We have now released the 15th episode of the podcast Wireless Future, with the following abstract:
Machine learning builds on the collection and processing of data. Since the data often are collected by mobile phones or internet-of-things devices, they must be transferred wirelessly to enable machine learning. In this episode, Emil Björnson and Erik G. Larsson are visited by Carlo Fischione, a Professor at the KTH Royal Institute of Technology. The conversation circles around distributed machine learning and how the wireless technology can evolve to support learning applications via network slicing, information-aware communication, and over-the-air computation. To learn more, they recommend the article “Wireless for Machine Learning”. Please visit Carlo’s website and the Machine Learning for Communications ETI website.
You can watch the video podcast on YouTube:
You can listen to the audio-only podcast at the following places:
We have now released the 14th episode of the podcast Wireless Future, with the following abstract:
In this episode, Emil Björnson and Erik G. Larsson answer questions from the listeners on the topics of distributed MIMO, THz communications, and non-orthogonal multiple access (NOMA). Some examples are: Is cell-free massive MIMO really a game-changer? What would be its first use case? Can visible light communications be used to reach 1 terabit/s? Will Massive MIMO have a role to play in THz communications? What kind of synchronization and power constraints appear in NOMA systems? Please continue asking questions and we might answer them in later episodes!
You can watch the video podcast on YouTube:
You can listen to the audio-only podcast at the following places:
One of the physical-layer technologies that have received a lot of attention from the research community in recent years is called Non-Orthogonal Multiple Access (NOMA). For instance, it has been called “A Paradigm Shift for Multiple Access for 5G and Beyond“.
In NOMA, the users are assigned to the same time-frequency resource and instead separated in the power or code domains.
The core idea of NOMA is to assign the same time-frequency resource to multiple users, and instead (partially) separate the users in the power or code domain. This is illustrated in the figure and stands in contrast to the classic approach of assigning orthogonal resources to the users, which was done in 4G using Orthogonal Frequency-Division Multiple Access (OFDMA) and in 3G using orthogonal spreading codes. The benefit of the non-orthogonality is that the sum spectral efficiency (bit/s/Hz/cell) can be increased, if the increased interference can be dealt with using clever signal processing, such as successive interference cancelation. But from a practical standpoint, it matters a lot if the performance gain is 1% or 1000% (~10x). The former is negligible while the latter would constitute a paradigm shift.
Massive MIMO is also based on non-orthogonal access
The use of many antennas has become a natural part of 5G. When having an antenna array, the users can be spatially multiplexed, instead of assigned to orthogonal time-frequency resources. This is what we call Massive MIMO and it is a non-orthogonal multiple access scheme; if you direct a spatial beam towards each user, there will be interference leakage between the beams. MIMO schemes have been around for decades, for example, under the name spatial division multiple access (SDMA). There is both experimental and theoretical evidence that the widespread support for Massive MIMO in 5G is a paradigm shift when it comes to spectral efficiency, but nevertheless, it is not what most papers refer to when using the NOMA terminology.
Instead, the NOMA literature focuses on another aspect of the non-orthogonality: joint decoding of the interfering signals. It is known in information theory that weakly interfering signals should be treated as noise, while strongly interfering signals should be decoded jointly with the desired signal (or using successive interference cancelation). Hence, the methods considered in the NOMA literature are mainly effective in systems with strongly interfering signals.
Since Massive MIMO is used in 5G from the beginning, while NOMA remains to be standardized, a natural question arises:
Do we need other non-orthogonal access schemes than Massive MIMO in 5G?
One of the key motivating factors for Massive MIMO is the favorable propagation, which basically means that the base station has sufficiently many antennas to beamform so that the users’ channels become nearly orthogonal. One can think of it as transmitting narrow beams that lead to low interference leakage. Under these conditions, there are no strongly interfering signals, which implies that no additional NOMA features are needed to deal with the interference. We have shown this analytically in two papers: one about power-domain NOMA and a new one about code-domain NOMA.
