Six Differences Between MU-MIMO and Massive MIMO

Multi-user MIMO (MU-MIMO) is not a new technology, but the basic concept of using multi-antenna base stations (BSs) to serve a multitude of users has been around since the late 1980s.

An example of how MU-MIMO was illustrated prior to Massive MIMO.

I sometimes get the question “Isn’t Massive MIMO just MU-MIMO with more antennas?” My answer is no, because the key benefit of Massive MIMO over conventional MU-MIMO is not only about the number of antennas. Marzetta’s Massive MIMO concept is the way to deliver the theoretical gains of MU-MIMO under practical circumstances. To achieve this goal, we need to acquire accurate channel state information, which in general can only be done by exploiting uplink pilots and channel reciprocity in TDD mode. Thanks to the channel hardening and favorable propagation phenomena, one can also simplify the system operation in Massive MIMO.

This is how Massive MIMO is often illustrated for line-of-sight operation.

Six key differences between conventional MU-MIMO and Massive MIMO are provided below.

Conventional MU-MIMO Massive MIMO
Relation between number of BS antennas (M) and users (K) MK and both are small (e.g., below 10) K and both can be large (e.g., M=100 and K=20).
Duplexing mode Designed to work with both TDD and FDD operation Designed for TDD operation to exploit channel reciprocity
Channel acquisition Mainly based on codebooks with set of predefined angular beams Based on sending uplink pilots and exploiting channel reciprocity
Link quality after precoding/combining Varies over time and frequency, due to frequency-selective and small-scale fading Almost no variations over time and frequency, thanks to channel hardening
Resource allocation The allocation must change rapidly to account for channel quality variations The allocation can be planned in advance since the channel quality varies slowly
Cell-edge performance Only good if the BSs cooperate Cell-edge SNR increases proportionally to the number of antennas, without causing more inter-cell interference

Footnote: TDD stands for time-division duplex and FDD stands for frequency-division duplex.

Some Impactful Rejected Papers

Yes, my group had its share of rejected papers as well. Here are some that I specially remember:

  1. Massive MIMO: 10 myths and one critical question. The first version was rejected by the IEEE Signal Processing Magazine. The main comment was that nobody would think that the points that we had phrased as myths were true. But in reality, each one of the myths was based on an actual misconception heard in public discussions! The paper was eventually published in the IEEE Communications Magazine instead in 2016, and has been cited more than 180 times.
  2. Massive MIMO with 1-bit ADCs. This paper was rejected by the IEEE Transactions on Wireless Communications. By no means a perfect paper… but the review comments were mostly nonsensical. The editor stated: “The concept as such is straightforward and the conceptual novelty of the manuscript is in that sense limited.” The other authors left my group shortly after the paper was written. I did not predict the hype on 1-bit ADCs for MIMO that would ensue (and this happened despite the fact that yes, the concept as such is straightforward and its conceptual novelty is rather limited!). Hence I didn’t prioritize a rewrite and resubmission. The paper was never published, but we put the rejected manuscript on arXiv in 2014, and it has been cited 80 times.
  3. Finally, a paper that was almost rejected upon its initial submission: Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems, eventually published in the IEEE Transactions on Communications in 2013. The review comments included obvious nonsense, such as “Overall, there is not much difference in theory compared to what was studied in the area of MIMO for the last ten years.” The paper subsequently won the IEEE ComSoc Stephen O. Rice Prize, and has more than 1300 citations.

There are several lessons to learn here. First, that peer review may be the best system we know, but it isn’t perfect: disturbingly, it is often affected by incompetence and bias. Second, notwithstanding the first, that many paper rejections are probably also grounded in genuine misunderstandings: writing well takes a lot of experience, and a lot of hard, dedicated work. Finally, and perhaps most significantly, that persistence is really an essential component of success.

What is the Difference Between Beamforming and Precoding?

I’ve got an email with this question last week. There is not one but many possible answers to this question, so I figured that I write a blog post about it.

One answer is that beamforming and precoding are two words for exactly the same thing, namely to use an antenna array to transmit one or multiple spatially directive signals.

