The blog YTD2525 contains a collection of clippings news and on telecom network technology.
Beside of the
OnItemGet() events, I added an extra
OnClear() event which gets called when the ring buffer Clear() method gets called. The events are disabled by default not to add any overhead, and they can be enabled individually.
Using the CDE (Component Development Environment) of Processor Expert makes it very easy to such additional events: define the interface for the event, and then add the event code to the driver. Below shows it for the Put() method:
To check the details of the change, see the commit on GitHub.
So with using the new RingBuffer events in my USB stack, I can get application notifications for every byte received or sent, which is very useful.
The updated component will be available with the next *.PEupd release.
SK Telecom’s Network Evolution Strategies: Carrier aggregation, inter-cell coordination and C-RAN architecture8 Oct
SK Telecom is the #1 mobile operator in Korea, with sales of KRW 16.6 trillion (USD 15.3 billion) in 2013, and with 50.1% of a mobile mobile subscription market share in 2Q 2014. It launched LTE service back in July 2011, and now more than half of its subscribers are LTE service subscribers, with 55.8% of LTE penetration as of 2Q 2014.
Due to LTE subscription growth, more advanced device features, and high-capacity contents, LTE networks are experiencing an unprecedented surge in traffic. To accommodate the flooded traffic, SK Telecom adopted LTE-A (Carrier Aggregation, CA) in 2013, and Wideband LTE-A (Wideband CA) in 2014 for improved network capacity.
As another effort to increase network capacity, the company made LTE/LTE-A macro cells a lot smaller, as small as hundreds of meters long, resulting in an increased number of cell sites. To save costs of building and operating the increased number of cell sites, it has built C-RAN (Advanced-Smart Cloud Access Network, A-SCAN, as called by SK Telecom) through BBU concentration since January 2012.
In 2014, SK Telecom began to introduce small cells (low-power small RRHs) in selected areas. As with macro cells, small RRHs have the same C-RAN architecture where they are connected to concentrated BBU pools through CPRI interfaces. SK Telecom calls it “Unified RAN (Cloud and Heterogeneous)”.
To prevent performance degradation at cell edges caused by introduction of small cells, SK Telecom developed HetNet architecture (known as SUPER Cell) where macro cells cooperate with small cells. The company, aiming to commercialize 5G networks in 2020, plans to commercialize SUPER Cell first in 2016, as a transitional phase to 5G networks.
Figure 1. SK Telecom’s Network Evolution Strategies
We analyzed SK Telecom’s network evolution strategies using the following three axes: 1) Carrier Aggregation (CA), 2) Inter-Cell Coordination, and 3) RAN Architecture in the Figure 1. Here, the CA axis shows how speeds have been and can be increased (n times) by expanding total frequency bandwidth aggregated. The Inter-Cell Coordination axis displays the company’s strategy to achieve higher speeds at cell edges by improving frequency efficiency. Finally, the RAN Architecture axis shows SK Telecom’s plan to switch to an architecture that would yield better LTE-A performance at reduced costs of building and operating RAN. Figure 2 is SK Telecom’s evolved LTE-A network, as illustrated according to the evolution strategies shown in Figure1.
Figure 2. SK Telecom’s LTE-A Evolution Network
1. CA Evolution Strategies
CA is a technology that combines up to five frequencies in different bands to be used as one wideband frequency. It allows for expanded radio transmission bandwidth, which would naturally boost transmission speeds as much as the bandwidth is expanded. So, for example, if bandwidth is increased n times, then so is the transmission speed. Table 1 shows the LTE frequencies that SK Telecom has as of September 2014, totaling 40 MHz (DL only) across three frequency bands, which operate as Frequency Division Duplexing (FDD).
SK Telecom commercialized CA in June 2013 for the first time in the world, and then Wideband CA a year later in June 2014.
It is now offering a maximum speed of 225 Mbps through the total 30 MHz bandwidth. As of May 2014, out of the total 15 million LTE subscribers, 3.5 million (23%) subscribers are using CA-enabled devices. Let’s see where SK Telecom’s CA is heading.
1.1 Combining More Bands: 3-band CA
3-band CA combines three frequency bands, instead of the current two, for wider-band transmission. Currently, SK Telecom has three LTE frequency bands, and is offering 2-band CA of 20 MHz or 30 MHz by combining two of the bands at once. This is because, although LTE-A standards technically support combining of up to five frequency bands, RF chips in CA-enabled mobile devices available now can support combining of two bands only.
3-band LTE devices are on the way and will be arriving in the market soon – sometime in early 2015 or by late 2014 at the latest. So, SK Telecom is planning to commercialize 3-band CA that combines all of its three frequency bands, just in time. The commercialization of 3-band CA is expected to increase transmission bandwidth to 40 MHz and data transmission rate to 300 Mbps. SK Telecom is also planning to combine three 20 MHz bands to further expand transmission bandwidth up to 60 MHz, and boost data transmission rate to 450 Mbps.
1.2 Femto Cell with CA
SK Telecom commercialized LTE Femto cell for the first time in the world in June 2012, to provide indoor users with more stable communication quality, and now is attempting to apply CA technology to Femto cell as well. The company completed a technical demonstration of LTE-A Femto cell in MWC 2014, proving it is capable to support 2-band CA. It will be conducting trial tests in a commercial network in late 2014 for final commercialization of the technology in 2015.
