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Viavi Solutions sees an evolution of network monitoring to meet demand from 5G, VoLTE, NFV

18 Jan

As 2016 dawns on the wireless industry and operators continue coping with the challenge of improving customer experience and reducing costs, four aiding technologies will take center stage: network functions virtualization; voice over LTE and Wi-Fi calling; self-organizing networks; and the rise of “5G” networks. While we’ve been hearing about these next-generation technologies for some time, the challenge in the next year will be ensuring they are all working to maximize business opportunity profitability. And this will require granular, end-to-end real-time visibility across all devices and parts of the network.

Today we are poised to see a real revolution in networking over the next year where network operators now have the potential to intelligently and efficiently manage the ebb and flow of traffic and exploit under-utilized resources without compromising infrastructure or the customer experience. But it will take advancements in real-time visibility to do so. As end users come to expect flawlessness from their providers, assuring service will become much more detailed than simply checking to make sure everything’s plugged in.

Network functions virtualization
NFV can significantly lower network operating costs and increase flexibility and service velocity. Today, industry guidelines are for the most part in place to allow introducing the virtualized functions themselves, but management and orchestration standards for the self-configuration required to truly enable NFV are still in their infancy.
While 2016 will see a significant increase in NFV deployments, these will primarily revolve around semi-automatic configuration – in other words, not the full-blown automation required to realize 100% of NFV’s benefit. The NFV industry is therefore likely to put a great deal of effort into developing guidelines for the management and orchestration side of NFV deployments.

The benefits of NFV will only be realized if network performance management tools can access these new, virtual network interfaces. Operators will need to invest in solutions that ensure they can satisfy quality-of-service needs, including resiliency and latency in initial virtualization deployments. This next year should show a major ramp-up in the availability of test and assurance solutions able to provide truly actionable performance insights for virtualized network environments.

Voice over LTE and Wi-Fi
The fast growth in VoLTE rollouts will continue in 2016, as it becomes the de facto voice service over the legacy voice service. But VoLTE cannot exist as an island. It needs to evolve to reflect the way people communicate today, which comprises not just voice but also data, messaging social media, video and other multimedia-rich services. This implies that assurance systems must empower more granular and flexible control over performance parameters and thresholds to meet the needs of these different applications, alongside the visibility to react in real-time to unpredictable user behaviors.

The interaction between VoLTE and VoWi-Fi will mature, characterized by soft and seamless handoffs between the access methods. Managing VoLTE end to end – meaning understanding service quality from handset to the radio access network to backhaul to core – will be a key operator goal as they ensure that their services deliver high customer quality of experience. This means deploying sophisticated assurance platforms to know in real time where VoLTE services are performing poorly and where there is a stress in the network.

Self-organizing networks
Self-organizing networks are essentially the key to a connected future. By automating configuration, optimization and healing of the network, this frees up operational resources to focus on what’s truly important – better quality of experience and aligning revenue to network optimization. And, with the number of connected “things” positively exploding, managing and keeping up with the sheer number of devices requires an automated approach that also yields a new set of network-assurance challenges operators will have to deal with in 2016.

Today, many SON techniques simply baseline a network. In 2016, as the extreme non-uniformity in the network becomes more apparent, it will take a new, end-to-end approach to SON to keep these benefits coming.

The network will become more sporadic and this will manifest in several forms: time, subscriber, location and application. For example, take subscriber and location: a recent Viavi Solutions customer study found just 1% of users consume more than half of all data on a network. The study also found 50% of all data is consumed in less than 0.35% of the network area. To achieve significant performance gains via SON, operators can apply predictive approaches using analytics that reveal exactly which users are consuming how much bandwidth – and where they are located. This level of foresight is key to not only unlocking the full potential of SON in the RAN, but also to maximizing ROI for software-defined networking and NFV in the core.

5G
2016 will be the year that at least the term “5G” proliferates, but we’re still a ways off from actual implementations. A future filled with driverless cars, drones that can deliver packages and location-based IoT products will require always-on networks with less than 1 millisecond latency – and that’s what 5G promises on paper. But 5G is imminent, and 2016 will reveal many advances toward building and delivering it to end users and their applications.

