to achieve optimal constellations in the presence of mutual interference [3].

Figure 2: A conventional transceiver chain consisting of multiple signal processing blocks (top) is replaced by a DL-based auto encoder (bottom).

Figure 2: A conventional transceiver chain consisting of multiple signal processing blocks (top) is replaced by a DL-based auto encoder (bottom).
Figure 3: Visualization of DL-based adaption of constellations in the interface scenario of two auto encoder transmit-receiver pairs (Gif animation included in online version. Animation produced by Lloyd Pellatt, University of Sussex).
Figure 3: Visualization of DL-based adaption of constellations in the interface scenario of two auto encoder transmit-receiver pairs (Gif animation included in online version. Animation produced by Lloyd Pellatt, University of Sussex).

Spectrum Sharing with AI

The concept of cognitive radio was originally introduced in the visionary work of Joseph Mitola as the marriage between wireless communications and artificial intelligence, i.e., wireless devices that can change their operations in response to the environment and changing user requirements, following a cognitive cycle of observe/sense, learn and act/adapt.  Cognitive radio has found its most prominent application in the field of intelligent spectrum sharing. Therefore, it is befitting to highlight the critical role that AI can play in enabling a much more efficient sharing of radio spectrum in the era of 5G. 5G New Radio (NR) is expected to support diverse spectrum bands, including the conventional sub-6 GHz band, the new licensed millimetre wave (mm-wave)  bands which are being allocated for 5G, as well as unlicensed spectrum. Very recently 3rd Generation Partnership Project (3GPP) Release-16 has introduced a new spectrum sharing paradigm for 5G in unlicensed spectrum. Finally, both in the UK and Japan the new paradigm of local 5G networks are being introduced which can be expected to rely heavily on spectrum sharing. As an example of such new challenges, the scenario of 60 GHz unlicensed spectrum sharing is shown in Figure 4(a), which depicts a beam-collision interference scenario in this band. In this scenario, multiple 5G NR BSs belonging to different operators and different access technologies use mm-wave communications to provide Gbps connectivity to the users. Due to high density of BS and the number of beams used per BS, beam-collision can occur where unintended beam from a “hostile” BS can cause server interference to a user. Coordination of beam-scheduling between adjacent BSs to avoid such interference scenario is not possible when considering the use of the unlicensed band as different  BS operating in this band may belong to different operators or even use different access technologies, e.g., 5G NR versus, e.g., WiGig or Multifire. To solve this challenge, reinforcement learning algorithms can successfully be employed to achieve self-organized beam-management and beam-coordination without the need for any centralized coordination or explicit signalling [4].  As 4(b) demonstrates (for the scenario with 10 BSs and cell size of 200 m) reinforcement learning-based self-organized beam scheduling (algorithms 2 and 3 in the Figure 4(b)) can achieve system spectral efficiencies that are much higher than the baseline random selection (algorithm 1) and are very close to the theoretical limits obtained from an exhaustive search (algorithm 4), which besides not being scalable would require centralised coordination.

Figure 4: Spectrum sharing scenario in unlicensed mm-wave spectrum (left) and system spectral efficiency of 10 BS deployment (right). Results are shown for random scheduling (algorithm 1), two versions of ML-based schemes (algorithms 2 and 3) and theoretical limit obtained from exhaustive search in beam configuration space (algorithm 4).

Figure 4: Spectrum sharing scenario in unlicensed mm-wave spectrum (left) and system spectral efficiency of 10 BS deployment (right).  Results are shown for random scheduling (algorithm 1), two versions of ML-based schemes (algorithms 2 and 3) and theoretical limit obtained from exhaustive search in beam configuration space (algorithm 4).


In this article, we presented few case studies to demonstrate the use of AI as a powerful new approach to adaptive design and operations of 5G and beyond-5G mobile networks. With mobile industry heavily investing in AI technologies and new standard activities and initiatives, including ETSI Experiential Networked Intelligence ISG [5], the ITU Focus Group on Machine Learning for Future Networks Including 5G (FG-ML5G) and the IEEE Communication Society’s Machine Learning for Communications ETI are already actively working on harnessing the power of AI and ML for future telecommunication networks, it is clear that these technologies will play a key role in the evolutionary path of 5G toward much more efficient, adaptive, and automated mobile communication networks. However, with its phenomenally fast pace of development, deep penetration of Artificial Intelligence and machine-learning may eventually disrupt the entire mobile networks as we know it, hence ushering the era of 6G.


5G Network Slicing – Separating the Internet of Things from the Internet of Talk

1 Mar

Recognized now as a cognitive bias known as the frequency illusion, this phenomenon is thought to be evidence of the brain’s powerful pattern-matching engine in action, subconsciously promoting information you’ve previous deemed interesting or important. While there is far from anything powerful between my ears, I think my brain was actually on to something. As the need to support an increasingly diverse array of equally critical but diverse services and endpoints emerges from the 4G ashes, network slicing is looking to be a critical function of 5G design and evolution.

Euphoria subsiding, I started digging a little further into this topic and it was immediately apparent that the source of my little bout of déjà vu could stem from the fact that network slicing is in fact not one thing but a combination of mostly well-known technologies and techniques… all bundled up into a cool, marketing-friendly name with a delicately piped mound of frosting and a cherry on top. VLAN, SDN, NFV, SFC — that’s all the high-level corporate fluff pieces focused on. We’ve been there and done that.2


An example of a diagram seen in high-level network slicing fluff pieces

I was about to pack up my keyboard and go home when I remembered that my interest had originally been piqued by the prospect of researching RAN virtualization techniques, which must still be a critical part of an end-to-end (E2E) 5G network slicing proposition, right? More importantly, I would also have to find a new topic to write about. I dug deeper.

A piece of cake

Although no one is more surprised than me that it took this long for me to associate this topic with cake, it makes a point that the concept of network slicing is a simple one. Moreover, when I thought about the next step in network evolution that slicing represents, I was immediately drawn to the Battenberg. While those outside of England will be lost with this reference,3 those who have recently binge-watched The Crown on Netflix will remember the references to the Mountbattens, which this dessert honors.4 I call it the Battenberg Network Architecture Evolution principle, confident in the knowledge that I will be the only one who ever does.


The Battenberg Network Architecture Evolution Principle™

Network slicing represents a significant evolution in communications architectures, where totally diverse service offerings and service providers with completely disparate traffic engineering and capacity demands can share common end-to-end (E2E) infrastructure resources. This doesn’t mean simply isolating traffic flows in VLANs with unique QoS attributes; it means partitioning physical and not-so-physical RF and network functions while leveraging microservices to provision an exclusive E2E implementation for each unique application.

Like what?

Well, consider the Internet of Talk vs. the Internet of Things, as the subtitle of the post intimates. Evolving packet-based mobile voice infrastructures (i.e. VoLTE) and IoT endpoints with machine-to-person (M2P) or person-to-person (P2P) communications both demand almost identical radio access networks (RAN), evolved packet cores (EPC) and IP multimedia subsystem (IMS) infrastructures, but have traffic engineering and usage dynamics that would differ widely. VoLTE requires the type of capacity planning telephone engineers likely perform in their sleep, while an IoT communications application supporting automatic crash response services5 would demand only minimal call capacity with absolutely no Mother’s Day madness but a call completion guarantee that is second to none.

In the case of a network function close to my heart — the IMS Core — I would not want to employ the same instance to support both applications, but I would want to leverage a common IMS implementation. In this case, it’s network functions virtualization (NFV) to the rescue, with its high degree of automation and dynamic orchestration simplifying the deployment of these two distinct infrastructures while delivering the required capacity on demand. Make it a cloud-native IMS core platform built on a reusable microservices philosophy that favors operating-system-level virtualization using lightweight containers (LCXs) over virtualized hardware (VMs), and you can obtain a degree of flexibility and cost-effectiveness that overshadows plain old NFV.

I know I’m covering a well-trodden trail when I’m able to rattle off a marketing-esque blurb like that while on autopilot and in a semi-conscious state. While NFV is a critical component of E2E network slicing, things get interesting (for me, at least) when we start to look at the virtualization of radio resources required to abstract and isolate the otherwise common wireless environment between service providers and applications. To those indoctrinated in the art of Layer 1-3 VPNs, this would seem easy enough, but on top of the issue of resource allocation, there are some inherent complications that result from not only the underlying demand of mobility but the broadcast nature of radio communications and the statistically random fluctuations in quality across the individual wireless channels. While history has taught us that fixed bandwidth is not fungible,6 mobility adds a whole new level of unpredictability.