1.3 Combining Heterogeneous Networks: LTE-Wi-Fi CA
In July 2014, SK Telecom performed a technical demonstration of heterogeneous CA that combines LTE and Wi-Fi bands by using multipath TCP (MPTCP), an IETF standard. MPTCP is designed to combine more than one TCP flow (or MPTCP subflow) to make a single MPTCP connection, and send data through it. This technology is applied to a device and application server. In the demonstration, an MPTCP proxy server was used instead of an application server (Figure 3).
Figure 3. LTE – Wi-Fi CA using Multipath TCP (MPTCP)
This technology will allow SK Telecom to combine i) its LTE bands that are currently featuring 2-band CA and ii) 802.11ac-based Giga Wi-Fi bands, together offering up to 1 Gbps or so.
The detailed commercialization timeline is to be determined in accordance with the company’s plan for future development of MPTCP device and server.
1.4 Combining Heterogeneous LTE Technologies: FDD-TDD CA
This method enables operators to expand transmission bandwidth by combining two different types of LTE technologies: FDD-LTE and TDD-LTE. In a demonstration performed in Mobile Asia Expo in June 2014, SK Telecom successfully demonstrated FDD-TDD CA using ten 20 MHz bandwidths and 8×8 MIMO antenna showing 3.8 Gbps throughout.
Handover is one of the key operations in the mobility management of long-term evolution (LTE)-based systems. Hard handover decided by handover margin and time to trigger (TTT) has been adopted in third Generation Partnership Project (3GPP) LTE with the purpose of reducing the complexity of network architecture. Various handover algorithms, however, have been proposed for 3GPP LTE to maximize the system goodput and minimize packet delay. In this paper, a new handover approach enhancing the existing handover schemes is proposed. It is mainly based on the two notions of handover management: lazy handover for avoiding ping-pong effect and early handover for handling real-time services. Lazy handover is supported by disallowing handover before the TTT window expires, while early handover is supported even before the window expires if the rate change in signal power is very large. The performance of the proposed scheme is evaluated and compared with two well-known handover algorithms based on goodput per cell, average packet delay, number of handovers per second, and signal-to-interference-plus-noise ratio. The simulation with LTE-Sim reveals that the proposed scheme significantly enhances the goodput while reducing packet delay and unnecessary handover.
The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.
“We must stop the confusion about which technology is going to win; it achieves nothing positive and risks damage to the entire industry.”
Anyone among the curious band of people who track articles about the status of mobile broadband (and the chances are that you are one of them) will have noticed an interesting trend over the past 18 months: the temperature of the debate about the technology most likely to succeed is rising rapidly. Increasingly polarised articles are published on a daily basis, each arguing that Long Term Evolution (LTE) is the 4G technology of choice, or that WiMAX is racing ahead, or that it’s best to stick with good old 3GPP because HSPA+ is going to beat both of them. It remains surprising that their articles invite us, their readers, to focus slavishly on the question “WiMAX vs. LTE vs. HSPA+: which one will win?”
The question that we should ask of the authors is “Who cares who wins?” The torrent of propaganda washes over the essence of mobile broadband and puts sustained growth in the mobile industry at risk. By generating fear, uncertainty and doubt, the mobile broadband “battle” diverts attention away from the critical issues that will determine the success or failure of these evolving technologies. The traditional weapon of the partisan author is the mighty “Mbps”; each wields their peak data rates to savage their opponents.
In the HSPA+ camp, authors fire out theoretical peak data rates of 42Mbps DL and 23 Mbps UL. The WiMAX forces respond with theoretical peak data rates of 75Mbps DL and 30Mbps UL. LTE joins the fray by unleashing its theoretical peak data rates of 300Mbps DL and 75 Mbps UL. All hell breaks loose, or so it would appear. Were it not for the inclusion of the word “theoretical”, we could all go home to sleep soundly and wake refreshed, safe in the knowledge that might is right. The reality is very different.
Sprint has stated that it intends to deliver services at between 2 and 4 Mbps to its customers with Mobile WiMAX. In the real world, HSPA+ and LTE are likely to give their users single digit Mbps download speeds. Away from the theoretical peak data rates, the reality is that the technologies will be comparable with each other, at least in the experience of the user. These data rates, from a user’s perspective, are a great improvement on what you will see while sitting at home on your WiFi or surfing the web while on a train. The problem is that the message being put out to the wider population has the same annoying ringtone as those wild claims that were made about 3G and the new world order that it would usher in. Can you remember the allure of video calls? Can you remember the last time you actually saw someone making a video call?
3G has transformed the way that people think about and use their mobile phones, but not in the way that they were told to expect. In the case of 3G, mismanagement of customer expectations put our industry back years. We cannot afford to repeat this mistake with mobile broadband. Disappointed customers spend less money because they don’t value their experience as highly as they had been led to expect by advertisers. Disappointed customers share their experience with friends and family, who delay buying into the mobile broadband world. What we all want are ecstatic customers who can’t help but show off their device. We need to produce a ‘Wow’ factor that generates momentum in the market.
Every pundit has a pet theory about the likely deployment of mobile broadband technologies. One will claim that HSPA+ might delay the deployment of LTE. Another will posit that WiMAX might be adopted, predominantly, in the laptop or netbook market. A third will insist that LTE could replace large swathes of legacy technologies. These scenarios might happen, but they might not, too.