The race to 5G is bringing with it advancements in the network that inch us closer to always-on, always-fast and always improving networks. This work is pushing the industry to develop new tools and solutions that offer real-time troubleshooting and network healing, faster turn-up times and the ability to instantaneously respond to traffic spikes driven by external events. These new solutions may, at the same time, encourage new revenue streams by supporting the delivery of location- and contextually-relevant applications and services. Examples of these include mobile payment support and security as well as smart city applications for public services and emergency support.

The move to 5G is not an evolution, but a revolution – and major challenges exist across every stage of the technology deployment lifecycle and every part of the end-to-end network.

To move the needle on 5G development in 2016, operators need a partner with a wide breadth of expertise and solutions to collaborate on strategic planning and development in consideration of the significant dependencies and coordination needed for successful deployment.

Edge network configuration must change and move towards ultra-dense heterogeneous networks. Front- and backhaul transport require lower latency. These and other factors present significant challenges for commercial 5G evolution; however, the train has clearly left the station. And it will gain substantial momentum in 2016.

To 2016 and beyond

It’s exciting to watch the networking revolution – with myriad new capabilities and services surfacing thanks to evolving end-user habits and demands, the network simply cannot remain stagnant. And as new approaches – from hyped technologies like SDN/NFV or 5G – come about, operators need more sophisticated ways of ensuring it’s all working. In 2016, expect not only to see the network evolve, but also ways organizations capture and leverage analytics for assurance and optimization.

Photo copyright: wisiel / 123RF Stock Photo

Source: http://www.rcrwireless.com/20160118/opinion/2016-predictions-network-revolutions-require-new-monitoring-approaches-in-2016-tag10

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SON Progress Report: A Lot Still to Be Done!

22 May

SON

Since the first building blocks of SON were laid down around 2008 by 3GPP and NGMN, uptake in SON deployments has been very selective by a few leading carriers for some use cases. However, universal applicability remains elusive. To say the least, the SON market is struggling – but why, and how that can be turned around is what interests me. Having just attended the SON USA conference, I had made a few observations and like to put some down here.

The context: SON building blocks were laid by NGMN and 3PGG in 2008 and have progressively been revised and updated to widen the scope of SON. They used a bottom up approach to define SON use cases for LTE which has expanded with every new 3GPP release of this technology. Specifications on 3G are more limited and follow from those of LTE. Applications of SON to the macro cell has been limited to a few use cases such as configuration and provisioning (neighbor relation is one of the most used features).

The operator perspective: There are multiple sentiments aired by operators when it comes to SON. There are questions on the value proposition which is difficult to quantify. For activities that can be streamlined, operators have developed in-house processes that substitute external SON systems. Operators are also more prone to test the water with the SON system provided by the RAN vendor rather than opt for a third party SON. With this approach, operators aim to limit investment in SON. This makes more sense wherever vendors are managing operator networks – especially in this case, SON becomes a feature of the RAN that the OEM can have a complete lock on. Network engineers perceive SON as a threat in the worst case. In the meantime, SON can be a contentious domain between different functional groups within the operator organization. Operators are highly vocal about having a multi-RAN SON system, yet this is ironic since a single SON system invests power into a single SON vendor.

The vendor perspective: The vendor space can be divided into RAN equipment vendors and third parties. RAN vendors have the advantage of easy access to data that the network elements generate (OEMs can easily hamper third parties’ access to this data). However, they don’t have monopoly on smarts and third party vendors differentiate by having innovative solutions that actually solve specific problems for operators. The third parties have specifically focused on 3G networks. Yet, some of the third party solutions have a narrow focus while some of the RAN OEM solutions struggle in terms of performance.

What’s next: escape forward! This sums up the state of SON. One emerging concept is pairing SON with big data analytics. While this is an interesting idea, the devil is in the details. Analytics target a certain use case – a well defined problem which is solved by customizing a process and algorithms. Coupling SON with data sciences requires good knowledge of both spaces. How the benefits are imparted to the network still remains to be seen especially as a closed-loop approach forms the basis of such pairing. Operator resistance to closed-loop processes limits the effectiveness of this new approach.