Although these papers show that NOMA can usually not improve the sum spectral efficiency, there are indeed some special cases when it can. In particular, this happens in situations when the number of antennas is insufficient to achieve favorable propagation. This can, for example‚ happen in line-of-sight scenarios where the users are closely spaced and therefore have very similar channels. However, in my experience, the NOMA gains are marginal also in these cases. When writing the two papers mentioned above, we had to spend much time on parameter tuning to find the cases where NOMA could provide meaningful improvements. With this in mind, it is fully plausible that NOMA will never be used in 5G, at least not for increasing the spectral efficiency (it could be useful for other purposes, such as grant-free access).
What about beyond 5G systems?
When it became clear that NOMA wouldn’t play any big role in 5G, the research focus has shifted towards beyond 5G systems. One of the prominent new advances on non-orthogonal access is called rate splitting. The recent paper “Is NOMA Efficient in Multi-Antenna Networks?” provides a pedagogical overview. The paper also makes a case for that rate splitting methods combines the best aspects of conventional NOMA and Massive MIMO, in a way that guarantees a higher sum spectral efficiency. While it is true that a well-designed rate splitting system can never be worse than conventional Massive MIMO with linear processing, the key question is: how large performance gains can be achieved?
In the overview paper, the case for rate splitting is based on multiplexing gain analysis. This means that the sum spectral efficiency (bit/s/Hz) is studied when the transmit power P is asymptotically large. Different access schemes will achieve different spectral efficiencies, but they all behave as M log2(P)+C, where the factor M is the multiplexing gain and C is a constant. When P is large, the scheme that achieves the largest multiplexing gain is guaranteed to give the largest spectral efficiency, irrespective of the value of C.
If the channels are known perfectly, then a single-cell Massive MIMO system achieves the maximum multiplexing gain (it is equal to the minimum of the total number of transmit antennas and the total number of receive antennas). However, if the channels are known imperfectly, then the multiplexing gain is reduced when using linear processing and the above-mentioned paper shows that the rate splitting approach added to achieve a larger multiplexing gain than conventional Massive MIMO. This is mathematically correct, but there is one catch: the power used for channel estimation is assumed to grow more slowly than the power P used for data transmission. However, in practice, we could use the same power for both estimation and data transmission; hence, in the large-P regime considered in the multiplexing gain analysis, we will have perfect channel knowledge. Rate splitting cannot increase the multiplexing gain in that case.
That said, rate splitting can still improve the sum spectral efficiency compared to Massive MIMO in practical setups (at least it cannot be worse), but we should not expect any paradigm shift. Massive MIMO is already utilizing the multiplexing gain to push the spectral efficiency to new heights in 5G. Further improvements are possible by increasing the number of antennas, while it cannot be achieved by refining the access scheme. That could only increase the parameter C, not M.
If you want to learn more about NOMA and rate splitting, I recommend the following episode of our podcast:
We have now released the 13th episode of the podcast Wireless Future, with the following abstract:
Wireless devices normally connect to a single access point, deployed at one location. The access points are deployed sparsely to create large cell regions, each controlled by the nearest access point. This architecture was conceived for mobile telephony and has been inherited by today’s networks, even if those mainly transfer wireless data. However, future wireless networks might be organized entirely differently. In this episode, Erik G. Larsson and Emil Björnson discuss how one can create cell-free networks consisting of distributed massive MIMO arrays. The vision is that each user will be surrounded by small access points that cooperate to provide uniformly high service quality. The conversation covers the key benefits, how the network architecture can be evolved to support the new technology, and what the main research challenges are. To learn more, they recommend the article “Ubiquitous Cell-Free Massive MIMO Communications” and the new book “Foundations of User-Centric Cell-Free Massive MIMO”.