More likely, but less stirring, is the prediction that they are all coming, they’ll be rolled out to hundreds of millions of subscribers and, within five years, will be widespread. We must stop the confusion about which technology is going to win; it achieves nothing positive and risks damage to the entire industry.
Confusion unsettles investors, who move to other markets and starve us of the R&D funds needed to deliver mobile broadband. At street level, confusion leads early adopters to hold off making commitments to the new wave of technology while they “wait it out” to ensure they don’t buy a Betamax instead of a VHS. Where we should focus, urgently, is on the two topics that demand open discussion and debate. First, are we taking the delivery of a winning user experience seriously? Secondly, are we making plans to cope with the data tidal wave that will follow a successful launch?
The first topic concerns delivery to the end user of a seamless application experience that successfully converts the improved data rates to improvements on their device. This can mean anything from getting LAN-like speeds for faster email downloads through to slick, content-rich and location-aware applications. As we launch mobile broadband technologies, we must ensure that new applications and capabilities are robust and stable. More effort must be spent developing and testing applications so that the end user is blown away by their performance.
The second topic, the tidal wave of data, should force us to be realistic about the strain placed on core networks by an exponential increase in data traffic. We have seen 10x increases in traffic since smartphones began to boom. Mobile device makers, network equipment manufacturers and application developers must accept that there will be capacity shortages in the short term and, in response, must design, build and test applications rigorously. We need applications with realistic data throughput requirements and the ability to catch data greedy applications before they reach the network.
In Anite, we see the demands placed on test equipment by mobile broadband technologies at first hand. More than testing the technical integrity of the protocol stack and its conformance to the core specifications, we produce new tools that test applications and simulate the effects of anticipated capacity bottlenecks. Responding to the increased demand for mobile applications, we’re developing test coverage that measures applications at the end-user level. Unfortunately, not everyone is thinking that far ahead. Applications that should be “Wow”, in theory, may end up producing little more than a murmur of disappointment in the real world.
So, for the sake of our long-term prospects, let’s stop this nonsense about how one technology trounces another. Important people, the end users, simply do not care. WiMAX, LTE and HSPA+ will all be widely deployed. As an industry, our energy needs to be focused on delivering services and applications that exceed the customer expectations. Rather than fighting, we should be learning from each other’s experiences. If we do that, our customers will reward us with growing demand. If we all get sustained growth, then don’t we all win..?
As mobile data usage proliferates, so does the demand for capacity and coverage, particularly with the rise of connected devices, data-hungry mobile apps, video streaming, LTE roll-outs and the popularity of the smartphone and other smart devices. With mobile data traffic expected to double annually, existing mobile backhaul networks are being asked to handle more data than they were ever designed to cope with, and operators are being asked to deal with a level of capacity demand far greater than ever could have been imagined.
Breaking the backhaul bottleneck
The demand on operators to provide more, and faster, services for the same costs is putting mobile backhaul networks under intense pressure, and effectively means the operator ARPU (Average Revenue per User) is in decline. iGR Research Company has confirmed that the demand on mobile backhaul networks in the US market will increase 9.7 times between 2011 and 2016, fueled by rapidly growing data consumption, faster than operators can keep up with. Surging data traffic is stressing existing connections and forcing many operators to invest in their network infrastructures in order to remain competitive and minimize subscriber churn.
Mobile operators realize that in order to meet capacity, coverage and performance demands, while raising their ARPU, they need to evolve their mobile backhaul networks to perform better and be more efficient. As the capacity and coverage demands accumulate, mobile backhaul evolution comes to the forefront as an area that operators must address and align with growing demand.
Evolution not revolution
As wireless technologies have developed over the years, a mixture of transmission technologies and interfaces to Radio Access Network (RAN) equipment have been utilized to support communications back to the mobile network operator, including 2G, 3G and now 4G LTE. Today, operators evolve their backhaul by converging multiple backhaul technologies into one unified technology and converging multiple parallel backhaul networks into a single all-IP network. Based on IP and MPLS, having one, all-IP network makes more efficient use of network resources, reduces operational costs, and is cheaper to manage and maintain. IP gives operators the ability to converge RAN traffic and MPLS technology addresses the challenge of
Source: A Knowledge Network Article by the Broadband Forum http://www.totaltele.com/view.aspx?C=1&ID=487671
LTE growth is being driven by consumer demand for data, the absence of fixed line infrastructure in many parts of emerging APAC (EMAP), and the need to provide the network capacity to enable next-generation mobile and services.
Operators are desperately looking to efficiently scale network capacity; wireless technology holds the key to delivering the performance and profits operators require as the mobile landscape changes dramatically.
Consumers all over the world want the fastest network, with the highest quality of experience. This is no more evident than in the Asia-Pacific (APAC) region where LTE is now out of the experimental stage and being deployed widely across most of the developed markets in the region. According to a new report by Allied Market Research, APAC is forecast to surpass other geographical markets by 2020 with approximately 40 per cent of the global LTE market. Analysys Mason is also forecasting that APAC and Latin America will account for the majority of the networks that are planned for launch by 2018. A recent report from the Global Mobile Suppliers Association confirms the demand for LTE networks, estimating an approximate 200 million LTE subscribers globally with the APAC region boasting 77.8 million, a 38.8 per cent share of the overall subscribers.