SON is widely viewed as essential for HetNets and while the uptake in small cells has lagged market expectations, it is not strange that SON has lagged correspondingly. But waiting for HetNets to take off means, to me, that it will be many years before SON sees some traction: The pain is not large enough yet to warrant its application.

Source: http://frankrayal.com/2014/05/19/son-progress-report-a-lot-still-to-be-done/

The Rise of SON in LTE Deployments

8 Oct

4G Americas Report Details SON Enhancements in 3GPP Release 11

 

4G Americas, a wireless industry trade association representing the 3GPP family of technologies, including LTE, today announced that it has published a white paper titled Self-Optimizing Networks in 3GPP Release 11: The Benefits of SON in LTE, which outlines the enhancements of Self Organizing Network (SON) features in 3GPP Release 11 (Rel-11) and addresses the multi-vendor aspects of SON and its deployment challenges and opportunities. The SON standards provide network intelligence, automation and network management features in order to automate the configuration and optimization of wireless networks to adapt to varying radio channel conditions. With these features, SON increases efficiencies and improves network capacity, quality, performance and flexibility.

“SON has been available within the standards for quite some time, yet with the new techniques and capabilities available from vendors, it has an opportunity to be even more important to wireless carriers’ overall network strategy in the years ahead,” remarked Kamakshi Sridhar of Alcatel-Lucent and co-project leader of the 4G Americas technical group that authored the white paper.

LTE is being rapidly deployed throughout the world, with 209 commercial networks today and 250 commercial LTE networks anticipated by the end of the year. The density of wireless networks is increasing rapidly to cope with the exponential growth of user traffic, mostly driven by smartphones, tablets, connected applications and video streaming. LTE and small cells enable Heterogeneous Network (HetNet) architecture, mixing macro cells and small cells for extended coverage and capacity. However, this can increase the complexity of network operation activities in an industry working to improve and streamline efficiencies. To tackle this challenge, most, if not all, major mobile operators worldwide have or are planning to deploy SON features.

Co-project leader Pantelis Monogioudis, also from Alcatel-Lucent commented, “The densification of various cell layers in carrier networks will lead to thousands of additional small cells that will need the latest SON enhancements for operators to efficiently deploy and manage the network.”

SON is a promising feature defined by 3GPP to help operators automate several recurring tasks required for activities such as cell deployment and performance optimization for LTE, as well as a solution to manage network complexity while reducing operational expenses (OPEX).

SON focuses on three main areas:

  • Self-Configuration functions: the ability for the network to reconfigure itself automatically when nodes are added, deleted or modified, such as Automatic Neighbor Relation (ANR)
  • Self-Optimizing functions: a recurring and automated process for the dynamic tuning of network parameters for optimal performance in changing conditions, such as handling traffic density migration due to periodicity
  • Self-Healing functions: automatic compensation to restore service where it has been degraded, for example, in the case of base station outage, by dynamic reconfiguration to adjacent healthy cells

A key goal of 3GPP standardization has been the ability to support SON features in multi-vendor network environments. Therefore, a significant part of the SON standardization has been devoted to defining the appropriate interfaces to allow the exchange of common information which can then be used by each SON algorithm. The SON specifications have been built over the existing 3GPP network management architecture defined over Releases 8, 9, 10 and 11. These management interfaces are being defined in a generic manner to leave room for innovation on different vendor implementations. In addition to specifying the interfaces, 3GPP has defined a set of LTE SON use cases and associated SON functions. The standardized SON features effectively track the expected LTE network evolution stages as a function of time, following expected commercial network maturity. In Release 8, SON functionality focused on procedures associated with initial equipment installation and integration to support the commercial deployment of the first LTE networks, and in each subsequent Release, the standards are evolved to benefit the more complex network architectures.

The 4G Americas’ white paper published in 2011, Self-Optimizing Networks: The Benefits of SON in LTE, addressed the rationale for SON and the description of SON features in 3GPP Releases 8, 9 and 10. Building upon that information, the newly updated paper based on Rel-11 standards focuses on SON use cases, which play an important role in the operation of multi-vendor Heterogeneous Networks (HetNets) comprised of macro and metro cells and various architecture options and tradeoffs for implementation.