You can watch the video podcast on YouTube:
You can listen to the audio-only podcast at the following places:
Mobile networks are divided into semi-autonomous cells. It is essentially a divide-and-conquer approach to network operation, where each cell becomes simple to operate and the reuse of radio resources over the cells can be planned in advance. This network structure was proposed already in the 1950s and has been vital for the wide-spread adoption of mobile network technology. However, the weaknesses of the cellular architecture have become increasingly apparent as mobile data has replaced voice calls as the main type of traffic. While the peak data rates are high in contemporary networks, the user-guaranteed rates are very modest, due to the largest pathloss variations and inter-cell interference that is inherent in the cellular architecture.
A promising solution to these issues is to leave the cellular paradigm behind and create a new network architecture that is free from cells. This vision is called Cell-free Massive MIMO.
This is a technology that essentially combines three main components that have been previously considered separately: 1) the efficient physical-layer operation with many antennas that enabled wide-spread adoption of Massive MIMO in cellular networks; 2) the vision of deploying many access points close to the users, to create a reality where users are surrounded by access points instead of the opposite; 3) the joint transmission and reception from distributed access points, that have been analyzed under many names over the last two decades, including coordinated multipoint (CoMP).
This blog post is about the first book on the topic: “Foundations of User-Centric Cell-Free Massive MIMO” by Özlem Tuğfe Demir, Emil Björnson, and Luca Sanguinetti. We provide the historical background, theoretical foundations, and state-of-the-art signal processing algorithms. The book is 300 pages long and is accompanied by a GitHub repository with all the simulation code. We hope that this book will serve as the starting point for much further research. The last section of the book outlines many future research directions.
NOW publishers is offering a free PDF until April 2, 2021. To obtain it, go to the book’s website, create a free account, and then click on download. For the same period, they are offering printed copies for the special prize of $40 (including non-trackable shipping). To purchase the printed version, go to the secure Order Form and use the Promotion Code 584793.
Since 5G is designed to be future-proof and enable decoupling of the control signaling and data transmissions, I believe that the 5G networks will become increasingly cell-free during this decade, while beyond 5G networks will embrace the cell-free architecture from the outset.
We have now released the twelfth episode of the podcast Wireless Future, with the following abstract:
The data that flows through wireless networks are protected by encryption, but there are anyway privacy and security issues inherent in wireless technologies. In this episode, Erik G. Larsson and Emil Björnson are visited by Panos Papadimitratos, a Professor at the KTH Royal Institute of Technology. The conversation focuses on location privacy and spoofing; what the practical issues are, what countermeasures exist, and which tradeoffs must be made when building wireless technologies.
You can watch the video podcast on YouTube:
You can listen to the audio-only podcast at the following places:
Consider the following simulation example, where the respective path losses are indicated:
Case 1 represents a weak direct path and Case 2 represents a direct path that is of the same strength as the paths to/from the RIS. When adding more and more elements in the RIS, the spectral efficiency behaves as follows (“baseline” is the case without RIS):
The RIS can improve the performance by a lot in Case 1 (weak direct channel), while the improvements are mediocre in Case 2 (stronger direct channel). (You can download the simulation code here.) Hence, we should utilize an RIS to improve the SNR when the channel quality is otherwise low. This might not be so surprising but it means that we must deploy the RIS strategically to get good channels both to and from it.
Criterion 2: Line-of-sight channels to and from the RIS
A more subtle point is that we should deploy the RIS to have line-of-sight channels to the transmitter and the receiver. There are three main reasons for this:
Criterion 1 is likely to be satisfied, at least if the direct path is non-line-of-sight.
The RIS can be used over wideband channels (e.g., tens of MHz) since most of the energy comes from one angular direction and should be delivered in one angular direction.
The channel estimation can be vastly simplified by exploiting angular sparsity.
The following paper contains recent experimental results that demonstrate the feasibility of the RIS technology: “RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials“. The paper describes outdoor field trials over 50 and 500 meters in scenarios satisfying the two conditions above. The paper proposes an algorithm for RIS configuration that is explicitly utilizing the angular channel sparsity and provided large SNR improvements.
I talk more about these measurements and underlying theory in the following video (which is also based on my new tutorial article):