LTE growth drivers
LTE growth is being driven by consumer demand for data, the absence of fixed line infrastructure in many parts of emerging APAC (EMAP), and the need to provide the network capacity to enable next-generation mobile and services. Rapid economic development, which has increased the region’s prosperity, has also been a factor in making mobile services more affordable and helped seed the LTE ambitions of operators.
In addition, access to high-speed LTE is facilitating a wide variety of socio-economic benefits across APAC, encouraging governments to incentivise operators to deploy next generation networks. LTE is helping people lead more productive lives and, for example, enabling businesses to become more efficient in delivering goods and services. The onset of widespread broadband connectivity across the region is sustaining this economic development with improved networks in some of the countries in the EMAP region, empowering education, increasing trade and driving innovation.
Growth in LTE, and the subsequent rise in mobile data traffic, is leading to an increase in infrastructure investment. Operators have the challenge of efficiently scaling infrastructure which delivers the capacity to satisfy consumer appetite for mobile connectivity and support the array of new services being deployed across the region.
This challenge is evident in the diversity of development across APAC’s mobile market which has led to a multitude of LTE network adoption scenarios. The variety is evident in the 47 countries and 3.7 billon people in the region which contain many intricacies and complexities due to economic, political and geographic factors. South Asia, for instance, reflects a diverse mix of mobile and internet diffusion patterns. Malaysia and Singapore have a mature network infrastructure and mobile penetration exceeding 100 per cent, whilst countries like the Philippines and Indonesia are still considered to have a developing infrastructure.
It is expected that these EMAP regions will be able to take most advantage of the demand for LTE networks rather than the developed APAC (DVAP) regions that have more mature offerings. However, managing and constructing an LTE network has many factors to consider, not least the technical requirements needed for mobile backhaul. As always, the cost of backhaul is a paramount consideration in running and launching new networks.
The Philippines is a good example of the complexities of managing LTE networks. A recent report by OpenSignal Inc. has concluded that the Philippines have the slowest LTE connection among the 16 countries surveyed, with 5.3 Mbps (megabits per second). Operators in EMAP regions have increasing pressure to provide the capacity needed to handle the huge data demands from smartphones, tablets and new technologies such as M2M. Operators are in danger of failing to provide consumers and business with the fast, high quality network that is demanded of LTE.
Operators have known for some time that they need to drive innovation in their business processes and run networks at a much lower cost per bit to achieve success. However, the extensive capital expenditures (CAPEX) and operating expenditure (OPEX) challenge in setting up new infrastructure is seeing operators struggle to make a successful business case. For example, putting vast amounts of fibre networks into the ground can encounter huge costs and lengthy time to the market as well as a geopolitical minefield of regulation which can reduce an operator’s return on investment (ROI). Even worse, fibre can suffer from poor reliability and high maintenance costs due to either deliberate or accidental damage. Increasingly, operators are turning to a new wave of efficient, flexible and high capacity wireless technologies, including point-to-multipoint (PMP) microwave.
Traditionally, the low ARPU in the APAC countries puts even more emphasis on operators to make efficiencies. This means that, for the overall business case to work, every bit of data must be delivered at the lowest possible cost, and it’s this imperative that makes operators turn to innovative solutions like PMP microwave. Because the hub radio itself, as well as the backhaul spectrum, are shared across a number of LTE sites in the sector; both the hub equipment and spectrum cost are amortised across this number of links. Analyst consultancy Senza Fili recently found this allows PMP microwave to deliver savings of up to 50 per cent over other forms of backhaul, while delivering the same carrier-grade service essential for LTE.
PMP microwave uses area-licensed spectrum to create a sector of backhaul coverage from a single hub site and ensures the guaranteed quality of service LTE demands. Multiple cell sites can be backhauled within this sector, and bandwidth is dynamically shared across all links. Due to this real-time allocation of spectrum, PMP microwave enables the ‘troughs’ of one cell site’s traffic demands to be filled by the ‘peaks’ of another. This aggregation reduces the total bandwidth required for a sector and has been proven to improve spectral efficiency by at least 40 per cent when compared to traditional point-to-point (PTP) technology. By efficiently managing the backhaul spectrum required for LTE, operators can run networks at a much lower cost and achieve a higher ROI – crucial at a time where revenues are under threat. Importantly, PMP microwave (which operates above 6GHz) has the capacity to handle the most demanding LTE networks and is already proven in LTE backhaul deployments in other regions of the world.
Whilst the enormous promise of LTE is clearly evident, operators still need to look at new and innovative ways to unlock the true potential of their backhaul infrastructure and increase ROI. Many operators see the deployment of multiple virtual networks over a common physical network as the answer. Some operators currently choose to build completely new LTE or enterprise access networks to sit alongside legacy infrastructure, however this can create inefficiencies across the different generations of technologies.
The latest backhaul technology now allows for new profitable business models to be created. By creating a converged backhaul network, LTE backhaul can be accommodated whilst also using the virtual networking capability to monetise spare capacity by deploying additional services to businesses. A converged PMP microwave backhaul network, for instance, enables operators to introduce fixed enterprise access services on the same LTE network – serving business with next generation connectivity.