Key SON enhancements described in this report are:

  • Automatic Neighbor Relations
  • Load Balancing Optimization
  • Handover Optimization
  • Coverage and Capacity Optimization
  • Energy Savings
  • Coordination between various SON Functions
  • Minimization of Drive Tests

“SON is an important enhancement that affords operators the benefit of increasing their overall network performance,” stated Chris Pearson, President of 4G Americas. “With a scarcity of spectrum in the Americas, and in many countries worldwide, 3GPP continues to evolve the standards for network technology to serve customers’ ever growing appetite for mobile broadband data.”

Pearson added, “Although SON improves network performance and capacity issues, it is not a substitute for the wireless industry’s important need for more spectrum to meet the challenge of the impending capacity crunch.”

The white paper, Self-Optimizing Networks in 3GPP Release 11: The Benefits of SON in LTE, was written collaboratively by members of 4G Americas and is available for free downloadat: www.4gamericas.org.

 

Source: http://www.marketwired.com/press-release/the-rise-of-son-in-lte-deployments-1838517.htm

De-risking SON Deployments with Network Emulation

27 Sep

SON (Self-Organizing Network) is a promising new feature defined by the 3GPP standard to help Mobile Network Operators (MNOs) automate several recurring tasks required for activities such as cells deployment, failure compensation and performance optimization. The massive OPEX savings foreseen is the driver for SON demand. However, many MNOs are concerned that the introduction of such a disruptive technology could generate major revenue losses if not properly matured. This article shows how network emulation can help the mobile network ecosystem de-risk and deploy SON features efficiently.

What is SON?

The density and complexity of mobile networks is increasing rapidly to cope with exponential evolution of user traffic, mostly driven by smartphones, connected applications and video streaming. LTE is an answer to these challenges with a new, very flexible transmission technology associated with many innovations. It enables heterogeneous network architectures mixing macro cells and small cells for extended coverage and capacity. This extended flexibility compared to previous network generations comes with increased complexity for the network operation activities, in a landscape under stronger and stronger cost reduction pressure.

SON is a solution to manage the complexity and decrease OPEX. We can summarize it as a network operation automation technology which focuses on three main areas:
Self-Configuration functions: this is the ability for the network to re-configure itself automatically when nodes are added, deleted or modified. One example is ANR (Automatic Neighbour Relation), a feature that simplifies the reconfiguration process required when a new cell is added to a network.

Self-Optimization functions: a recurring and automated process for the dynamic tuning of network parameters for optimal performances in changing conditions. For example, handling of traffic density migration linked to periodicity of business activities.

 

Self-Healing functions: automatic compensation of network nodes failures, to restore the service where it has been degraded. For example, self-healing can handle the network coverage loss in case of base station outage, by dynamic reconfiguration of adjacent healthy cells.

The challenges

SON has been designed to decrease the manpower required for daily network operations, linked to new site deployments, handling of outages and fine-tuning of network parameters. Operational teams are under high pressure to deploy SON technologies, but they also need to keep the reliability of their networks, at least as the same level as before the SON era.

This is highly disruptive, as these operations have been managed manually for decades with reliable workflows, whereas SON maturity has not been proven yet. There is a legitimate strong resistance for swapping proven manual workflows by potentially divergent automated processes in a field where any error can lead to big revenue losses. In France, a recent 24-hour network outage has been estimated between €10 million and €20 million in repairs and compensation to customers.

Also, the growing scarcity of experienced resources in operational teams linked to the automation of daily tasks is a major concern: how to resume control of the system in manual mode in case of automation failure, when nobody owns the required know-how anymore?

We can compare the context of SON introduction to the one faced by the aeronautical industry about computerized flight control, mostly driven by the need to save weight and costs. The electric flight control technology has been used since 1958, but we had to wait for more than two decades to see in 1984 the generalization of computerized flight command in the commercial planes industry. Extensive usage of simulation associated with more and more accurate test beds has been used for technology maturation and pilots training to achieve sufficient confidence before massive deployment.

In the following we will see how, as for the aeronautical industry, the mobile network ecosystem can take advantage of network emulation technologies to de-risk the deployment of SON.

