All posts by Erik G. Larsson

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.


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).

Does Reciprocity-based Beamforming Break Down at Low SNR?

I hear this being claimed now and then, and it is – of course – both correct and incorrect, at the same time. For the benefit of our readers I take the opportunity to provide some free consulting on the topic.

The important fact is that ergodic capacity can be lower-bounded by a formula of the form log2(1+SINR), where SINR is an “effective SINR” (that includes, among others, the effects of the terminal’s lack of channel knowledge).

This effective SINR scales proportionally to M (number of antennas), for fixed total radiated power.  Compared to a single-antenna system, reciprocity always offers M times better “beamforming gain” regardless of the system’s operating point.  (In fact one of the paradoxes of Massive MIMO is that performance always increases with M, despite the fact that there are “more unknowns to estimate”!) And yes, at very low SNR, the effective SINR is proportional to SNR^2 so reciprocity-based beamforming does “break down”, however, it is still M times better than a single-antenna link (with the same total radiated power). One will also, eventually, reach a point where the capacity bound for omnidirectional transmission (e.g. using a space-time code with appropriate dimension reduction in order to host the required downlink pilots) exceeds that of reciprocity-based beamforming, however, importantly, in this regime the bounds may be loose.

These matters, along with numerous case studies involving actual link budget calculations, are of course rigorously explained in our recent textbook.

Massive MIMO at 60 GHz vs. 2 GHz: How Many More Antennas?

The Brooklyn summit last week was a great event. I gave a talk (here are the slides) comparing MIMO at “PCS” (2 GHz) and mmWave (60 GHz) in line-of-sight. There are two punchlines: first, scientifically, while a link budget calculation might predict that 128.000 mmWave antennas are needed to match up the performance of 128-antenna PCS MIMO, there is a countervailing effect in that increasing the number of antennas improves channel orthogonality so that only 10.000 antennas are required. Second, practically, although 10.000 is a lot less than 128.000, it is still a very large number! Here is a writeup with some more detail on the comparison.

I also touched the (for sub-5 GHz bands somewhat controversial) topic of hybrid beamforming, and whether that would reduce the required amount of hardware.

A question from the audience was whether the use of antennas with larger physical aperture (i.e., intrinsic directivity) would change the conclusions. The answer is no: the use of directional antennas is more or less equivalent to sectorization. The problem is that to exploit the intrinsic gain, the antennas must a priori point “in the right direction”. Hence, in the array, only a subset of the antennas will be useful when serving a particular terminal. This impacts both the channel gain (reduced effective aperture) and orthogonality (see, e.g, Figure 7.5 in this book).

There was also a stimulating panel discussion afterwards. One question discussed in the panel concerned the necessity, or desirability, of using multiple terminal antennas at mmWave. Looking only at the link budget, base station antennas could be traded against terminal antennas – however, that argument neglects the inevitably lost orthogonality, and furthermore it is not obvious how beam-finding/tracking algorithms will perform (millisecond coherence time at pedestrian speeds!). Also, obviously, the comparison I presented is extremely simplistic – to begin with, the line-of-sight scenario is extremely favorable for mmWaves (blocking problems), but also, I entirely neglected polarization losses. Solely any attempts to compensate for these problems are likely to require multiple terminal antennas.

Other topics touched in the panel were the viability of Massive MIMO implementations. Perhaps the most important comment in this context made was by Ian Wong of National Instruments: “In the past year, we’ve actually shown that [massive MIMO] works in reality … To me, the biggest development is that the skeptics are being quiet.” (Read more about that here.)

Real-Time Massive MIMO DSP at 50 milliWatt

Colleagues at Lund University presented last month a working circuit that performs, in real time, zero-forcing decoding and precoding of 8 simultaneous terminals with 128 base station antennas, over a 20 MHz bandwidth at a power consumption of about 50 milliWatt.

Impressive, and important.

Granted, this number does not include the complexity of FFTs, sampling rate conversions, and several other (non-insignificant) tasks; however, it does include the bulk of the “Massive-MIMO”-specific digital processing. The design exploits a number of tricks and Massive-MIMO specific properties: diagonal dominance of the channel Gramian, in particular, in sufficiently favorable propagation.

When I started work on Massive MIMO in 2009, the common view held was that the technology would be infeasible because of computational complexity. Particularly, the sheer idea of performing zero-forcing processing in real time was met with, if not ridicule, extreme skepticism. We quickly realized, however, that a reasonable DSP implementation would require no more than some ten Watt. While that is a small number in itself, it turned out to be an overestimate by orders of magnitude!

I spoke with some of the lead inventors of the chip, to learn more about its design. First, the architectures for decoding and for precoding differ a bit. While there is no fundamental reason for why this has to be so, one motivation is the possible use of nonlinear detectors on uplink. (The need for such detectors, for most “typical” cellular Massive MIMO deployments, is not clear – but that is another story.)

Second, and more importantly, the scalability of the design is not clear. While the complexity of the matrix operations themselves scale fast with the dimension, the precision in the arithmetics may have to be increased as well – resulting in a much-faster-than-cubically overall complexity scaling. Since Massive MIMO operates at its best when multiplexing to many tens of terminals (or even thousands, in some applications), significant challenges remain for the future. That is good news for circuit engineers, algorithm designers, and communications theoreticians alike. The next ten years will be exciting.

How Much Performance is Lost by FDD Operation?

There has been a long-standing debate on the relative performance between reciprocity-based (TDD) Massive MIMO and that of FDD solutions based on grid-of-beams, or hybrid beamforming architectures. The matter was, for example, the subject of a heated debate in the 2015 Globecom industry panel “Massive MIMO vs FD-MIMO: Defining the next generation of MIMO in 5G” where on the one hand, the commercial arguments for grid-of-beams solutions were clear, but on the other hand, their real potential for high-performance spatial multiplexing was strongly contested.

While it is known that grid-of-beams solutions perform poorly in isotropic scattering, no prior experimental results are known. This new paper:

Massive MIMO Performance—TDD Versus FDD: What Do Measurements Say?

answers this performance question through the analysis of real Massive MIMO channel measurement data obtained at the 2.6 GHz band. Except for in certain line-of-sight (LOS) environments, the original reciprocity-based TDD Massive MIMO represents the only effective implementation of Massive MIMO at the frequency bands under consideration.