This efficient use of backhaul and spectrum enables operators to invest in fast mobile speeds and carrier grade services, whilst allowing for competitive pricing and increasing profitability. This increase in ROI is particularly beneficial at a time where the fragmentation of spectrum is a particular issue for APAC.
The long term growth prospects for mobile broadband in APAC are enormous as operators are finding consumers and businesses hungry for transformational mobile and internet services. With operators desperately looking to efficiently scale network capacity, wireless technology holds the key to delivering the performance and profits operators require as the mobile landscape changes dramatically.
New business models and innovative wireless backhaul will not only protect investments in LTE but pave the way for new services and revenue opportunities – helping operators reduce churn in what is becoming an increasingly competitive market.
It is an exciting opportunity for operators in APAC to upgrade their technology for LTE and bring new innovative services to the market. With cost savings obtained by increased efficiency and utilisation of resources, quality of service or features need not be sacrificed with wireless technologies. As customer preferences change and mature in the APAC region, there is huge potential in the market to deploy efficient and flexible wireless technologies to build fast successful networks.
LTE Direct, a new feature being added to the LTE protocol, will make it possible to bypass cell towers, notes Technology Review. Phones using LTE Direct (Qualcomm whitepaper), will be able to “talk” directly to other mobile devices as well as connect to beacons located in shops and other businesses.
The wireless technology standard is baked into the latest LTE spec, which is slated for approval this year. It could appear in phones as soon as late 2015. Devices capable of LTE Direct can interconnect up to 500 meters — far more than either Wi-Fi or Bluetooth. But issues like authorisation and authentication, currently handled by the network, would need to be extended to accommodate device to device to communication without the presence of the network.
At the LTE World Summit, Thomas Henze from Deutsche Telekom AG presented some use cases of proximity services via LTE device broadcast.
Since radio to radio communications is vital for police and fire, it has been incorporated into release 12 of the LTE-A spec, due in 2015.
At Qualcomm’s Uplinq conference in San Francisco this month, the company announced that it’s helping partners including Facebook and Yahoo experiment with the technology.
Facebook is also interested in LTE Multicast which is a Broadcast TV technology. Enhanced Multimedia Broadcast Multicast Services (also called E-MBMS or LTE Broadcast), uses cellular frequencies to multicast data or video to multiple users, simultaneously. This enables mobile operators to offer mobile TV without the need for additional spectrum or TV antenna and tuner.
|Intuitive Understanding In most of the wireless communication environement, a signal out of a transmitter radiate into wide direction and these radiated signal takes different path and arriving at the reciever at different timing and with different signal strength(amplitude). As a result, the signal coming into the reciever is the composite of all the components. As you may learned in high school physics, when thetwo copies of the signal get combined the resulting signal can be an augmented signal or attenuated signal depending on whether the two signal constructively combined or destructively combined. Then what determines whether the signals constructively combine or destructively combine ? The answer also come from high school physics. The phase of the two signal determines whether the signal constructively combine or destructively combine.
In wireless communication environment, many copies of the signals get combined at the reciever side and some of them constructively combines and some of them destructively combines. So final result of combination of all the incoming signals become very complicated and the combined signal becomes drastically different from the original signal tranmitted from the source. In most case, the quality of the combined signal at the reciever gets poorer (deteriorated) than the original signal. This kind of process of signal deterioration by the multiple propogation path of a signal is called ‘Fading’. When we say “Fading”, it usually implies “Signal quality gets bad”.
For more intuitive unerstanding of the fading, I will show you a couple of different aspect of fading you can mesure using various equipments.
First, let’s compare a faded signal and non-faded signal using a spectrum analyzer. You would get the two results as follows and you will see the highly fluctuating amplitude across the channel bandwidth. (Note : These two capture are not the one from the same signal, so comparing the absolute value of the amplitude does not many any sense. Just take the image of overall amplitude profile).
Now let’s look at how the fading influence the signal quality decoded by the reciever. Look at the following samples of constellation for faded and non-faded signal.
Now let’s get into even further and I think this is the thing that you would be most interested in. How this fading would influence the final performance. Following graph shows the data rate at PHY layer and PDCP layer. The plot showing at the top (labeled as ‘PHY Transmission Rate’) is the amount of data being transmitted per second by PHY layer of the transmitter. The plot in the middle (labeled as ‘PHY throughput with HARQ ACK’ is the amount of data getting ACKED per second from the reiever PHY layer. You see there is pretty much gap between the two plots. It means that a considerable portions of the data were failed to get properly decoded by the reciever and the reciever sent NACK for the data. Normally in this case, the transmitter PHY layer retransmits the previous data rather than moving into the next step of the transmission.
The plot at the bottom (labelled as ‘PDCP Transmission Rate’) is the amount of data being sent to the lower layer from the transmitter’s PDCP layer. You will see another gap here. It is not easy to explain exactly what is causing this gap. In this case, some portions of the gap would come from the overhead of higher layer data structure but majority of the gap would be because PDCP cannot push the data down to the lower layer since PHY layer is too busy with retransmitting the previous data rather than transmitting the new data coming from the higher layer.
Major Components of Fading
There are many factors generating Fading effect. Followings are the list of components (factors) of fading we can think of.