Another answer is that beamforming can be divided into two categories: analog and digital beamforming. In the former category, the same signal is fed to each antenna and then analog phase-shifters are used to steer the signal emitted by the array. This is what a phased array would do. In the latter category, different signals are designed for each antenna in the digital domain. This allows for greater flexibility since one can assign different powers and phases to different antennas and also to different parts of the frequency bands (e.g., subcarriers). This makes digital beamforming particularly desirable for spatial multiplexing, where we want to transmit a superposition of signals, each with a separate directivity. It is also beneficial when having a wide bandwidth because with fixed phases the signal will get a different directivity in different parts of the band. The second answer to the question is that precoding is equivalent to digital beamforming. Some people only mean analog beamforming when they say beamforming, while others use the terminology for both categories.

Analog beamforming uses phase-shifters to send the same signal from multiple antennas but with different phases. Digital beamforming designs different signals for each antennas in the digital baseband. Precoding is sometimes said to be equivalent to digital beamforming.

A third answer is that beamforming refers to a single-user transmission with one data stream, such that the transmitted signal consists of one main-lobe and some undesired side-lobes. In contrast, precoding refers to the superposition of multiple beams for spatial multiplexing of several data streams.

A fourth answer is that beamforming refers to the formation of a beam in a particular angular direction, while precoding refers to any type of transmission from an antenna array. This definition essentially limits the use of beamforming to line-of-sight (LoS) communications, because when transmitting to a non-line-of-sight (NLoS) user, the transmitted signal might not have a clear angular directivity. The emitted signal is instead matched to the multipath propagation so that the multipath components that reach the user add constructively.

A fifth answer is that precoding consists of two parts: choosing the directivity (beamforming) and choosing the transmit power (power allocation).

I used to use the word beamforming in its widest meaning (i.e., the first answer), as can be seen in my first book on the topic. However, I have since noticed that some people have a more narrow or specific interpretation of beamforming. Therefore, I nowadays prefer only talking about precoding. In Massive MIMO, I think that precoding is the right word to use since what I advocate is a fully digital implementation, where the phases and powers can be jointly designed to achieve high capacity through spatial multiplexing of many users, in both NLoS and LOS scenarios.

Six Differences Between Massive MIMO for Sub-6 GHz and mmWave

The “Massive MIMO” name is currently being used for both sub-6 GHz and mmWave applications. This can be very confusing because the multi-antenna technology has rather different characteristics in these two applications.

The sub-6 GHz spectrum is particularly useful to provide network coverage, since the pathloss and channel coherence time are relatively favorable at such frequencies (recall that the coherence time is inversely proportional to the carrier frequency). Massive MIMO at sub-6 GHz spectrum can increase the efficiency of highly loaded cells, by upgrading the technology at existing base stations. In contrast, the huge available bandwidths in mmWave bands can be utilized for high-capacity services, but only over short distances due to the severe pathloss and high noise power (which is proportional to the bandwidth). Massive MIMO in mmWave bands can thus be used to improve the link budget.

Six key differences between sub-6 GHz and mmWave operation are provided below:

Sub-6 GHz mmWave
Deployment scenario Macro cells with support for high user mobility Small cells with low user mobility
Number of simultaneous users per cell Up to tens of users, due to the large coverage area One or a few users, due to the small coverage area
Main benefit from having many antennas Spatial multiplexing of tens of users, since the array gain and ability to separate users spatially lead to great spectral efficiency Beamforming to a single user, which greatly improves the link budget and thereby extends coverage
Channel characteristics Rich multipath propagation Only a few propagation paths
Spectral efficiency and bandwidth High spectral efficiency due to the spatial multiplexing, but small bandwidth Low spectral efficiency due to few users, large pathloss, and large noise power, but large bandwidth
Transceiver hardware Fully digital transceiver implementations are feasible and have been prototyped Hybrid analog-digital transceiver implementations are needed, at least in the first products

Since Massive MIMO was initially proposed by Tom Marzetta for sub-6 GHz applications, I personally recommend to use the “Massive MIMO” name  only for that use case. One can instead say “mmWave Massive MIMO” or just “mmWave” when referring to multi-antenna technologies for mmWave bands.

5.5 Hours of Massive MIMO Tutorials

Video recordings from the 2017 Joint IEEE SPS and EURASIP Summer School on Signal Processing for 5G Wireless Access are available for IEEE members, as we wrote about in a previous post. Now two of the Massive MIMO tutorial talks are openly available on Youtube.