The Business of WNV

Like most things in this business, the division of ownership and utilization can range from strikingly simple to ridiculously convoluted. At one end of the scale, a mobile network operator (MNO) partitions its network resources — including the spectrum, RAN, backhaul, transmission and core network — to one or more service providers (SPs) who use this leased infrastructure to offer end-to-end services to their subscribers. While this is the straightforward MNV model and it can fundamentally help increase utilization of the MNOs infrastructure, the reality is even easier, in that the MNO and SP will likely be the same corporate entity. Employing NFV concepts, operators are virtualizing their network functions to reduce costs, alleviate stranded capacity and increase flexibility. Extending these concepts, isolating otherwise diverse traffic types with end-to-end wireless network virtualization, allows for better bin packing (yay – bin packing!) and even enables the implementation of distinct proof-of-concept sandboxes in which to test new applications in a live environment without affecting commercial service.


Breaking down the 1-2 and 4-layer wireless network virtualization business model

Continuing to ignore the (staggering, let us not forget) technical complexities of WNV for a moment, while the 1-2 layer business model appears to be straightforward enough, to those hell-bent on openness and micro business models, it appears only to be monolithic and monopolistic. Now, of course, all elements can be federated.7 This extends a network slice outside the local service area by way of roaming agreements with other network operators, capable of delivering the same isolated service guarantees while ideally exposing some degree of manageability.

To further appease those individuals, however, (and you know who you are) we can decompose the model to four distinct entities. An infrastructure provider (InP) owns the physical resources and possibly the spectrum which the mobile virtual network provider then leases on request. If the MVNP owns spectrum, then that component need not be included in the resource transaction. A widely recognized entity, the mobile virtual network operator (MVNO) operates and assigns the virtual resources to the SP. In newer XaaS models, the MVNO could include the MVNP, which provides a network-as-a-service (NaaS) by leveraging the InPs infrastructure-as-a-service (IaaS). While the complexities around orchestration between these independent entities and their highly decomposed network elements could leave the industry making an aaS of itself, it does inherently streamline the individual roles and potentially open up new commercial opportunities.

Dicing with RF

Reinforcing a long-felt belief that nothing is ever entirely new, long before prepending to cover all things E2E, the origin of the term “slicing” can be traced back over a decade in texts that describe radio resource sharing. Modern converged mobile infrastructures employ multiple Radio Access Technologies (RATs), both licensed spectrum and unlicensed access for offloading and roaming, so network slicing must incorporate techniques for partitioning not only 3GPP LTE but also IEEE Wi-Fi and WiMAX. This is problematic in that these RATs are not only incompatible but also provide disparate isolation levels — the minimum resource units that can be used to carve out the air interface while providing effective isolation between service providers. There are many ways to skin (or slice) each cat, resulting in numerous proposals for resource allocation and isolation mechanisms in each RF category, with no clear leaders.

At this point, I’m understanding why many are simply producing the aforementioned puff pieces on this topic — indeed, part of me now wishes I’d bowed out of this blog post at the references to sponge cake — but we can rein things in a little.  Most 802.11 Wi-Fi slicing proposals suggest extending existing QoS methods — specifically, enhanced DCF (distributed coordination function) channel access (EDCA) parameters. (Sweet! Nested acronyms. Network slicing might redeem itself, after all.) While (again) not exactly a new concept, the proposals advocate implementing a three-level (dimensional) mathematical probability model know as a Markov chain to optimize the network by dynamically tuning the EDCA contention window (CW), arbitration inter-frame space (AIFS) and transmit opportunity (TXOP) parameters,8 thereby creating a number of independent prioritization queues — one for each “slice.” Early studies have already shown that this method can control RF resource allocation and maintain isolation even as signal quality degrades or suffers interference. That’s important because, as we discussed previously, we must overcome the variations in signal-to-noise ratios (SNRs) in order to effectively slice radio frequencies.

In cellular networks, most slicing proposals are based on scheduling (physical) resource blocks (P/RBs), the smallest unit the LTE MAC layer can allocate, on the downlink to ensure partitioning of the available spectrum or time slots.


An LTE Physical Resource Block (PRB), comprising 12 subcarriers and 7 OFDM symbols

Slicing LTE spectrum, in this manner, starts and pretty much ends with the eNodeB. To anyone familiar with NFV (which would include all you avid followers of Metaswitch), that would first require virtualization of that element using the same fundamental techniques we’ve described in numerous posts and papers. At the heart of any eNodeB virtualization proposition is an LTE hypervisor. In the same way classic virtual machine managers partition common compute resources, such as CPU cycles, memory and I/O, an LTE hypervisor is responsible for scheduling the physical radio resources, namely the LTE resource blocks. Only then can the wireless spectrum be effectively sliced between independent veNodeB’s owned, managed or supported by the individual service provider or MVNO.


Virtualization of the eNodeB with PRB-aware hypervisor

Managing the underlying PRBs, an LTE hypervisor gathers information from the guest eNodeB functions, such as traffic loads, channel state and priority requirements, along with the contract demands of each SP or MVNO in order to effectively slice the spectrum. Those contracts could define fixed or dynamic (maximum) bandwidth guarantees along with QoS metrics like best effort (BE), either with or without minimum guarantees. With the dynamic nature of radio infrastructures, the role of the LTE hypervisor is different from a classic virtual machine manager, which only need handle physical resources that are not continuously changing. The LTE hypervisor must constantly perform efficient resource allocation in real time through the application of an algorithm that services those pre-defined contracts as RF SNR, attenuation and usage patterns fluctuate. Early research suggests that an adaptation of the Karnaugh-map (K-map) algorithm, introduced in 1953, is best suited for this purpose.9

Managing the distribution of these contracted policies across a global mobile infrastructure falls on the shoulders of a new wireless network controller. Employing reasonably well-understood SDN techniques, this centralized element represents the brains of our virtualized mobile network, providing a common control point for pushing and managing policies across highly distributed 5G slices. The sort of brains that are not prone to the kind of cognitive tomfoolery that plague ours. Have you ever heard of the Baader-Meinhof phenomenon?

1. No one actually knows why the phenomenon was named after a West German left wing militant group, more commonly known as the Red Army Faction.


3. Quite frankly, as a 25-year expat and not having seen one in that time, I’m not sure how I was able to recall the Battenberg for this analogy.

4. Technically, it’s reported to honor of the marriage of Princess Victoria, a granddaughter of Queen Victoria, to Prince Louis of Battenberg in 1884. And yes, there are now two footnotes about this cake reference.

5. Mandated by local government legislation, such as the European eCall mandate, as I’ve detailed in previous posts.

6. E.g. Enron, et al, and the (pre-crash) bandwidth brokering propositions of the late 1990s / early 2000s

7. Yes — Federation is the new fancy word for a spit and a handshake.

8. OK – I’m officially fully back on the network slicing bandwagon.

9. A Dynamic Embedding Algorithm for Wireless Network Virtualization. May 2015. Jonathan van de Betl, et al.


Comparative Study WIFI vs. WIMAX

5 Sep

Wireless networking has become an important area of research in academic and industry. The main objectives of this paper is to gain in-depth knowledge about the Wi-Fi- WiMAX technology and how it works and understand the problems about the WiFiWiMAX technology in maintaining and deployment. The challenges in wireless networks include issues like security, seamless handover, location and emergency services, cooperation, and QoS. The performance of the WiMAX is better than the Wi-Fi and also it provide the good response in the access. It’s evaluated the Quality of Service (Qos) in Wi-Fi compare with WiMAX and provides the various kinds of security Mechanisms. Authentication to verify. The identity of the authorized communicating client stations. Confidentiality (Privacy) to secure that the wirelessly conveyed information will remain private and protected. Take necessary actions and configurations that are needed in order to deploy Wi-Fi -WiMAX with increased levels of security and privacy.

Download: ART20161474


The Basics Of QoS

16 Aug

Learn how Quality of Service works and common use cases.

Providing sufficient Quality of Service (QoS) across IP networks is becoming an increasingly important aspect of today’s enterprise IT infrastructure. Not only is QoS necessary for voice and video streaming over the network, it’s also an important factor in supporting the growing Internet of Things (IoT). In this article, I’ll explain why QoS is important, how it works, and describe some use-case scenarios to show how it can benefit your end users’ experience.

Why is QoS important?

Some applications running on your network are sensitive to delay. These applications commonly use the UDP protocol as opposed to the TCP protocol. The key difference between TCP and UDP as it relates to time sensitivity is that TCP will retransmit packets that are lost in transit while UDP does not. For a file transfer from one PC to the next, TCP should be used because if any packets are lost, malformed or arrive out of order, the TCP protocol can retransmit and reorder the packets to recreate the file on the destination PC.

But for UDP applications such as an IP phone call, any lost packet cannot be retransmitted because the voice packets come in as an ordered stream; re-transmitting packets is useless. Because of this, any lost or delayed packets for applications running the UDP protocol are a real problem. In our voice call example, losing even a few packets will result in the voice quality becoming choppy and unintelligible. Additionally, the packets are sensitive to what’s known as jitter. Jitter is the variation in delay of a streaming application.