De-risking SON deployment with network emulation

SON is a complex technology associating network measurements with sophisticated analysis and decision algorithms reproducing human reasoning. The outputs of these algorithms are directly connected to operational levers of the network for a fully automated workflow. As networks are heterogeneous (multiple technologies, multiple vendors), SON systems must cope with the multiplicity of protocols, data formats and interfaces. They are looking more as labyrinthine system than as a Zen garden. Also, SON technology is so strategic that vendors put it under strong secret and sell black-boxes with very limited information on internal mechanisms.

This is where network emulation enters the game as a pragmatic solution to help all SON stakeholders to increase their trust and control over this new, promising technology.

We have seen previously that SON is tightly linked to dynamic aspects of the network with complex use cases involving multiple cells, multiple network nodes and SON equipments in front of many users with varying behaviours and sophisticated radio conditions. So, the validation and tuning of SON should be done at full system level with conditions as close as possible to the reality, including the trickiest part which is the radio environment.

Historically, tools available for mobile network testing were focused on only a few aspects of the complexity of the problem because of the limitation of available emulation technologies. Typically, up to 2009, mobile network system testing tools where spread across the following main categories:

  • System level simulators used in R&D (network vendors or research labs) with very realistic models but absolutely no means to connect to real devices and nodes;
  • Radio channel emulators associated to real handsets to check system performances with a limited number of users (typically a few dozens of users);
  • Load and stress test tools able to generate heavy traffic (several hundreds of handsets) but with non-realistic traffic, and no radio impairments emulations.

All these legacy techniques are still in use but cannot cover correctly the new test cases needed for modern telecom systems. The case of SON is critical. Proving its proper operation and stability requires using a complex system test bed composed of many different boxes coming from different vendors, with all features activated simultaneously, as in a live network. This is something that cannot be fully validated with legacy techniques.

After 2009, we have seen test tool vendors coming up with breakthrough technologies able to reproduce very realistic conditions in a lab, including the radio path, under heavy traffic (several thousands of handsets). That has been possible thanks to the phenomenal increase of the CPU power and the ability to run in real-time radio propagation models on a per handset basis with a huge number of devices on chip computing platforms.

It is now possible to set up a test bed able to run the most critical SON use cases with real network nodes and heavy traffic.

As many SON mechanisms are based on radio measurements and cells radio coverage reconfiguration, the test bed should integrate a multi-channel radio path and interferences emulation component. It should be able to handle thousands of simultaneous different radio conditions (one for each handset/eNodeB couple). This technology can reproduce easily in the lab a very complex radio scenario so that the efficiency of SON mechanisms is evaluated with the accuracy required to trust SON systems.

The following diagram (click for larger) shows a typical setup used to highlight a mobile network in the lab to assess the behavior of the SON closed loop in front of different scenarios.

This test bed is composed of the following elements:

  • A network installed in a lab composed of all relevant real nodes and SON equipments. This example shows three eNodeBs with three sectors each, but other radio topologies can be considered easily
  • A handset and radio channel emulator able to handle thousands of devices and reproduce very accurate traffic conditions down to interferences and fading effects

The emulator is configured for different scenarios needed to assess the performance and stability of the network and SON equipments such as:

  • Emulation of traffic variation throughout the day (high traffic at day time, low traffic by night) to check how SON reacts to achieve minimization of energy consumption
  • Emulation of interferences to assess the SON mechanisms in charge of coverage and capacity optimization
  • Emulation of cell outage to assess self-healing behaviour to detect and compensate cell failure

When a test campaign is running, QoE and QoS are automatically measured to build a picture of the level of end-to-end service experienced by users. Typically, a test which emulates an outage should show a temporary loss of service for some users followed by failover as soon as the SON system has detected and fixed the issue. When the network is reconfigured to handle the outage, the available capacity is lower because of the loss of one or several sectors; even if there is no coverage hole. So, the users should experience a degradation of QoE if the capacity demand cannot be fully satisfied in this situation. This effect can be measured objectively through QoE estimation embedded in the test bed.

The following diagram shows a typical synthetic report generated by such a test bed when evaluating the self-healing feature.

