Category Archives: 5G

Chasing Data Rate Records

5G networks are supposed to be fast, to provide higher data rates than ever before. While indoor experiments have demonstrated huge data rates in the past, this has been the year where the vendors are competing in setting new data rate records in real deployments.

Nokia achieved 4.7 Gbps in an unnamed carrier’s cellular network in the USA in May 2020. This was achieved by dual connectivity where a user device simultaneously used 800 MHz of mmWave spectrum in 5G and 40 MHz of 4G spectrum.

The data rate with the Nokia equipment was higher than the 4.3 Gbps that Ericsson demonstrated in February 2020, but they “only” used 800 MHz of mmWave spectrum. While there are no details on how the 4.7 Gbps was divided between the mmWave and LTE bands, it is likely that Ericsson and Nokia achieved roughly the same data rate over the mmWave bands. The main new aspect was rather the dual connectivity between 4G and 5G.

The high data rates in these experiments are enabled by the abundant spectrum, while the spectral efficiency is only 5.4 bps/Hz. This can be achieved by 64-QAM modulation and high-rate channel coding, a combination of modulation and coding that was available already in LTE. From a technology standpoint, I am more impressed by reports of 3.7 Gbps being achieved over only 100 MHz of bandwidth, because then the spectral efficiency is 37 bps/Hz. That can be achieved in conventional sub-6 GHz bands which have better coverage and, thus, a more consistent 5G service quality.

How “Massive” are the Current Massive MIMO Base Stations?

I have written earlier that the Massive MIMO base stations that have been deployed by Sprint, and other operators, are very capable from a hardware perspective. They are equipped with 64 fully digital antennas, have a rather compact form factor, and can handle wide bandwidths in the 2-3 GHz bands. These facts are supported by documentation that can be accessed in the FCC databases.

However, we can only guess what is going on under the hood – what kind of signal processing algorithms have been implemented and how they perform compared to ideal cases described in the academic literature. Erik G. Larsson recently wrote about how Nokia improved its base station equipment via a software upgrade. Are the latest base stations now as “Massive MIMO”-like as they can become?

My guess is that there is still room for substantial improvements. The following joint video from Sprint and Nokia explains how their latest base stations are running 4G and 5G simultaneously on the same 64-antenna base station and are able to multiplex 16 layers.

This is the highest number of multiuser MIMO layers achieved in the US” according to the speaker. But if you listen carefully, they are actually sending 8 layers on 4G and 8 layers 5G. That doesn’t sum up to 16 layers! The things called layers in 3GPP are signals that are transmitted simultaneously in the same band, but with different spatial directivity. In every part of the spectrum, there are only 8 spatially multiplexed layers in the setup considered in the video.

It is indeed impressive that Sprint can simultaneously deliver around 670 Mbit/s per user to 4 users in the cell, according to the video. However, the spectral efficiency per cell is “only” 22.5 bit/s/Hz, which can be compared to the 33 bit/s/Hz that was achieved in real-world trials by Optus and Huawei in 2017.

Both numbers are far from the world record in spectral efficiency of 145.6 bit/s/Hz that was achieved in a lab environment in Bristol, in a collaboration between the universities in Bristol and Lund. Although we cannot expect to reach those numbers in real-world urban deployments, I believe we can reach higher numbers by building 64-antenna arrays with a different form factor: long linear arrays instead of compact square panels. Since most users are separable in terms of having different azimuth angles to the base station, it will be easier to separate them by sending “narrower” beams in the horizontal domain.

Record 5G capacity via software upgrade!

In the news: Nokia delivers record 5G capacity gains through a software upgrade.   No surprise!  We expected, years ago, this would happen.

What does this software upgrade consist of?  I can only speculate.  It is, in all likelihood, more than the usual (and endless) operating system bugfixes we habitually think of as “software upgrades”.   Could it be even something that goes to the core of what massive MIMO is?  Replacing eigen-beamforming with true reciprocity-based beamforming?! Who knows. Replacing maximum-ratio processing with zero-forcing combining?!  Or even more mind-boggling, implementing more sophisticated processing of the sort that has been stuffing the academic journals in the last years? We don’t know!  But it will certainly be interesting to find out at some point, and it seems safe to assume that this race will continue.  