If your network has plenty of bandwidth and no traffic that bursts above what it can handle, you won’t have a problem with packet loss, delay or jitter. But in many enterprise networks, there will be times where links become overly congested to the point where routers and switches start dropping packets because they are coming in/out faster that what can be processed. If that’s the case, your streaming applications are going to suffer. This is where QoS comes in.

How does QoS work?

QoS helps manage packet loss, delay and jitter on your network infrastructure. Since we’re working with a finite amount of bandwidth, our first order of business is to identify what applications would benefit from managing these three things. Once network and application administrators identify the applications that need to have priority over bandwidth on a network, the next step is to identify that traffic. There are several ways to identify or mark the traffic. Class of Service (CoS) and Differentiated Services Code Point (DSCP) are two examples. CoS will mark a data stream in the layer 2 frame header while DSCP will mark a data stream in the layer 3 packet header. Various applications can be marked differently, which allows the network equipment to be able to categorize data into different groups.

QoS basics.jpg


(Image: kgtoh/iStockphoto with modification)

Now that we can categorize data steams into different groups, we can use that information to place policy on those groups in order to provide preferential treatment of some data streams over others. This is known as queuing. For example, if voice traffic is tagged and policy is created to give it access to the majority of network bandwidth on a link, the routing or switching device will move these packets/frames to the front of the queue and transmit them immediately. But if a standard TCP data transfer stream is marked with a lower priority, it will wait (be queued) until there is sufficient bandwidth to transmit. If the queues fill up too much, these lower-priority packets/frames are the first to get dropped.

QoS use-case scenarios

As stated previously, the most common use cases for QoS are voice and video streams. But there are plenty more examples, especially now that IoT is beginning to take off. An example is in the manufacturing sector, where machines are beginning to leverage the network to provide real-time status information on any issues that may be occurring. Any delay in the identification of a problem can result in manufacturing mistakes costing tens of thousands of dollars each second. With QoS, the manufacturing status data stream can take priority in the network to ensure information flows in a timely manner.

Another use case might be in the steaming of various smart sensors for large-scale IoT projects such as a smart building or smart city. Much of the data collected and analyzed, such as temperature, humidity, and location awareness, is highly time sensitive. Because of this time sensitivity, this data should be properly identified, marked and queued accordingly.

It’s safe to say that as our connectivity needs continue to expand into all aspects of our personal and business lives, QoS is going to play an increasingly important role in making sure that certain data streams are given priority over others in order to operate efficiently.


Indoor Building Distributed Antenna System (DAS)

1 Aug

Introduction & Objectives:

Indoor sites are built to cater capacity and coverage issues in indoor compounds where outdoor macro site can’t be a good solution.

In dense urban clutter where buildings structures and indoor environment losses are quite large for macro site which makes it‘s an inappropriate solution. Generally floors underground (basements and lower ground) have poor RSSI. Major part of reflections takes place from ground and because of this portion below ground have poor signal coverage.

On the other hand floors above third have quality and DCR issues. Due to fewer obstacles in the LOS path, path losses are less compared to ground floors. So there is a multiservers environment due to less path losses and cells overshooting which leads to ping pong handovers and interference issues inside the compound.

In urban areas there are buildings that generate high traffic loads like commercial buildings, offices; shopping malls may need indoor systems to take care of the traffic demands. For such areas indoor is the efficient solution regarding cost, coverage and capacity.

In indoors downlink is
the critical link in the air interface. There is no need to use the uplink diversity in an indoor system or use amplifiers like TMA for improving the uplink signal .Multi-antenna indoor system is providing diversity as uplink signals received by several antennas.

In-building solutions DAS-IBS technology is one of the fastest changes in mobile network rollouts. It has been estimated that 70-90% of all mobile calls are made inside the buildings; therefore to improve the QOS, operators today have started concentrating more on this aspect of network rollouts.

The most efficient way to achieve optimal quality, coverage & capacity result inside the building is to use Microcell with Distributed Antennae System (DAS)

Hayat Telecom LCC has set up support to Venders in rolling out IBS network & gathered both planning tools and professionals for attaining quality rollouts with utmost levels of customer satisfaction.

Indoor Building Systems Solution, Specifically the Solutions of Radio Network Design is needed to enhance QOS and Capacity of the network. Most of calls are generated from inside of buildings so it ‘does require special attention for enhancing the network performance’.

The key essentials for a potential IBS system for planning are:-

  • Identification of potential buildings for IBS.Design Distributed Antenna system using passive & active elements and, Prepare complete Link engineering diagram with each antenna’s EIRP proposal report.
  • Implementation of IBS solution with best professional way without disturbing aesthetic of building.
  • LOS & Link Planning to connect site.
  • RF parameter planning, RF walk test and call quality testing.

As moving ahead details of key part explain in detail.

Types of indoor cells:

There are mainly three types of indoor cell.

1-Micro Cells

2-Pico cells

3-Femto Cells


Micro cells constitute most of the indoors deployed for BTS coverage. They are more costly and also on large scale with respect to Femto or Pico cells. They consist of indoor micro /metro BTS and distributed antenna system for signal propagation in indoor environment .Usually they have passive components but where large distance to be required amplifiers especially optical amplifiers are deployed called active components.

A Pico cell is wireless communication system typically covering a small area, such as in-building (offices, shopping malls, train stations, etc.), or more recently in-aircraft. A Pico cell is analogous to a WIFI access point. In cellular wireless networks, such as GSM, the Pico cell base station is typically a low cost, small (typically the size of a sheet of A4 paper and about 2-3cm thick), reasonably simple unit that connects to a Base Station Controller (BSC). Multiple Pico cell ‘heads’ connect to each BSC: the BSC performs radio resource management and hand-over functions, and aggregates data to be passed to the Mobile Switching Centre (MSC) and/or the GPRS Support Node (GSN).

In telecommunications, a Femto cell—originally known as an Access Point Base Station—is a small cellular base station, typically designed for use in residential or small business environments. It connects to the service provider’s network via broadband (such as DSL or cable); current designs typically support 5 to 100 mobile phones in a residential setting. A Femto cell allows service providers to extend service coverage indoors, especially where access would otherwise be limited or unavailable. The Femto cell incorporates the functionality of a typical base station but extends it to allow a simpler, self contained deployment; an example is a UMTS Femto cell containing a Node B, RNC and GPRS Support Node (SGSN) with Ethernet for backhaul. Although much attention is focused on UMTS, the concept is applicable to all standards, including GSM, CDMA2000, TD-SCDMA and WiMax solutions.

Objective of IBS design:

The basic aim of indoor building solution is increasing the quality of indoor signal at different public and business locations. The Public locations are such as said before Shopping Malls, Airport terminals, Hospitals, Residential flats and business exhibition centers, Govt and private offices etc

The fig shown the obligation of IBS .With BTS site deep indoor signal penetration is not good in dense urban areas specially in high rise Building Areas.IBS cover this obligation .

IBS Design solution Scenarios:

There are various solutions that can be implemented for a particular site. For a design approach, we will select the most cost-effective solution to meet the performance criteria.

Distributed antenna network:

The useful application of antennas in indoor systems is the idea of distributed antennas.  The philosophy behind this approach is to split the transmitted power among several antenna elements, separated in space so as to provide coverage over the same area as a single antenna, but with reduced total power and improved reliability.  The smaller coverage footprint of each antenna element provides for controlled coverage and reduces excessive interference and spillage effects.

A distributed antenna system can be implemented in several ways, a number of which are listed below.

DAS-1-Passive coaxial network design:

The network is made up of passive components such as coaxial cable, combiners, splitters, directional couplers, etc. Antennas that are utilized can be of wide-bandwidth to support multi-band and/or multi-system requirements. The advantage of this approach is that the network is simple and requires minimal maintenance.

DAS -2-Leaky feeder system:

The ultimate form of a passive distributed antenna system is a radiating cable (leaky feeder) that is a special type of coaxial cable where the screen is slotted to allow radiation along the cable length. With careful design, such cables can produce virtually uniform coverage. This type of system is best suited for applications requiring in-tunnel coverage (such as in subways). The radiating cable in this case is run along the entire length of the tunnel. The cable is either a radiating coaxial cable or radiating wire.

 DAS-3-Fiber Optic Solution:

In this method, RF signals are converted to optical signals before being transmitted to distribution units via optical fibers. Single-mode and multi-mode fibers can be used but multi-mode fiber requires frequency conversion before RF- to-optic conversion. The fiber optic solution is ideal for wide-area deployments such as in buildings with extensive floor areas and high-rise office buildings. The installation cost can be well contained if the existing optical fiber infrastructure within a building can be re-used. This solution is also useful for expanding on an existing distributed antenna system that is operating on coaxial solutions.