Prof. Erik. G. Larsson gave a 2.5 hour tutorial on the fundamentals of Massive MIMO, which is highly recommended for anyone learning this topic. You can then follow up by reading his book with the same topic.

When you have viewed Erik’s introduction, you can learn more about the state-of-the-art signal processing schemes for Massive MIMO from another talk at the summer school. Dr. Emil Björnson gave a 3 hour tutorial on this topic:

Out-of-band Radiation can Impact the Massive MIMO Operation

The received signal power is proportional to the number of antennas M in Massive MIMO systems. This property is known as the array gain and it can basically be utilized in two different ways.

One option is to let the signal power become M times larger than in a single-antenna reference scenario. The increase in SNR will then lead to higher data rates for the users. The gain can be anything from \log_2(M) bit/s/Hz to almost negligible, depending on how interference-limited the system is. Another option is to utilize the array gain to reduce the transmit power, to maintain the same SNR as in the reference scenario. The corresponding power saving can be very helpful to improve the energy efficiency of the system.

In the uplink, with single-antenna user terminals, we can choose between these options. However, in the downlink, we might not have a choice. There are strict regulations on the permitted level of out-of-band radiation in practical systems. Since Massive MIMO uses downlink precoding, the transmitted signals from the base station have a stronger directivity than in the single-antenna reference scenario. The signal components that leak into the bands adjacent to the intended frequency band will then also be more directive.

For example, consider a line-of-sight scenario where the precoding creates an angular beam towards the intended user (as illustrated in the figure below). The out-of-band radiation will then get a similar angular directivity and lead to larger interference to systems operating in adjacent bands, if their receivers are close to the user (as the victim in the figure below). To counteract this effect, our only choice might be to reduce the downlink transmit power to keep the worst-case out-of-band radiation constant.

Another alternative is that the regulations are made more flexible with respect to precoded transmissions. The probability that a receiver in an adjacent band is hit by an interfering out-of-band beam, such that the interference becomes M times larger than in the reference scenario, reduces with an increasing number of antennas since the beams are narrower. Hence, if one can allow for beamformed out-of-band interference if it occurs with sufficiently low probability, the array gain in Massive MIMO can still be utilized to increase the SNRs. A third option will then be to (partially) reduce the transmit power to also allow for relaxed linearity requirements of the hardware.

These considerations are nicely discussed in an overview article that appeared on ArXiv earlier this year. There are also two papers that analyze the impact of out-of-bound radiation in Massive MIMO: Paper 1 and Paper 2.

Asymptomania

I am borrowing the title from a column written by my advisor two decades ago, in the array signal processing gold rush era.

Asymptotic analysis is a popular tool within statistical signal processing (infinite SNR or number of samples), information theory (infinitely long blocks) and more recently, [massive] MIMO wireless communications (infinitely many antennas).

Some caution is strongly advisable with respect to the latter. In fact, there are compelling reasons to avoid asymptotics in the number of antennas altogether:

  • First, elegant, rigorous and intuitively comprehensible capacity bound formulas are available in closed form.
    The proofs of these expressions use basic random matrix theory, but no asymptotics at all.
  • Second, the notion of “asymptotic limit” or “asymptotic behavior” helps propagate the myth that Massive MIMO somehow relies on asymptotics or “infinite” numbers (or even exorbitantly large numbers) of antennas.
  • Third, many approximate performance results for Massive MIMO (particularly “deterministic equivalents”) based on asymptotic analysis are complicated, require numerical evaluation, and offer little intuitive insight. (And, the verification of their accuracy is a formidable task.)

Finally, and perhaps most importantly, careless use of asymptotic arguments may yield erroneous conclusions. For example in the effective SINRs in multi-cell Massive MIMO, the coherent interference scales with M (number of antennas) – which yields the commonly held misconception that coherent interference is the main impairment caused by pilot contamination. But in fact, in many relevant circumstances it is not (see case studies here): the main impairment for “reasonable” values of M is the reduction in coherent beamforming gain due to reduced estimation quality, which in turn is independent of M.

In addition, the number of antennas beyond which the far-field assumption is violated is actually smaller than what one might first think (problem 3.14).