In this diagram, we can understand how the self-healing feature of the network is configured:

  • At time 15, one or more sectors break down
  • We see many handsets loosing connection to the network on the “No service” curve
  • The “VoLTE MOS,” “Video MOS” and “Web bitrate” are degrading because of radio coverage hole appearing around the faulty eNodeB
  • At time 45, the SON has detected the failure and reconfigured the healthy sectors to compensate for the eNodeB lost
  • Then, at time 60-90, we see “VoLTE MOS” going back near to its nominal value, meaning that voice communications are no longer significantly degraded
  • “Video MOS” is also recovering, but at a lower level because the network has lost capacity and it is configured to give priority to VoLTE
  • Web service is handled in best effort mode. As the demand is higher than the remaining capacity after the failure, it cannot recover to the initial level

As seen, the failover mode of the network causes performance degradation linked to weaker radio coverage and increasing interference for some users. These effects are fully emulated by the test bed for a valuable performance evaluation. By analyzing the curves, the MNO can verify that the SON strategy is in line with its requirements. MNOs can tune network parameters to change the strategy, depending on commercial needs. For example, another MNO may choose a different strategy and put Video at higher priority than VoLTE. After having achieved the required behavior in the lab, the MNO can safely push the new network configuration to the live network.

Summary

SON is a promising technology that should help MNOs to save OPEX by decreasing the volume of manual tasks required for day to day network operation, and shall drive improvement in capacity, quality and network performance. However, SON deployment is slowed down by concerns about its effective maturity and by the organizational impacts on operational teams.

The diversity and complexity of SON systems implies sophisticated assessment logistic in order to achieve full confidence before effective deployment. Since major SON features are tightly linked to the radio path behavior, the assessment is difficult to achieve accurately and efficiently with standard techniques and tools.

Up to date emulation technology, involving realistic radio channel emulation with thousands of virtual handsets, is paving the way to help all stakeholders to successfully assess the SON benefits and deploy it in live networks:

  • SON providers can use network emulation to tune SON software and demonstrate the stability and benefits of their products to MNOs, with realistic and convincing traffic conditions
  • MNOs can validate proper operation of SON in a multi-vendor context with their own specific traffic model and network topology
  • Operational teams can train their employees on very realistic use cases, including simulation of emergency situations

With network emulation, the ecosystem can push the usage of SON securely; as flight simulation has helped the aeronautical industry to safely deploy next generation computerized auto-pilot technologies.

Source: http://www.mobilitytechzone.com/topics/4g-wirelessevolution/articles/2013/09/25/354316-de-risking-son-deployments-with-network-emulation.htm

Inter-Cell Interference Coordination for LTE Advanced Heterogeneous Networks

9 Sep

Multi-layer heterogeneous network layout including small cell base stations are considered to be the key to further enhancements of the spectral efficiency achieved in mobile communication networks. It has been recognized that inter-cell interference has become the limiting factor when trying to achieve not only high average user satisfaction, but a high degree of satisfaction for as many users as possible. Therefore, inter-cell interference coordination (ICIC) lies in the focus of researchers defining next generation mobile communication standards, such as LTE-A.

Building upon our previous white paper, this paper provides an overview over the background calling for ICIC in heterogeneous LTE-A networks. It outlines  techniques standardized in Rel. 10 of LTE-A, discusses them showing their benefits and limitations by means of system-level simulations and motivates the importance of self optimizing network (SON) procedures for ICIC in LTE-A.

Download White Paper

Source: http://www.nomor-research.com/home/technology/white-papers/icic-for-lte-a-hetnets

Centralized SON

26 Aug

I was going through the presentation by SKT that I blogged about here and came across this slide above. SKT is clearly promoting the benefits of their C-SON (centralized SON) here.

The old 4G Americas whitepaper (here) explained the differences between the three approaches; Centralized (C-SON), Distributed (D-SON) and Hybrid (H-SON). An extract from that paper here:

In a centralized architecture, SON algorithms for one or more use cases reside on the Element Management System (EMS) or a separate SON server that manages the eNB’s. The output of the SON algorithms namely, the values of specific parameters, are then passed to the eNB’s either on a periodic basis or when needed. A centralized approach allows for more manageable implementation of the SON algorithms. It allows for use case interactions between SON algorithms to be considered before modifying SON parameters. However, active updates to the use case parameters are delayed since KPIs and UE measurement information must be forwarded to a centralized location for processing. Filtered and condensed information are passed from the eNB to the centralized SON server to preserve the scalability of the solution in terms of the volume of information transported. Less information is available at the SON server compared to that which would be available at the eNB. Higher latency due to the time taken to collect UE information restricts the applicability of a purely centralized SON architecture to those algorithmsthat require slower response time. Furthermore, since the centralized SON server presents a single point of failure, an outage in the centralized server or backhaul could result in stale andoutdated parameters being used at the eNB due to likely less frequent updates of SON parameters at the eNB compared to that is possible in a distributed solution.