DAS-4-Repeater Solution:

This solution is implemented to expand the coverage of an indoor or outdoor cell. If coverage is to be expanded to an isolated place, a repeater solution can be used. This input signal to the repeater can be sourced either from an existing off-the-air RF signal or fiber-fed from a remote location. In large buildings, where coaxial cable network is required to use, EBTS power will not be enough to power all the antennas. In this instance, in-line repeaters are used to boost up RF signal.

DAS-IBS Deployment Design:

Passive IBS

Mostly passive IBS is deployed as an indoor solution. Passive IBS contains splitters, couplers, attenuators, combiners, coaxial cable, DAS but there is no active element involved.

Active IBS

Active IBS is generally used when the EIRP required is more than the available. Usually this happen when distance involve are large and antenna elements are more as well. Active IBS is actually a hybrid IBS as it contains an active component (repeater) and passive IBS.

DAS-IBS-Design Entities:


Mostly antennas used in IBS design are Omni directional and flat panel directional antennas.The selection of antenna types is based on the availability, feasibility. Retain ability, compatibility and performance with selected solution .The usage of different type of antennas varies for different physical atmosphere. The antennas are connected with coax feeders inside the building. The antenna selection depends upon the general Product Description and specification shared by venders.

The Primary Antenna types in IBS design are:

1-Omni directional antenna

2-Directional antenna

3-Leaky cable

1-Omni Directional Antennas

It transmits signal in all direction .it contain Low gain. Horizontal direction pattern all over the place but vertical direction concentrated. General specifications of Omni Antenna as below:

Gain   2-3 dbi

Beam width 360

Polarized Vertical

VSWR  less than 1

2-Directional Antennas:

It transmits signal in a specified direction. It Contain high gain.

3-Leaky Coaxial Cable:

It transmits signal along path of the coaxial cable .Contains closely spaced slots in the outer conductor of the cable to transmit/Receive signals. There atre Two types of losses in leaky cable.

I-Feeder loss- cable attenuation loss

II-Coupling loss-Average signal level difference between the cable and dipole antenna at distance of 6m approx.

Some of the general feature reviews of antennas are given below:


-WiFi System, ISM application


-Indoor/in-building Coverage


-WLAN Communication Application


-CDMA, GSM, DCS, 3G/4GUMTS Application


-Next Gen Mobile-LTE



-Low return loss

-Wide beanwidth


-Suitable for wall mounting


-Low, aestheticall pleasing profile


Model: XXXXXXXX (Any )


RF Parameters:


-Frequency: In MHz (its selection depend upon spectrum allocation)

-Polarization: Vertical, Linear


-Horizontal Beam Width: 360 deg


-Vertical Beam Width: 90 deg (698-960MHz band (its selection depend upon spectrum allocation))


50 deg (1710-2700MHz band (its selection depend upon spectrum allocation))


-Gain: in dBi


-VSWR ≤ 1.5


-F/B >in dB


-Max Power: in  “W”


-Impedance: in Ω


Mechanical Specification:


-Radome Material ABS with UV Protection


-Lightning Protection Direct Ground


-Connector N-female


-Weight in  kg


-Size in mm


-Operating Temperature Range in degrees


-Storage Temperature in degrees


Different technologies antennas are available in market. Customer selects it as per need, services and requirement. i.e dual band antennas supports two band signal, quad band antennas suppots threes different band signals etc .

In addition of antennas detail as mentioned above in Passive Coaxial Cable design Distributed antennas connected with couplers, Power splitters, Jumpers and feeder cable Link Budget calculations based on how many couplers and Splitters are we used & Losses of coupler, splitters and feeder cable length in design. In the marker 20db, 15db, 10db and 6db couplers  2way, 3way and 4 way splitters  ½” inch Jumpers, ½”,7/8”,11/4” inch  feeders cables are using. Below Figures indicates how we cater losses of these coupler, splitter and cable.

Power Splitters

Splitters are used to split antenna feeder network power equally over the output ports.Two way, three way and four way splitters are generally used.

Splitters Loss:

2-Way Splitter Loss – around 3 db

3-Way Splitter Loss- around 5db

4-Way Splitter Loss- around 6db

Insertion loss for these splitters is 0 .2db.

Power Couplers:

Couplers are used to split antenna feeder power unequally among output ports.Couplers have tap/coupling loss and through loss e.g 10/0.5 coupler means its coupling loss is 10 while through loss is 5.Couplers generally are available in ratings of 3, 6, 7, 10, 15 & 20 db.


Attenuators are used to reduce EIRP at antennas where less EIRP   required but the other antennas required high EIRP.

Attenuators are of values 3, 5, 7, 10 etc.



The Base station capacity specification varies in Vander to Vander. The General specification of base station   is same as off Outdoor Base station or normal Base station.

Building Specifications and Coverage and Capacity Demands (Expansions): The capacity requirement enhances and fulfilled by adding extra Transceivers card into the cabinet of IBS_BTS. You can add as many card as IBS-base station supports.

For DAS-IBS coverage design regardless any type of DAS accurate building sketch and dimensions of building are very important .Designer should must required sketch map of building because defining he marked the route of cable and plan the coupler and splitter at right place without effecting KPI of deployment and coverage. For sacking this many tools in the markets are available .Mostly recommended by Vander.

Initial RF Survey:

Following are the things which are taken under consideration during initial RF Survey:

  • Site(Indoor Building) coordinates
  • Site Rough Layout sketch
  • RSSI and C/I of strong servers in different location of indoor site using TEMS pocket view mode.
  • No. of subscribers’ estimation/ floor or as the building architectural division.
  • Marking of the different areas what they are specified for.
  • Snaps of different floors
  • Building structure observation.

Initial RF survey report:

After the survey report is made in which all the above inputs are put.


Indoor Site Evaluation:

After the survey it is checkout if any modifications (Hard / Soft Changes) can be done to the existing neighboring site to improve the condition at the affected area. Otherwise Site is evaluated as to be an indoor Micro or wall mounted metro according to the location, requirements and conditions.

DAS-IBS Designing Tools:

iBwave Design radio planning software automates the design in-building wireless networks for optimal voice coverage and data capacity. It eliminates guesswork, to bring strong, reliable wireless communications indoors. iBwave Design is an integrated solution that takes RF designers through network planning, design, costing, validation, documentation and reporting. iBwave Design makes it easy for RF engineers to test scenarios for optimizing network coverage for 2G, 3G and 4G cellular technologies, as well as WiFi, public safety bands and femtocell.

  • RF System Design and Calculations.
  • Components Database to manage DAS equipment
  • Display DAS equipment position on floor plans
  • Create professional project documentation
  • Create automated reports on IBS project performance and cost
  • Standardize IBS design format
  • Propagation Module- Simulate indoor and outdoor propagation prediction in your building
  • Optimization module – Extrapolate outdoor wireless signals inside the building to analyze signal quality and data throughput before design phase
  • Collection module- import survey data and trace routes from collection devices, and overlaying survey data onto wireless indoor network design.
  • RF professionals to manage complex in-building network projects, generating cost efficiency, increasing productivity and delivering a larger return on investment.
  • Below address may help us to review and finalize designing tools. We can ask the IBS design module quotations to all RF Tools Venders after mailing info@ to all link presents..

Planning Tools for Wide Area Wireless Systems

Radio Planning Tools
Mentum Planet ™
Mentum CellPlanner ™
Forsk Atoll
Broadband Planner
V-Soft Probe













RF Survey with floor Plan:

Once the indoor site is finalized, floor Architectural Plans are requested from building Authorities.

RF survey with Floor Plans is carried, RSSI is checked & recorded at each and every part of the indoor environment and C/I is checked at worst.

Drive test tool idle mode log files for different floors are made using floor plans provided.

During the RF survey Detailed Analysis/Observations of the building/environment is carried out as well as what is the ceiling thickness, floor heights, thickness of the walls in between floors, thin walls and their thickness.

Antenna locations are finalized using traditional Ray tracing techniques(By simply analyzing how reflections and propagation going to occur)

Fig  RSSI of different servers with floor plans

Marking of Priority Area:

In indoor areas like offices and meeting rooms etc have usually high priority. On the other hand areas like mosques, gyms etc have low priorities. Similarly area in which outdoor macro coverage and quality is satisfactory should not be included in intended coverage area for indoor site. For high priority area coverage should be around -75 dbm at each point while for low priority area levels should be around -85 dbm. These values vary according to KPI’s doc of the network.