To study on fading requires the detailed understanding of each of these components. If I express some major components in visual form, it can be presented as follows. Just from the high level view, you should see some typtical pattern from this representation. First notice that the horizontal axis represents ‘Distance in Linear Scale’. You may have seen a lot of different plots in various textbook and get confused by those. One of the first step to remove those confusion would be clearly understanding the meaning of axis of the graph. Even for the same things, you would see different shape of plots if you define the meaning of the axis differently. One of the factors from various text confusing you the most is the scale of each axis. For example, in case of path loss graph in some textbook where they represent the axis in log scale you would see a straight line, but in this page you are seeing a kind of negative exponential graph since the axis is represented in linear scale.
Now let’s look into each of the graphs.
Path loss is just gradually decreases but not much of fluctuation. In the second plot, you see some fluctuation of the signal strength but the fluctuation frequency over the distance is not that high. In the third plot, you see fluctuations of signal strength and the fluctuation frequency is also pretty high.
When you observe a real received signal, you wouldn’t see these factors in separate form as in the graph on the left side. What you see at the real reciever is the result of summation of all the factors and it is like the graph on the right side.
Path Loss and Shadowing
Path Loss and Shadowing may not be considered to be a part of Fading in narrow sense, but it can be considered as a factor causing the fading in wider sense. Understanding or Modeling the path loss is not that difficult. You may see a mathematical model which you were faimilar with in your high school math. The famous format of anti-proportional to ‘something squared’.
Modling for Path Loss is done with the respect to the distance between a transmitter and receiver. The key question is ..
Test A) When a transmitter antenna is transmitting a certain (fixed) amount of energy and you put a measurement antenna which is a certain distance (let’s call this distance as ‘d’) and measure the received(measured) energy. Once you get the value at a certain ‘d’, write down the value. And then move the reciever antenna a little bit further and do the same measurement and write down the value. Repeat this process as you move ‘d’ further and further and collect all the measured value and plot it on the graph with horizontal axis = d and vertical axis = measured energy level (power). How would the plot look like ? Can you come up with an equation to represent the graph ?
Test B) Now let’s try a little bit different type of measurement. This time, draw a circle with radius d with the transmitter antenna at the center. and then put several antenna along the circumference and measure the received power at each antenna and plot the value with horizontal axis = location of receiver antenna and vertical axis = measured energy level (power). How would the plot look like ?
Test C) Now let’s think about two test environment. In one setup there is no obstacles between the reciever and transmitter antenna (Let’s call this as ‘Setup A’). In the other setup, there are a lot of obstacles (like building, tree, houses, mountain etc) between the transmitter antenna and reciever antenna (Let’s call this as ‘Setup B’). Then you do the measurement described in Test B) for both setup and compare the result. Would you see any difference ? what kind of difference would you see ?
These three test will be used as a basis for modeling following cases. (Details will come later)
< Path Loss Mode in Free Space >
< Path Loss Mode in Real Situation : Shadowing >
Channels with MultiPath
A defining characteristic of the mobile wireless channel is to build up a mathematical model for the variations of the channel strength over time and over frequency. There are mainly two factors influencing the variations of the channel strength as listed below.
Let’s suppose we have a case as shown below. A UE is communicating with a eNodeB and there is a lot of building between the UE and the eNodeB. You may intuitively guess the eNodeB would get the multiple copies of the same signal from UE because the same signal can reach the eNodeB via multiple path as shown below. Let’s assume that we have four different path as shown below. There are two major characteristics of the paths. One is attenuation and the other one is time delay. Of course there would be several additional factors, but these two components would be the largest factors. Let’s assume that each of the path has the delay and attenuation as shown below.
How do we represent a channel using these delay and attenuator factor ? There can be a couple of different ways to represent it, but one of the most common way would be to represent it as an impulse response model. Even though you may not familiar with the term ‘impulse response’, you may have seen this kind of description if you have any experience of using channel simulators (fader) and dealt with fading profile. Following is the graphical representation of impulse response of the four paths in this example and physical meaning of it. I hope this make sense to you.
If you represent this impulse response into a mathematical form, it become as follows. If you are not familiar with this kind of mathematical representation. Read again the section of Signal Representation. (When you are first learning this way of signal representation.. you may think .. what the hell is delta function ? Why we need this kind of function ? etc… this is one example why you need to learn those textbook concept).
Let’s look into a example that (hopefully) would give you more concrete understanding of these concepts.
Let’s suppose that a signal goes through six different path (multipath) as indicated in plot (1). Horizontal axis represents the time delay from the transmission to arrival at the reciever antenna. Vertical axis indicates the amplitude of the received signal. plot(2)~(7) shows the signal going through each path reaching to Rx antenna. If you look at each plots, you would notice that the phase (time delay) and amplitude are different from each other. plot (8) shows the summation of all the plots from (2) through (6). This is the signal being sensed by the recieving antenna.
What do you observe from the plot (8) (the sum of all the multipath signal) ? You would notice that the amplitude and phase gets different from the transmitted signal, but overall shape of the signal is same as the transmitted signal. It means that no distortion happened by this multipath.
Now let’s think of how the received signal is influenced by the multipath with the frequency of the transmitted signal. I created plots for three different frequency (In this case, the amplitude of transmitted signal are same for all frequencies). As you see at plot (8) of each case (w=1,2,5), the amplitude of the total received signal gets different depending on frequency.
Note : This shows the cases of only three different frequencies. Isn’t there any way of showing the amplitude of total received signal for all the different frequencies in easier way ? We will look into this shortly.