A lot of improvement could be achieved over the baseline canonical massive MIMO processing. One could, for example, exploit fading correlation, develop improved power control algorithms or implement algorithms that learn the propagation environment, autonomously adapt, and predict the channels.  

It might seem that research already squeezed every drop out of the physical layer, but I do not think so.  Huge gains likely remain to be harvested when resources are tight, and especially we are limited by coherence: high carriers means short coherence, and high mobility might mean almost no coherence at all.  When the system is starved of coherence, then even winning a couple of samples on the pilot channel means a lot.  Room for new elegant theory in “closed form”?  Good question. Could sound heartbreaking, but maybe we have to give up on that.  Room for useful algorithms and innovation? Certainly yes.  A lot.  The race will continue.

Beyond the Cellular Paradigm: Cell-Free Architectures with Radio Stripes

I just finished giving an IEEE Future Networks Webinar on the topic of Cell-free Massive MIMO and radio stripes. The webinar is more technical than my previous popular-science video on the topic, but it can anyway be considered an overview on the basics and the implementation of the technology using radio stripes.

If you missed the chance to view the webinar live, you can access the recording and slides afterwards by following this link. The recording contains 42 minutes of presentation and 18 minutes of Q/A session. If your question was not answered during the session, please feel to ask it here on the blog instead.

Update: The recording from the webinar has been delayed (due to the virus crisis), so I have recorded an alternative video:

Two Roles of Deep Learning in Massive MIMO

The hype around machine learning, particularly deep learning, has spread over the world. It is not only affecting engineers but also philosophers and government agencies, which try to predict what implications machine learning will have on our society.

When the initial excitement has subsided, I think machine learning will be an important tool that many engineers will find useful, alongside more classical tools such as optimization theory and Fourier analysis. I have spent the last two years thinking about what role deep learning can have in the field of communications. This field is rather different from other areas where deep learning has been successful: We deal with man-made systems that have designed based on rigorous theory to operate close to the fundamental performance limits, for example, the Shannon capacity. Hence, at first sight, there seems to be little room for improvement.

I have nevertheless identified two main applications of supervised deep learning in the physical layer of communication systems: 1) algorithmic approximation and 2) function inversion.

You can read about them in my recent survey paper “Two Applications of Deep Learning in the Physical Layer of Communication Systems” or watch the following video:

In the video, I’m exemplifying the applications through two recent papers where we applied deep learning to improve Massive MIMO systems. Here are links to those papers:

Trinh Van Chien, Emil Björnson, Erik G. Larsson, “Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning,” IEEE International Conference on Communications (ICC), 2019.

Özlem Tugfe Demir, Emil Björnson, “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” IEEE Open Journal of the Communications Society, 2020.

Revitalizing the Research on Wireless Communications in a New Decade

In the last decade, the research on wireless communications has been strongly focused on the development of 5G. Plenty of papers have started with sentences of the kind: “We consider X, which is a promising method that can greatly improve Y in 5G.” For example, X might be Massive MIMO and Y might be the spectral efficiency. We now know which physical-layer methods made it into the first release of the 5G standard, and which did not. It remains to be seen which methods will actually be used in practice and how large performance improvements 5G can deliver.

There is no doubt that the 5G research has been successful. However, it remains is to improve the developed methods to bridge the gap between the simplifying models and assumptions considered in academia and the practical conditions faced by the industry. Although new scientific papers appear on almost every day, few of them focus on these practically important aspects of the 5G development. Instead, minor variations on well-studied problems dominate and the models are the same simplified ones as ten years ago. We seem to be stuck in doing the same things that led to important advances at the beginning of the 2010s, although we have already solved most problems that can be solved using such simple models. This is why I think we need to revitalize the research!

Two concrete examples

The following two examples explain what I mean.

Example 1: Why would we need more papers on Massive MIMO with uncorrelated Rayleigh fading channels and maximum ratio (MR) processing? We already know that practical channels are spatially correlated and other processing methods are vastly superior to MR while having practically affordable complexity.