In a distributed approach, SON algorithms reside within the eNB’s, thus allowing autonomous decision making at the eNB’s based on UE measurements received on the eNB’s and additional information from other eNB’s being received via the X2 interface. A distributed architecture allows for ease of deployment in multi-vendor networks and optimization on faster time scales. Optimization could be done for different times of the day. However, due to the inability to ensure standard and identical implementation of algorithms in a multi-vendor network, careful monitoring of KPIs is needed to minimize potential network instabilities and ensure overall optimal operation.

In practical deployments, these architecture alternatives are not mutually exclusive and could coexist for different purposes, as is realized in a hybrid SON approach. In a hybrid approach, part of a given SON optimization algorithm are executed in the NMS while another part of the same SON algorithm could be executed in the eNB. For example, the values of the initial parameters could be done in a centralized server and updates and refinement to those parameters in response to the actual UE measurements could be done on the eNB’s. Each implementation has its own advantages and disadvantages. The choice of centralized, distributed or hybrid architecture needs to be decided on a use-case by use case basis depending on the information availability, processing and speed of response requirements of that use case. In the case of a hybrid or centralized solution, a practical deployment would require specific partnership between the infrastructure vendor, the operator and possibly a third party tool company. Operators can choose the most suitable approach depending upon the current infrastructure deployment.

Finally, Celcite CMO recently recently gave an interview on this topic on Thinksmallcell here. An extract below:

SON software tunes and optimises mobile network performance by setting configuration parameters in cellsites (both large and small), such as the maximum RF power levels, neighbour lists and frequency allocation. In some cases, even the antenna tilt angles are updated to adjust the coverage of individual cells.
Centralised SON (C-SON) software co-ordinates all the small and macrocells, across multiple radio technologies and multiple vendors in a geographic region – autonomously updating parameters via closed loop algorithms. Changes can be as frequent as every 15 minutes– this is partly limited by the bottlenecks of how rapidly measurement data is reported by RAN equipment and also the capacity to handle large numbers of parameter changes. Different RAN vendor equipment is driven from the same SON software. A variety of data feeds from the live network are continuously monitored and used to update system performance, allowing it to adapt automatically to changes throughout the day including outages, population movement and changes in services being used.
Distributed SON (D-SON) software is autonomous within each small cell (or macrocell) determining for itself the RF power level, neighbour lists etc. based on signals it can detect itself (RF sniffing) or by communicating directly with other small cells.
LTE has many SON features already designed in from the outset, with the X.2 interface specifically used to co-ordinate between small and macrocell layers whereas 3G lacks SON standards and requires proprietary solutions.
C-SON software is available from a relatively small number of mostly independent software vendors, while D-SON is built-in to each small cell or macro node provided by the vendor. Both C-SON and D-SON will be needed if network operators are to roll out substantial numbers of small cells quickly and efficiently, especially when more tightly integrated into the network with residential femtocells.
Celcite is one of the handful of C-SON software solution vendors. Founded some 10 years ago, it has grown organically by 35% annually to 450 employees. With major customers in both North and South America, the company is expanding from 3G UMTS SON technology and is actively running trials with LTE C-SON.
Quite a few companies are claiming to be in the SON space, but Celcite would argue that there are perhaps only half a dozen with the capabilities for credible C-SON solutions today. Few companies can point to live deployments. As with most software systems, 90% of the issues arise when something goes wrong and it’s those “corner cases” which take time to learn about and deal with from real-world deployment experience.
A major concern is termed “Runaway SON” where the system goes out of control and causes tremendous negative impact on the network. It’s important to understand when to trigger SON command and when not to. This ability to orchestrate and issue configuration commands is critical for a safe, secure and effective solution.\
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