Following is the Octave source code (I think this would work with Matlab as well) for the graphs shown above, try playing the list ‘a’ and ‘tau’, get some intuitive understanding on your own.
a=[0.6154 0.7919 0.9218 0.7382 0.1763 0.4057];
tau=[0.0099 0.0579 0.3529 0.4103 0.8132 0.8936];
t = 0:1/100:2;
w = 5;
N_path = length(tau);
exp_iwt_sum = zeros(1,length(t));
subplot(N_path + 2,2,[1 2]);
exp_iwt = a(d) .* exp(j .* 2 .* pi .* w .* (t .- tau(d)) );
exp_iwt_sum += exp_iwt;
subplot(N_path + 2,2,2*d+1);
plot(t,real(exp_iwt)); xlim([0 2]);ylim([-1 1]); set(gca,’xticklabel’,); set(gca,’ytick’,[-1 0 1]);
subplot(N_path + 2,2,2*d+2);
plot(t,imag(exp_iwt)); xlim([0 2]);ylim([-1 1]); set(gca,’xticklabel’,); set(gca,’ytick’,[-1 0 1]);
d = N_path + 1;
subplot(N_path + 2,2,2*d+1);
plot(t,real(exp_iwt_sum),’r-‘); xlim([0 2]);ylim([-3 3]); set(gca,’xticklabel’,); set(gca,’ytick’,[-3 0 3]);
subplot(N_path + 2,2,2*d+2);
plot(t,imag(exp_iwt_sum),’r-‘); xlim([0 2]);ylim([-3 3]); set(gca,’xticklabel’,); set(gca,’ytick’,[-3 0 3]);
a=[0.6154 0.7919 0.9218 0.7382 0.1763 0.4057];
tau=[0.0099 0.0579 0.3529 0.4103 0.8132 0.8936];
w = 0:pi/100:40*pi;
wmax = max(w);
N_path = length(tau);
Hw_sum = zeros(1,length(w));
subplot(N_path + 2,3,[1 3]);
Hw = a(d) .* exp(j * w * tau(d) );
Hw_sum += Hw;
subplot(N_path + 2,3,3*d+1);
plot(w,real(Hw)); xlim([0 wmax]);ylim([-1 1]); set(gca,’xticklabel’,); set(gca,’ytick’,[-1 0 1]);
subplot(N_path + 2,3,3*d+2);
plot(w,imag(Hw)); xlim([0 wmax]);ylim([-1 1]); set(gca,’xticklabel’,); set(gca,’ytick’,[-1 0 1]);
subplot(N_path + 2,3,3*d+3);
plot(w,abs(Hw)); xlim([0 wmax]);ylim([0 1]); set(gca,’xticklabel’,); set(gca,’ytick’,[0 1]);
d = N_path + 1;
subplot(N_path + 2,3,3*d+1);
plot(w,real(Hw_sum),’r-‘); xlim([0 wmax]);ylim([-5 5]); set(gca,’xticklabel’,); set(gca,’ytick’,[-5 0 5]);
subplot(N_path + 2,3,3*d+2);
plot(w,imag(Hw_sum),’r-‘); xlim([0 wmax]);ylim([-5 5]); set(gca,’xticklabel’,); set(gca,’ytick’,[-5 0 5]);
subplot(N_path + 2,3,3*d+3);
plot(w,abs(Hw_sum),’r-‘); xlim([0 wmax]);ylim([0 5]); set(gca,’xticklabel’,); set(gca,’ytick’,[0 5]);
Now we have a channel described in the form of impulse response. Let’s suppose that we have a signal x(t) going through the channel. The x(t) will split into multiple pathes in the channel and take the different route and finally recombine at the receiver antenna. What would the signal at the receiving antenna look like ? There is an excellent mathematical tool to give you the answer to this question. It is a tool called ‘Convolution‘. If you just take the convolution of channel impulse response and input signal (x(t)), you would take the signal at the receiving antenna.
Correlation among Channels
In addition to this delay, gain, PDF, we have to think about the correlations among all the antenna on transmitter and reciever side as follows.
These correlation is defined in 3GPP as shown below and this correlation matrix should be applied to the Fading model when we use multiple antenna configuration.
Do you see any difference between the matrix shown above (channel matrix) for 2 x 2 MIMO and the correlation matrix as shown below for 2 x 2 case ?
One obvious difference is size of the matrix. The channel matrix shown above is 2×2 matrix and the correlation matrix for 2 x 2 case shown below is 4×4.
Any idea on how the 2×2 matrix show above is converted to 4 x 4 matrix shown below ?
This is a major topic for following section.
(The full correlation matrices is obtained by Tensor Product/Kronecker product of two antenna correlation matrix)
How these correlation are derived ?
Let’s start with the simplest case and add piece by piece until we reach more realistic (complex) cases.
Let’s suppose that you have two Tx antenna and one Rx antenna. If draw this situation as in channel model, you can draw this setup as follows. (Let’s call this a Case A)
In this setup, you have two channel path(channel coefficient) h1 and h2. Now let’s think about all the possible relation between these two channel. If you draw all the possible relationships as a diagram, you may represent as follows.