Example 2: Why would we need more papers on hybrid beamforming design for flat-fading channels? We already know that the hybrid architecture is only meaningful in wideband mmWave communications, in which case the channels feature frequency-selective fading. The generalization is non-trivial since it is mainly under frequency-selective conditions that the weaknesses/challenges of the hybrid approach appear.

I think that the above-mentioned simplifications were well motivated in the early 2010s when many of the seminal papers on Massive MIMO and mmWave communications appeared. It is usually easier to reach ambitious research goals by taking small steps towards them. It is acceptable to make strong simplifications in the first steps, to achieve the analytical tractability needed to develop a basic intuition and understanding. The later steps should, however, gradually move toward more realistic assumptions that also makes the analysis less tractable. We must continuously question if the initial insights apply also under more practical conditions or if they were artifacts of the initial simplifications.

Unfortunately, this happened far too seldom in the last decade. Our research community tends to prioritize analytical tractability over realistic models. If a model has been used in prior work, it can often be reused in new papers without being questioned by the reviewers. When I review a paper and question the system model, the authors usually respond with a list of previous papers that use the same model, rather than the theoretical motivation that I would like to see.

It seems to be far easier to publish papers with simple models that enable derivation of analytical “closed-form” expressions and development of “optimal” algorithms, than to tackle more realistic but challenging models where these things cannot be established. The two examples above are symptoms of this problem. We cannot continue in this way if we want to keep the research relevant in this new decade. Massive MIMO and mmWave communications will soon be mainstream technologies!

Entering a new decade

The start of the 2020s is a good time for the research community to start over and think big. Massive MIMO was proposed in a paper from 2010 and initially seemed too good to be true, possibly due to the simplicity of the models used in the early works. In a paper that appeared in 2015, we identified ten “myths” that had flourished when people with a negative attitude against the technology tried to pinpoint why it wouldn’t work in practice. Today – a decade after its inception – Massive MIMO is a key 5G technology and has even become a marketing term used by cellular operators. The US operator Sprint has reported that the first generation of Massive MIMO base stations improve the spectral efficiency by around 10x in their real networks.

I believe the history will repeat itself during this decade. The research into the next big physical layer technology will take off this year – we just don’t know what it will be. There are already plenty of non-technical papers that try to make predictions, so the quota for such papers is already filled. I’ve written one myself entitled “Massive MIMO is a Reality – What is Next? Five Promising Research Directions for Antenna Arrays”. What we need now are visionary technical papers (like the seminal Massive MIMO paper by Marzetta) that demonstrate mathematically how a new technology can achieve ten-fold performance improvements over the state-of-the-art, for example, in terms of spectral efficiency, reliability, latency, or some other relevant metric. Maybe one or two of the research directions listed in my paper will be at the center of 6G. Much research work remains before we can know, thus this is the right time to explore a wide range of new ideas.

The start of a new decade is a good time for the research community to start over and to think big. Massive MIMO was proposed in a paper from 2010 and initially seemed too good to be true, possibly due to the simplicity of the system models used in the early works. In a paper that appeared in 2015, we identified ten “myths” that had flourished when people tried to pinpoint why Massive MIMO wouldn’t work in practice. Today – a decade after its inception – Massive MIMO is a key 5G technology that has even become a marketing term used by cellular operators. It has been shown to improve the spectral efficiency by 10x in real networks.

I believe that the same procedure will repeat itself during this decade. The research into the next big physical layer technology will take off this year – we just don’t know what it will be. There are already plenty of non-technical papers that try to make predictions, so that quota has already been filled. I’ve written one myself entitled “Massive MIMO is a Reality – What is Next? Five Promising Research Directions for Antenna Arrays”. However, what we really need is visionary technical papers (like the seminal Massive MIMO paper by Marzetta) that demonstrate how we can actually achieve, say, ten-fold performance improvements over the state-of-the-art, concerning spectral efficiency, reliability, latency, or some other relevant metric. Maybe one or two of the research directions listed in my paper will become the main thing in 6G – much further work is needed before we can know.