Fig : Priority area marking for an indoor site location

Indoor Antenna Placement:

Antenna placement is the most crucial step in indoor planning. Following observations should   be considered during antenna placement:

  • Antennas especially Omni-directional antennas should be placed at centralized locations.
  • Panels should be placed in the corners of corridors or where design demands while keeping in view the spillage of indoor signals.
  • Antennas should be placed at high elevations where people can’t touch them as it will affect the performance.
  • Obstacle free path should be provided for antennas otherwise coverage in indoor will suffer a lot.
  • Antennas should be placed away from conductive objects.
  • Exposure levels of the indoor RF signals are below RF safety standard of WHO, IRPA, IEEE and FCC. However discretely placed antenna will reduce the unnecessary public concerns about RF exposure.
  • If the building with low traffic capacity is to be planned antennas should be placed in zigzag manner such to get an even distribution of signals as depicted in fig. below

Fig :  Improvement in indoor coverage

Link Budget:

Link Budget calculations are used to calculate the output power (db) at each antenna element. Passive component (coupler, splitter and attenuator losses) and feeder cable losses are subtracted from BTS output power. Link budget calculations are made for band to be used for indoor GSM/DCS/UMTS.

EIRP= Pout BTS + Ga – Lf – Lc- Ls – La

Pout BTS= BTS output power at antenna connector

Ga= Antenna gain (db)

Lf= Feeder loss

Lc= Coupler loss

Ls= Splitter loss

La= Attenuator loss

With standard parameters we can calculate link budget of the access site shared by Vander side

RF Indoor Plan:

After the path loss and link budget calculations RF plan is made floor by floor on the autocad layout of the building. Care should be taken while adjusting the AutoCAD scale. Also antenna, cable lengths and passive elements should be drawn accurately according to the plan.

Fig : RF indoor Plan for a floor

Antenna tree diagram:

Antenna tree diagram is made to have a quick overview of the IBS design. Care should be taken while calculating the lengths.


Fig 5.18: Antenna Tree diagram

Fig : Measurements for Cable lengths

Indoor Equipment List:

Detailed and complete BoQ list essential at site.

Fig: Indoor Equipment List

Indoor Site frequency planning:

Frequency planning is performed manually selecting suitable frequencies by carefully analyzing the neighboring frequencies.Exclude the co-channel and adjacent frequencies which will likely to interfere.From the remaining set choose the frequency that most likely to cause interference. BCCH frequency should be the least disturbed. Hopping on several frequencies will smooth out the interference.

Following need to be considered if two much clean frequency options exist:

  • Increase signal strength of indoor cell.
  • Allocate dedicated 3-5 frequencies for indoor cells.
  • Redesign the frequency plan.
  • (Indoor sites in our network are single cell; single band sites, so no frequency reuse is done in indoor)

IBS System Deployment Recommendations:

Traditional IBS deployment as said before Passive and active DAS –IBS.
Operators deploy solutions as per regulatory requirements (e.g. GSM or UMTS license) Recently operators deployed their own systems, single users DAS in a buildings. This resulted in multiple DAS in the same building, one for each operator (2-4) cause of

  • Multiple cable runs
  • Multiple Antennas
  • Multiple Maintenance organizations

So now a day’s regulatory authorities, building developers/owners and operators are

More operators are in force of sharing the IBS DAS. As illustrated before, all operators can share one DAS which cause of less cables and antennas and Shared maintenance efforts which helps controlling apex of IBS-DAS. This equals less negative impact on the esthetics of the building, less maintenance activities and lower cost for DAS.

The Third party installs the DAS most of the times. Generic Multi Operator DAS implemented by developer/owner in a building. DAS connected with Coaxial cables  with star configuration, Antennas (location based on generic guidelines, cables routed back to the nearest technical room e.g. maximum 90 meter cable run.

Wireless Design Simplicity

Goal – Provide a “-75dBm Coverage Blanket”for meeting coverage ,QOS KPI’s.

The Antenna Location Design Rules:

  • Outside antennas within 20ft of the edge of the building
  • Antennas spaced at 100 ft apart
  • One antenna per floor within 20 ft of the elevator core
  • One back-to-back antenna every 6 floors in the elevator shaft starting on floor 3
  • Cable: Star configuration

Following rules of thumb Maximum flexibility for the future RF planning

  • Omni antennas on a basic 100ft (30m) grid
  • Perimeter antennas < 20ft (6m) from walls
  • If on external wall, utilize directional antenna
  • One antenna < 20ft (6m) from elevator core




  • If open, Omni antenna every 6th floor,
  • If closed, Omni antenna every 2nd floor

Installation & Certification:

  • Each cable run directly to TR < 300ft (90m)
  • Install connectors on both ends
  • Sweep-test for integrity and loss
  • Attach antennas & document cable paths
  • Extended warranty

Site Acceptance:

Once the indoor site is implemented site acceptance request is made by vendors/sub cons. Implementation team will take care of VSWR calculations, antenna grounding etc. Following is required from RF Team for acceptance of the indoor site:

  1. On site Audit
  2. Walk test
  3. Spillage check

1-On Site Audit:

On site verification of the indoor is performed to check the antenna location as well as the equipment count.


Walk test summarizing the coverage actual manners. It will be tested at two  types of  drive test mode

I-Idle Mode:

Walk test in idle mode for the indoor site is performed to check the RSSI and C/I of indoor site. Logfiles are made on the floor plans provided. (In case of vendor planning walk test  report is to be provided by them).

Fig : Rx-Level Idle mode

II-Dedicated mode:

Dedicated mode walk test is performed to check the quality and RSSI of indoor after call setup. Qualities of different TRX are also checked at RF end by locking the call on different TRX’s. Also handovers with other neighboring sites is tested.

Fig : Rx-Qual Dedicated mode

III-Spillage Check:

Spillage is spill of indoor signal outside the indoor location. Spillage is generally checked 20m away from the periphery of indoor compound. Generally -85dbm is set as a threshold and levels below it are problematic   as they will cause unnecessary handovers on the indoor site. However using Cell Reselection Offset parameters and handover control parameters, the unnecessary reselections and handovers can be avoided.

Fig Spillage

4-Coverage Acceptance:

Coverage is checked at each part of the indoor compound and should be within the range.


To be checked by implementation.

6-Parameters fine tuning:

Before site is accepted by the planning team Fine tuning of parameters is performed to achieve the below mentioned KPI’s. After achieving the KPI targets planning will accept this and handed over to optimization team for further fine tuning

1 RX Level for 2G for 95% of the Covered Area=-75dBm
2 RSCP for 95% of the Covered Area=-80dBm
3 DL Rx Quality for 2G for 95% area of the covered Area less than 2


Pilot power  of 3G common area  less than -75 dBm

Pilot Ec/Io of common area  less than -7 dBm

Spillage Test (On the surrounding main street nearby the building)


Signal from indoor system not higher than -95dBm


Frequency Planning for Indoor Systems Conclusion:

For improve coverage and Capacity inside building using IBS solution and it shows an increase of the cellular traffic with up to 70% for larger buildings. For good coverage we have to assign frequencies manually by excluding the frequencies of the Surrounding cells and the adjacent frequencies. For avoiding interference it is good to apply Frequency Hooping to smooth out the interference. It is good for coverage if we are increasing the BTS power if the available frequencies are few in numbers.


IBS Planning & Implementation:

To starting planning process of IBS DAS Statically review of the network is very important and essential .The identification of the right area or building for IBS DAS design very critical .Once the Area identified with help of stats of the network, field visits and complains.

Once location identified standardized planning ladder followed till all entity of DAS IBS design practical implemented.


In-building Solutions as defined in this document is a way to enable efficient usage of wireless mobile applications inside different kinds of buildings. This requires that sufficient coverage and capacity with good radio quality is available inside the buildings. Although the mobile operators will cover most buildings from outdoor sites in their macro network, there is a need to provide many buildings with extended radio coverage and capacity. In-building solutions are well-proven methods for an operator to capture new traffic and new revenue streams.

One can provide enhanced in-building solutions to off-load the macro network, thus increasing mobile traffic, and attract additional subscribers due to the enhanced mobile network quality and accessibility to mobile Internet applications and other services that require high data-rates and capacity. There are several different ways to implement in-building solutions. Dedicated Radio Base Stations, RBSs, that are connected to Distributed Antenna Systems, DASs, are commonly implemented solutions. These solutions provide additional capacity as well as covers “black holes” inside different kinds of buildings. A number of different types of both RBSs and DASs are available and the solutions can be customized for different buildings and needs. Repeaters are often used for buildings with a limited need for capacity, but where additional coverage is needed, like road tunnels and smaller buildings or parts of buildings.

Indoor systems can be solution if the coverage is weak from outdoor cells or causing to bad quality To build indoor systems into the buildings, which are generating high traffic, can reduce the network load by handling that traffic In developed business centers, indoor system can replace the fixed network.

Indoor systems are sometimes the complements that can provide a good image.


A Pre-Scheduling Mechanism in LTE Handover for Streaming Video

21 Mar

This paper focuses on downlink packet scheduling for streaming video in Long Term Evolution (LTE). As a hard handover is adopted in LTE and has the period of breaking connection, it may cause a low user-perceived video quality. Therefore, we propose a handover prediction mechanism and a pre-scheduling mechanism to dynamically adjust the data rates of transmissions for providing a high quality of service (QoS) for streaming video before new connection establishment. Advantages of our method in comparison to the exponential/proportional fair (EXP/PF) scheme are shown through simulation experiments.