If you represent these relationship between the two channel path (channel coefficient) in a matrix format, you can represent it as follows. (I would not explain it in detail but you would need to have some intuitive understanding of vector/matrix. This is why you should have taken such a boring linear algebra course in the university. If you are really new to this vector/matrix concept, Vector/Matrix page on this site). Be careful, you may get confused with 2 x 2 matrix to be used to model 2 x 2 MIMO channel. (At least I was confused a lot at the beginning. I would not talk too much about it, but I strongly recommend you to compare the two matrix and clearly understand the difference between them). Just to give you the conclusion about the difference between the two matrix, the matrix shown here represents the correlation between each channel path(the path the signal flow through) and the matrix shown in channel model represents the correlation between Tx and Rx antenna.
The ‘Beta’ value represents the cross correlation between channel coefficient h1 and h2.
Now we have a different case. In this case, you have two Tx antenna and one Rx antenna. If draw this situation as in channel model, you can draw this setup as follows. (Let’s call this a Case B)
If you represent these relationship between the two channel path (channel coefficient) in a diagram, you can represent it as follows.
If you represent these relationship between the two channel path (channel coefficient) in a matrix format,, you can represent it as follows.The ‘Alpha’ value represents the cross correlation between channel coefficient h1 and h2.
Now let’s look into more complicated case. In this case, you have two transmitter antenna and two reciever antenna. The antenna and channel path can be illustrated as follows. You can think of this as a combination of Case A and Case B described above.
Now if you represents the correlations between each of these channel coefficients, it can be represented as follows.
As you did in case A and B, if you represents these correlations in vector/matrix format, you would get a big matrix as follows. How did you get the last matrix ?. Actually it came from the matrix for Case A and B described above. If you clearly understood the meaning of Beta and Alpha in Case A and B, you would easily understand how each of the elements in this big matrix can be represented as ‘Alpha’ or ‘Beta’.
Then what about the ‘?’ part ?
what about the ‘?’ part shown above ? Filling out these ‘?’ is not so straigtforward. If you brow the statical concept (I cannot explain though :)), those parts are the places where the correlation between part of Case A and part of Case B and can be filled as follows.
How to derive MIMO coeifficient matrix mathematically from Diversity coefficient matrix ?
I have explained how we got 2×2 MIMO correlation matrix from physical perspective and the each elements of 2 x 2 MIMO matrix can be derived from the elements of 2×1 Diversity matrix. I strongly recommend you to try to get familiar with this concept and build your own intuition. But as the number of antenna increases, deriving all the correlation matrix as I described above would not be that easy. Fortunately there is a mathematical way of deriving the MIMO correlation matrix from Diversity Correlation matrix. The MIMO correlation matrix can be derived by taking Kronecker product of two Diversity matrix as shown below. (If you are not familar with the concept of Kronecker product, refer to this page)
Going Back to 3GPP Correlation Tables
< 36.101 Table B.2.3.1-1 eNodeB correlation matrix >
< 36.101 Table B.2.3.1-2 UE correlation matrix >
The last component I want to show you about Fading is Doppler spread. Doppler spread is caused by the well known phenomenon called ‘Doppler Shift’ (I would not explain what Doppler shift is. Everybody would learned about this from High school physics and you can easily google out a lot of explanation of this effect).
Doppler spread can be expressed as shown in following function and plot. If you think of ‘Doppler spread’ as a kind of filter (or a block in a control system model). You can take the following function as a kind of transfer function of the filter.
As you see, how much a frequency (fc) get spreaded by doppler spread is indicated by fm. If you see another formula below the graph representing fm, you would see fm is determined by the velocity of object (relative velocify between transmitter and reciever) and speed of light and fc(carrier frequency). You know fc is constant (set by standard and assume hardware is good enough to maintain the fc the same). Now the only variable is the speed v. According to this equation, fm and v are in proportional relation, meaning if v gets higher, fm gets larger (the width of the graph gets wider) and if v gets smaller, fm gets smaller (the width of the graph gets narrower).
Now you may have question. How differently the narrow Doppler spread and wide doppler spread affect on the signal ?
The simulation result for these two cases (narrow and wide doppler spread) is shown below. The upper track is for narrow doppler spread and the lower track is for wide doppler spread. As you see, you see much frequency energy fluctuation over time in wide Doppler spectrum. I will update the page later about the mathmatical background.
General Concept of Fading Channel Simulation
Typical model for Fading channel can be described by a simple diagram as follows. This is very simplified model but fortunately this is almost enough for 3GPP specification for fading test. As you see, a signal comes into the fading block and splits into multiple path. Each path has three components, namely Delay, PDF, Gain component. By changing these parameters in each path, you can construct pretty complicated fading channel.
In LTE 3GPP specification, typical three types of Fading profiles are defined as follows.
Fading Channel Model Examples
Go through following pages (examples) and try to get some intuitive understanding on your own.
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NOTES TO EDITORS T-Mobile selects Ericsson for billing solution and new customer experience: http://www.ericsson.com/thecompany/press/releases/2014/06/1789370 Ericsson maintains leadership in the Magic Quadrant for LTE Infrastructure 2014: http://www.ericsson.com/thecompany/press/releases/2014/05/1785211 Download high-resolution photos and broadcast-quality video at http://www.ericsson.com/press Ericsson is the driving force behind the Networked Society – a world leader in communications technology and services. Our long-term relationships with every major telecom operator in the world allow people, business and society to fulfill their potential and create a more sustainable future.
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