Five ways to revitalize the research

To keep the wireless communication research relevant, we should stop considering minor variations on previously solved problems and instead focus either on implementation aspects of 5G or on basic research into entirely new methods that might eventually play a role in 6G. In both cases, I have the following five recommendations for how we can conduct more efficient and relevant research in this new decades.

1. We may start the research on new topics by using simplified models that are analytically tractable, but we must not get stuck in using those models. A beautiful analysis obtained with an unrealistic model might cause more confusion than it generates new practical insights. Just remember how a lot of Massive MIMO research focused on the pilot contamination problem, just because it happened to be the asymptotically limiting factor when using simplified models, while it is not the case in general.

2. We must be more respectful towards the underlying physics, particularly, electromagnetic theory. We cannot continue normalizing the pathloss variables or guesstimate how they can be computed. When developing a new technology, we must first get the basic models right. Otherwise we risk making fundamental mistakes and – even worse – trick others into repeating those mistakes for years to come. I covered the danger of normalization in a previous blog post.

3. We must not forget about previous methods when evaluating new methods but think carefully about what the true state-of-the-art is. For example, if we want to improve the performance of a cellular network by adding new equipment, we must compare it to existing equipment that could alternatively been added. For example, I covered the importance of comparing intelligent reflecting surfaces with relays in a previous blog post.

4. We must make sure new algorithms are reproducible and easily comparable, so that every paper is making useful progress. This can be achieved by publishing simulation code alongside papers and evaluating new algorithms in the same setup as previous algorithms. We might take inspiration from the machine learning field where ImageNet is a common benchmark.

5. We must not take the correctness of models and results in published papers for granted. This is particularly important nowadays when new IEEE journals with very short review times are getting traction. Few scientific experts can promise to make a proper review of a full-length paper in just seven days; thus, many reviews will be substandard. This is a step in the wrong direction and can severely reduce the quality and trustworthiness of published papers.

Let us all make an effort to revitalize the research methodology and selection of research problems to solve in the 2020s. If you have further ideas please share them in the comment field!

Is the Pathloss Larger at mmWave Frequencies?

The range of mmWave communication signals is often said to be lower than for signals in the conventional sub-6 GHz bands. This is usually also the case but the reason for it might not be the one that you think. I will explain what I mean in this blog post.

If one takes a look at the classical free-space pathloss formula, the received power P_r is

(1)   \begin{equation*}P_r = P_t \left( \frac{\lambda}{4\pi d} \right)^2,\end{equation*}

where the transmit power is denoted by P_t, the wavelength is \lambda, and the propagation distance is d. This formula shows that the received power is proportional to the wavelength and, thus, will be smaller when we increase the carrier frequency; that is, the received power is lower at 60 GHz (\lambda=5 mm) than at 3 GHz (\lambda=10 cm). But there is an important catch: the dependence on \lambda is due to the underlying assumption of having a receive antenna with the effective area

(2)   \begin{equation*}A = \frac{\lambda^2}{4\pi}.\end{equation*}

Hence, if we consider a receive antenna with arbitrary effective area A, we can instead write the received signal in (1) as

(3)   \begin{equation*}P_r = P_t  \frac{A}{4\pi d^2},\end{equation*}

which is frequency-independent as long as we keep the antenna area A fixed as we change the carrier frequency. Since the area of a fixed-gain antenna actually is proportional to \lambda^2, as exemplified in (2), in practice we will need to use arrays of multiple antennas in mmWave bands to achieve the same total antenna area A as in lower bands. This is what is normally done in mmWave communications for cellular networks, while a single high-gain antenna with large area can be used for fixed links (e.g., backhaul between base stations or between a satellite and ground station). As explained in Section 7.5 of Massive MIMO Networks, one can actually play with the antenna areas at both the transmitter and receiver to keep the same pathloss in the mmWave bands, while actually reducing the total antenna area!

So why is the signal range shorter in mmWave bands?

The main reasons for the shorter range are:

  • Larger propagation losses in non-line-of-sight scenarios, for example, due to less scattering (fewer propagation paths) and larger penetration losses.
  • The use more bandwidth, which leads to lower SNR.