1. Introduction

For improving a low transmission rate of the 3G technologies, LTE (Long Term Evolution) was designed as a next-generation wireless system by the 3rd Generation Partnership Project (3GPP) to enhance the transmission efficiency in mobile networks [1,2]. LTE is a packet-based network, and information coming from many users is multiplexed in time and frequency domains. Many different downlink packet schedulers are proposed and utilized to optimize the network throughput [3,4]. There are three typical strategies: (1) round robin (RR), (2) maximum rate (MR) and (3) proportional fair (PF). The RR scheme is a fair scheduler, in which every user has the same priority for transmissions, but the RR scheme may lead to low throughput. MR aims to maximize the system throughput by selecting the user with the best channel condition (the largest bandwidth) such as by comparing the signal to noise ratio (SNR) values. Moreover, the PF mechanism utilizes link adaptation (LA) technology. It compares the current channel rate with the average throughput for each user and selects the one with the largest value. However, these methods only consider non-real-time data transmissions. Therefore, some packet schedulers are proposed based on PF algorithm for real-time data transmissions [5,6]. In one study [5], a Maximum-Largest Weighted Delay First (M-LWDF) algorithm is proposed. In addition to data rate, M-LWDF takes weights of the head-of-line (HOL) packet delay (between current time and the arrival time of a packet) into consideration. It also combines HOL packet delay with the PF algorithm to achieve a good throughput and fairness. In another study [6], an exponential/proportional fair (EXP/PF) is proposed. EXP/PF is designed for both real-time and non-real time traffic. Compared to M-LWDF, the average HOL packet delay is also taken into account. Because of the consideration of packet delay time, M-LWDF and EXP/PF can achieve higher performance than the other mechanisms in real-time transmissions [7]. Other schedulers for real-time data transmissions are as follows. In one study [8], two semi-persistent scheduling (SPS) algorithms are proposed to achieve a high reception ratio in real-time transmission. It also utilizes wide-band time-average signal-to-interference-plus-noise ratios (SINR) information for physical resource blocks (PRBs) allocation to improve the performance of large packet transmissions. In another study [9], the mechanism provides fairness-aware downlink scheduling for different types of packets. Three queues are utilized for data transmission arrangement according to the different priority needs. If a user is located near cell′s edge, his services may not be accepted. This may still cause starvation and fairness problems. In yet another study [10], a two-level downlink scheduling is proposed. The mechanism utilizes a discrete control theory and a proportional fair scheduler in upper-level and lower level, respectively. Results show that the strategy is suitable for real-time video flows. However, most schedulers do not improve low transmission rates during the LTE handover procedure and meet the needs of video quality for users.
The scalable video coding (SVC) is a key technology for spreading streaming video over the internet. SVC can dynamically adapt the video quality to the network state. It divides a video frame into one base layer (BL) and number of enhancement layers (ELs). The BL includes the most important information of the original frame and must be used by a user for playing a video frame. Although ELs can be added to the base layer to further enhance the quality of coded video, it may not be essential. Therefore, in this paper, we propose a pre-scheduling mechanism to determine the transmission rates of BL and EL, especially focusing on the BL transmissions, before a new connection handover for providing high quality of service (QoS) for streaming video.

2. Pre-Scheduling Mechanism

Our proposed mechanism is divided into two phases: (1) handover prediction and (2) pre-scheduling mechanism.

2.1. Handover Prediction

Handover determination generally depends on the degradation of the Reference Signal Receiving Power (RSRP) from the base station (eNodeB). When the threshold value is reached, a handover procedure is triggered. Many works have focused on handover decisions [11,12,13,14,15,16]. In this paper, user measures RSRP periodically with neighbor eNodeBs. In addition, we use exponential smoothing (ES) to remove high-frequency random noise (Figure 1), where α is a smoothing constant. Then, we incorporate a linear regression model with RSRP values to predict time-to-trigger (TTT) for handover.

Figure 1. Exponential smoothing (α = 0.2).
The linear regression equation can be simply expressed as follows:

Pˆi=a+bti, i=1, 2, , n

where Pˆi is the predictive value of RSRP at time ti, and a and b are coefficients of the linear regression equation. Then, we use the least squares (LS) method to deduce a and b. The method of LS is a standard solution to estimate the coefficient in linear regression analysis.

Let the sum of the residual squares be S, that is


where Pi is the measured value of RSRP at time ti. The least squares method is to try to find the minimum of S, and then the minimum of S is determined by calculating the partial derivatives.

Finally we can get

⎧⎩⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪a =P¯¯¯bt¯b = ni=1tiPinTP¯¯¯¯¯ni=1ti2nT¯¯¯2

where T¯¯¯= ni=1tin and P¯¯¯= ni=1Pin. If there are several neighbor eNodes, we select the eNodeB with the maximum variation of RSRP (maximum slope) as target eNodeB. In Figure 2a, we can see that while RSRPSeNB=RSRPTeNB, the handover procedure is triggered. We have trigger time tt=a1a2b2b1.

Figure 2. Prediction for (a) time-to-trigger (TTT) of handover and (b) amount of data transmitted before handover.

2.2. Pre-Scheduling Mechanism

The BL is necessary for the video stream to be decoded. ELs are utilized to improve stream quality. Therefore, for high QoS for video streaming, we calculate the total number of BL that is required in a handover period for maintaining high QoS for video streaming.


where tr is the time interval from scheduling to starting handover (pre-scheduling time for handover). The starting time of scheduling is adjustable, and we will evaluate it in our simulation later. tho is the time during handover procedure. tn is the delay time before new transmission (preparation time of scheduling with new eNodeB). Ks is the required number of video frames per second and m is the number of BL that is needed in each video frame. In Figure 2b, according to transmission data rate of the serving eNodeB, we construct a linear regression line dx(t). Then, the amount of BL’s data (transmitted from serving eNodeB and stored in the buffer of users) before handover has to be no less than NBL.


where thandover is the TTT for handover. In the above inequality, the left part is the amount of data that the serving eNodeB can transmit before handover. According to the serving eNodeB capacity of transmission, we can dynamically adjust the transmission rate between BL and ELs. In Equation (6), while the inequality does not hold, it means the serving eNodeB cannot provide enough data for BL for maintaining high QoS for video streaming. Accordingly, the serving eNodeB merely transmits data for BL. On the contrary, while the inequality holds, the serving eNodeB can provide the data of BL and ELs simultaneously for desired quality of video service. In the following, we describe our mechanism of data rate adjustment between BL and ELs. The transmission rates of the BL and ELs are decreasing because the RSRP is degrading between the previous serving eNodeB and user. Hence, by the regression line dx(t), we can define the total descent rate s(slope) of transmissions as

In Figure 3, because of the decreasing RSRP, the transmission rates of BL and EL are also decreasing with time unit respectively. Then, we let per time unit be tunit, that is,

t0= t1=t2=t3==ti=tunit
Figure 3. The data rate of (a) BL and (b) EL under degrading RSRP.
Because of the limitative transmission rate of the serving eNodeB during a certain time interval, we have


where dBL,i and dEL,i are the transmitted number of BL and ELs during time interval ti, respectively. In Equation (9), the total transmitted number for streaming video (left part) is necessarily less than or equal to the total number of data the serving eNodeB can provide (right part). Thus, the total descent rate of transmission per tunit can be calculated as stunit. In this paper, for high QoS for video streaming, BL data has high priority for transmission. Furthermore, to achieve dynamically adjusting the transmission rate between BL and EL, we define the descent rate as

Ki is the proportion of the transmission rate between EL and BL during the time interval. That is, the transmission rate of BL is written as

Then, we calculate the transmission rate of BL in each time unit

dBL,0dBL,1=dBL,0+stunit1Ki+1dBL,2=dBL,1+stunit1Ki+1=dBL,0+2s tunit1Ki+1dBL,3=dBL,2+stunit1Ki+1=dBL,0+3s tunit1Ki+1dBL,i=dBL,0+is tunit1Ki+1=dBL,0+i s tunitKi+1
Finally, we can calculate the total transmitted BL data from time t0 to tr (pre-scheduling time before handover)

tunit[dBL,0+dBL,1+dBL,2++dBL,i]=tunit[dBL,0+dBL,1+dBL,2++dBL,(trtunit1)]=tunit[dBL,0+dBL,0+s tunitKi+1+dBL,0+2s tunitKi+1+]=tunit⎡⎣⎢trtunitdBL,0+(trtunit1+1)(trtunit1)2s tunitKi+1⎤⎦⎥=tunit⎡⎣⎢trtunitdBL,0+trs(trtunit1)2(Ki+1)⎤⎦⎥=trdBL,0+trs(trtunit)2(Ki+1)
The total transmission number of BL is required to be no less than the number of BL for maintaining high QoS for video streaming, that is,

Finally, we have

In Equation (15), because s, tunit,tho, tn, Ks, and m are pre-defined values, we only consider Ki, tr and dBL,0 in the following simulations. In this paper, for maintaining high QoS for video streaming, the BL data transmission must be given precedence over the EL data. Therefore, dBL,0 value can be determined in advance. Due to the limitation of the total number of data the serving eNodeB can provide, dEL,0 also can be determined. Eventually, Ki is decided for BL and EL transmissions. A sufficient tr represents that more pre-scheduling time can be utilized for transmitting EL data to enhance video quality. On the contrary, BL transmissions are increased to achieve high QoS for video streaming.
Research manuscripts reporting large datasets that are deposited in a publicly available database should specify where the data have been deposited and provide the relevant accession numbers. If the accession numbers have not yet been obtained at the time of submission, please state that they will be provided during review. They must be provided prior to publication.

3. Performance Evaluation

3.1. The Effect of the Prediction Mechanism

We evaluate our scheme through simulations implemented in the LTE-Sim [17] simulator. LTE-Sim can provide a thorough performance verification of LTE networks. We also utilize Video Trace Library [18] with LTE-Sim to present real-time streaming video for network performance evaluations. The simulation parameters are summarized as Table 1.

Table 1. Parameters of simulation.
The accuracy of handover prediction affects the pre-scheduling time (tr) for BL and EL transmission rate. In Figure 4, as user equipments (UEs) velocity is 30 km/h and the actual TTT of handover is 79.924 s, we can have an error rate smaller than 0.8% while the prediction is made after 59 s. On the other hand, as UE velocity is 120 km/h, the actual TTT of handover is 25.981 s and the error rate can be contained smaller than 0.5% as the prediction is made after 15 s. Faster UE results in shorter pre-scheduling time for transmissions accordingly. On the contrary, more pre-scheduling time can be used for transmissions. Therefore, we can adaptively trigger the pre-scheduling procedure and adjust the transmission rates between BL and ELs with limited resource.

Figure 4. The prediction of time-to-trigger (TTT) of handover. (a) User equipments (UEs) velocity = 30 km/h and (b) UE velocity = 120 km/h.

3.2. Base Layer Adjustment

Our goal is to provide high QoS for video streaming before new connection establishment. Since BL includes the most basic data for playing the video, for this reason, BL is needed to transmit in advance. In the following, we discuss the simulation result of BL adjustment.
As shown in Figure 5 and Figure 6, let Ki be a constant. When the starting time is approaching the actual TTT, the shortertr can be used for transmissions and the value of dBL,0 decreases accordingly. While the starting time is after 71 (Figure 4) or after 21 (Figure 5), dBL,0 increases slightly and approaches a constant. This is because there is a shorter pre-scheduling time for transmissions after 71 (Figure 5) or after 21 (Figure 6), we need to assign a higher dBL,0 for maintaining high QoS for streaming video. Furthermore, because of limitative pre-scheduling time, a greater number of users leads to higher dBL,0compared to a smaller number of users. On the other hand, high velocity causes a severe decrease of dBL,0 because of a shorter pre-scheduling time.

Figure 5. Starting time for pre-scheduling vs. dBL,0 (UE velocity = 30 km/h, actual TTT = 79.924 s).
Figure 6. Starting time for pre-scheduling vs. dBL,0 (UE velocity = 120 km/h, actual TTT = 25.981 s).
Because BL has higher priority for high QoS for video streaming, while the starting time is after 75 s (Figure 7) and 21 s (Figure 8), we can see K i has a severe decent rate, especially at higher velocity. This indicates our mechanism can provide more BL to meet high QoS for streaming video.

Figure 7. The decent rate Ki  vs. starting time (UE velocity = 30 km/h).
Figure 8. The decent rate Ki  vs. starting time (UE velocity = 120 km/h).
In the following, we set the length of pre-scheduling time tr to evaluate the relationship between K i and dBL,0. Here, Kiis a variable. In Figure 9 and Figure 10, a UE can dynamically adjust Ki for desirable video quality according to SNR values. A higher Ki indicates that dBL,0 has a lower proportion of transmission frames. While the UE requires better video quality with more data of enhanced layers transmitted, Ki can be set to a higher value. On the contrary, for a low SNR situation, Kican be set to a lower value to maintain high QoS for video streaming.

Figure 9. The decent rate Ki vs . dBL,0 (UE velocity = 30 km/h, tr = 20.924 s).
Figure 10. The decent rate Ki  vs. dBL,0 (UE velocity = 120 km/h, tr  = 8.981 s).
As shown in Figure 11 and Figure 12, our proposed mechanism achieves a higher throughput compared to the EXP/PF scheme. This is because BL has higher priority for transmission in our proposed mechanism. Furthermore, we combined the pre-scheduling mechanism with a prediction of TTT for packet transmissions. Note that BL is essential to video decoding, but the EXP/PF only fairly schedules BL and ELs transmissions.

Figure 11. Average user throughput (UE velocity = 30 km/h).
Figure 12. Average user throughput (UE velocity = 120 km/h).

4. Conclusions

In this paper, a pre-scheduling mechanism is proposed for real-time video delivery over LTE. We can adjust the data transmission rate before handover between BL and EL for high QoS for video streaming under the disconnection period by utilizing the handover prediction. The practical results show higher throughputs compared to the EXP/PF scheme.

Author Contributions

All authors contributed equally to this work. Wei-Kuang Lai and Chih-Kun Tai prepared and wrote the manuscript; Chih-Kun Tai and Wei-Ming Su performed and designed the experiments; Wei-Kuang Lai, Chih-Kun Tai and Wei-Ming Su performed error analysis. Wei-Kuang Lai gave technical support and conceptual advice.

Conflicts of Interest

We declare that we have no financial and personal relationships with other people or organizations that can suitably influence our work. There is no professional or other personal interest of any nature or type in any product, service, and/or company that could be said to influence the position presented in, or the review of, the manuscript entitled “A Pre-Scheduling Mechanism in LTE Handover for Streaming Video.


The following abbreviations are used in this manuscript:

Long Term Evolution
exponential/proportional fair
3rd Generation Partnership Project
round robin
maximum rate
proportional fair
link adaptation
Maximum-Largest Weighted Delay First
scalable video coding
base layer
enhancement layers
Reference Signal Receiving Power
exponential smoothing
least squares
quality-of experience
semi-persistent scheduling
physical resource blocks
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The Future of Telecom and Digital Services: Predictions for 2016

16 Dec

Today, telecom companies are undergoing a transformation in order to adapt their business models in a way that better engages the modern digital consumer. In 2015 we saw a great deal of shake-up in the media landscape, including a number of mergers and acquisitions and a continued rise of alternative service offerings, all pointing to an uncertain future for the market.As we evaluate what the future holds, and service providers shift their focus toward the customer experience to better engage the next generation consumer, there are a few trends that I predict will drive telecom transformation in the new year:

Service providers will try to appeal to cord cutters with new subscriptions offerings, including “skinny bundles” 

While the majority (83 percent) of American still pay a monthly cable fee, there is a growing number of consumers who are looking for options outside the standard cable and mobile packages. These “cord cutters,” as they have been dubbed by the media, want greater variety and personalization when it comes to their subscription offerings. In 2016, service providers need to focus on offering unique and customizable skinny bundles. In comparison to traditional offerings, these slimmed-down bundles would need to offer an a-la-carte feel that is attractive to consumers today, especially millennial audiences. By offering this more personalized approach, service providers can not only attract subscribers, but also begin to better engage today’s viewing audiences by providing a more customized experience.

Implementation of new distribution models and a focus on the Quality of Experience will be critical to engaging digital consumers 

The Quality of Experience will be a crucial differentiator for digital service providers in 2016. With video traffic growing to make up 80 percent of all consumer internet traffic by 2019, creating a seamless and high quality experience across devices will be critical for service providers in the new year. Digital consumers want to have access to their favorite shows from anywhere and at anytime – so they can start watching their favorite show on their tablet from the road, and finish it from their couch when they get home. Furthermore, personalization is imperative not only in the bundle offering, but also in the viewing experience. By implementing customized recommendation engines that tell viewers what they should view next based on their history, or providing integration with social media to encourage active viewing and sharing, service providers can enhance the viewing experience to make it easy, seamless and “sticky.”

Providers will increasingly innovate around their portfolio of offerings, moving beyond television viewing to attract consumers in new ways 

As digital services continue to become a major focus for service providers, my third prediction is that we will increasingly see telecom providers dipping their toes into new, and potentially unexpected, pools. Being the network provider is a huge asset for cable companies – whether wired or wireless, it gives them an almost limitless market for innovation, not only around pricing, but on potential future bundles as well.

The future service provider has the opportunity to introduce a portfolio of offerings. By exploring creative pricing bundles when integrating services such as mobile or integrating aspects of the connected home, cable companies can take advantage of data networks to fuel future bundles.

In looking to 2016, telecom providers face a number of challenges in defining their future role. However, there is also a great deal of opportunity to engage digital consumers by focusing on personalization in subscription offerings, the quality of experience across devices and exploring new innovative offerings.


WLC Video Stream- Multicast direct

15 Jun

The idea behind this new feature:

  1. Multicast packets are always sent on the highest mandatory rates from the AP to the wireless client  (might not reach clients at the edge of the cell, if the mandatory rate is not configured properly).
  2. No Ack for the multicast packets.
  3. Multicast packets are sent using best effort between the WLC and the AP then transmitted as multicast (using WLAN QoS) from the AP to the wireless clients.

WLC — (multicast as BE) –> AP — (highest mandatory rate, no ACK, WLAN QoS)  –> Wireless client

The solutions will be very simple, don’t sent the packets as BE send them as Video (AF 41, CoS 4) & send them as uni-cast from the AP to the client to solve the reliability and the data rate issue. Hence VideoStream combines several elements that work together to improve the video user experience.

Note: The AP will convert multicast to unicast frames in hardware using Direct Memory Access (DMA).

VideoStream enables you to define multicast streams on the controller and reserve bandwidth on the AP radios for these streams, called resource reservation control (RRC ) and to give a priority value to each stream, to privilege the streams.

Video Stream Configurations:

Step 1: Multicast setting

  • enable multicast globally
  • set multicast mode to multicast-multicast
  • enable IGMP snooping

Step 2: Enable Video Stream

  1. This can be done globally (recommended) or per radio band.
  2. Enable it per WLAN (This new check box will appear after enabling it globally not per band)

Step 3: Adjust the configurations per radio band

  1. Unicast Video Redirect: enabled automatically while enabling Multicast Direct globally which means the AP will send unicast to clients.
  2. Multicast Direct Admission Control:
    • CAC for Video Stream which is the sum of voice and video traffic on a radio interface.
    • Decide the percentage of the AP bandwidth that can be allocated to media streams (video and voice), 85% by default.
    • Decide the minimum data rate  for the client to be allocated a media stream (voice or video)
    • Decide how many of the lost packets should be re-sent, default is 80%
  3. Media Stream – Multicast Direct Parameters:
    • Number of  video streams are allowed per AP radio and per client.
    • Best Effort QoS Admission check box to allow non WMM clients to request and receive. Video Stream as video will be sent using WMM marking, only WMM clients are expected and allowed to request and use the unicast video stream.

Step 4: Create the streams and customize them

Stream information:

Set stream name, destination multicast address or range and expected bandwidth.

Bandwidth Reservation Control (RRC) Parameters:

  1. RRC helps the controller analyze the available bandwidth on the AP radio to only admit streams that have the space and allow to set priority which will take effect if no enough space in the cell, the WLC will privileges the streams with the higher priorities.
  2. Set average packet size and keep the RRC Periodic Update to force the controller to check the bandwidth availability at regular interval and alter the multicast stream admission accordingly.
  3. Set Violation option to decide what should happen if there is not enough space in the cell.

Note: to configure what message should be sent to users of a stream that has been dropped:  Wireless -> Media Stream -> General page -> Session Message Config section.


Useful links:



Telephony – Telco service or Internet application?

9 Jun


When comparing different forms of VoIP, one risk comparing “apples and oranges”. Broadly speaking, we can divide VoIP into two main categories. First, the service can be implemented as a faithful copy of circuit switched telephony; in a network with full control over performance and quality. Second, VoIP can be implemented as a standalone application used over the open Internet.

Originally published in NetworkWorld Norway.


3GPP and IMS

3GPP (3rd Generation Partnership Project) has played an important role when VoIP has become a recognised substitute for traditional telephony among telecom operators. 3GPP standardises the mobile technologies 2G, 3G and 4G, and they have done so based on the general IP technology standardised by IETF (Internet Engineering Task Force).

At first 3GPP concentrated on developing mobile networks as an evolving telecommunications architecture, following a vertically integrated model for provision of telephony. As the Internet revolution influenced the telecom market, the focus has shifted more towards IP-based services of various kinds.

IP networks and the Internet are not equivalent concepts. As IP technology was introduced in the mobile architecture, this was done in a way that maintained telecommunications networks’ support for QoS (Quality of Service). They had a clear view to continue provision of telecom services, as opposed to Internet applications, but based on a new IP-based network.

The service platform which was standardised as part of the mobile architecture was named IMS (IP Multimedia Subsystem). IMS is based on SIP (Session Initiation Protocol), the VoIP protocol from IETF, but extended with a comprehensive architecture for QoS. IMS has an “open” interface for service development, but requires a business agreement with the mobile operator. So this is a completely different kind of openness than the one found on the Internet where “everyone” can develop their own services.


The basic mobile architecture has undergone a tremendous development by 3GPP. Now we are in a phase where LTE (Long Term Evolution) is being adopted, often referred to as 4G despite the fact that it is not “real” 4G. LTE is the first 3GPP architecture that has eliminated the circuit switched domain, appearing as a pure IP network. Therefore there are great expectations for VoIP in this architecture, a functionality called VoLTE (Voice over LTE).

The transition from traditional telephony to VoIP has been going on for a long time. In mobile networks this has taken longer than expected. IMS has been around as a part of the mobile architecture for many years already. Furthermore, VoLTE includes options that could still delay this transition; LTE phones will initially combine LTE with older mobile technologies, allowing telephones to fall back to these older technologies. There is also a quasi-solution that transports traditional telecom protocols encapsulated in IP packets, so-called VoLGA (Voice over LTE via Generic Access).

The telecom industry also promotes advanced VoIP services that can stimulate the transition from traditional telephony and SMS to IP-based “equivalents” called RCS (Rich Communication Services). RCS provides services such as voice and video telephony, presence, instant messages and more, integrated in a unified user client for mobile phones that will provide seamless user experience of multimedia communication.

RCS is based on the IMS platform using SIP and SIMPLE (SIP for Instant Messaging and Presence Leveraging Extensions). Thus, the basis of this is IETF protocols, but implemented in an architecture that is intended to replicate the telecom network in the shape of an IP-based multimedia network. RCS is promoted by GSMA (GSM Association) and OMA (Open Mobile Alliance). OMA is the descendant of the WAP Forum, if there are still some who remember WAP.

QoS and Policy Control

RCS seems like an impressive technology, and what is the big deal? What distinguishes this from the applications that are already in use on the Internet? A major difference is that RCS can benefit directly from the mobile network built-in mechanisms for QoS. But it is difficult to predict what will give the best user experience, multimedia services integrated in the mobile architecture or free choice among different applications offered over the Internet.

A well-known characteristic of the Internet is that it is “best effort” and can’t guarantee the quality of the communication. In the mobile architecture, QoS is a key feature across the entire design. The underlying IP network will typically be based on DiffServ (Differentiated Services) and MPLS (Multiprotocol Label Switching), both well-known technologies from IETF supporting traffic management and QoS.

In the LTE architecture, QoS is policed by a function called PCC (Policy and Charging Control). As the name suggests, not unnaturally, management of QoS and charging are two sides of the same coin. PCC controls establishment of user sessions with various performance levels, and charging information is generated based to the capacity used by the different sessions.

Initially, IMS was specified for mobile networks, but in retrospect it has been found very useful extending the scope to include fixed networks, giving a combo solution which is often referred as NGN (Next Generation Networks). This facilitates convergence between fixed and mobile networks (Fixed-Mobile Convergence).

Over-the-top (OTT)

The traditional telcos are operating in a market that is completely changed because of the Internet. This leads to a situation where the business that telecom players envision, is facing strong competition from Internet players. The Internet model is based on decoupling of applications from the network layer, as opposed to the telecom model that relies on the services that are vertically integrated with the network.

Innovative solutions that can be used “over-the-top” without specific facilitation from telecom operators, enables virtually unlimited choices for end users. Internet applications, even real-time applications such as VoIP, work fairly well without the quality architecture of NGN. Congestion control mechanisms regulate traffic load of the Internet, sharing the available capacity between users.

However, users’ choice is not easy. Such innovative solutions in some cases evolve into isolated “islands” that are not compatible with each other. Major players are trying to create their own closed ecosystems consisting of operating systems or app stores for example. On the other hand, some traditional telecom operators introduce OTT solutions to meet the competition, making use of similar means.

The future will show which model is most adaptable. Net neutrality is tasked to ensure that the Internet model can develop freely. Meanwhile, the Norwegian guidelines for net neutrality are balanced, allowing the telecom model to evolve in parallel. This is often referred to as “specialised services”, as opposed to the Internet access service that works as a general electronic communication service.


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