“We observed up to a 3.4x increase in downlink sector throughput and up to an 8.9x increase in the uplink sector throughput versus 8T8R (obviously the gain is substantially higher relative to 2T2R). Results varied based on the test conditions that we identified. Link budget tests revealed close to a triple-digit improvement in uplink data speeds. Preliminary results for the downlink also showed strong gains. Future improvements in 64T64R are forthcoming based on likely vendor product roadmaps.”
There are thousands of papers that analyze different aspects of Massive MIMO. Although many different algorithms and models have been considered, I would say that the most common ones are:
Independent Rayleigh fading channels;
Signal processing based on maximum ratio (MR) or zero-forcing (ZF).
These are, for example, the assumptions made in the textbook Fundamentals of Massive MIMO. The beautiful analysis and insightful closed-form expressions developed under these assumptions have had a profound impact on the adoption of Massive MIMO in 5G. I would, therefore, like to refer to this canonical form of the technology as Massive MIMO 1.0.
Taking the technology to the next level
It is possible to squeeze out even higher spectral efficiency out of multi-antenna systems if we design the systems differently. For example, the paper “Massive MIMO has unlimited capacity” showed that the upper limit on the capacity that appears in Massive MIMO 1.0, due to pilot contamination, can be alleviated by replacing the two above-mentioned assumptions by:
Spatially correlated Rayleigh fading;
Signal processing that cancels interference between the pilot-sharing users.
Spatial correlation is something that appears naturally in all communication systems, thus the main difference is to embrace this fact in the signal processing design instead of neglecting it. I believe that this can make such as a huge difference that it is appropriate to introduce the term Massive MIMO 2.0 to describe this development.
This is done in a recent review paper called “Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination“. The paper’s main conclusion is that the acquisition and utilization of spatial correlation information will be key in beyond-5G systems, to take the spectral efficiency to the next level. Since the largest gains appear when having even larger antenna arrays than in 5G, new antenna deployments concepts are bound to arise. Three promising examples are described in the paper: large intelligent surfaces, distributed post-cellular architectures, and the use of carrier frequencies beyond 100 GHz.
As a complement to the review paper, the basics of Massive MIMO 2.0 are also described in the following video:
Channel fading has always been a limiting factor in wireless communications, which is why various diversity schemes have been developed to combat fading (and other channel impairments). The basic idea is to obtain many “independent” observations of the channel and exploit that it is unlikely that all of these observations are subject to deep fade in parallel. These observations can be obtained over time, frequency, space, polarization, etc.
Only one antenna is used at a time when using antenna selection.
Antenna selection is the basic form of space diversity. Suppose a base station (BS) equipped with multiple antennas applies antenna selection. In the uplink, the BS only uses the antenna that currently gives the highest signal-to-interference-and-noise ratio (SINR). In the downlink, the BS only transmits from the antenna that currently has the highest SINR. As the user moves around, the fading changes and we, therefore, need to reselect which antenna to use.
The term antenna selection diversity can be traced back to the 1980s, but this diversity scheme was analyzed already in the 1950s. One well-cited paper from that time is Linear Diversity Combining Techniques by D. G. Brennan. This paper demonstrates mathematically and numerically that selection diversity is suboptimal, while the scheme called maximum-ratio combining (MRC) always provides higher SINR. Hence, instead of only selecting one antenna, it is preferable for the BS to coherently combine the signals from/to all the antennas to maximize the SINR. When the MRC scheme is applied in Massive MIMO with a very large number of antennas, we often talk about channel hardening but this is nothing but an extreme form of space diversity that almost entirely removes the fading effect.
Even if the suboptimality of selection diversity has been known for 60 years, the antenna selection concept has continued to be analyzed in the MIMO literature and recently also in the context of Massive MIMO. Many recent papers are considering a generalization of the scheme that is known as antenna subset selection, where a subset of the antennas is selected and then MRC is applied using only these ones.
Why use antenna selection?
A common motivation for using antenna selection is that it would be too expensive to equip every antenna with a dedicated transceiver chain in Massive MIMO, therefore we need to sacrifice some of the performance to achieve a feasible implementation. This is a misleading motivation since Massive MIMO capable base stations have already been developed and commercially deployed. I think a better motivation would be that we can save power by only using a subset of the antennas at a time, particularly, when the traffic load is below the maximum system capacity so we don’t need to compromise with the users’ throughput.
The recent papers [1], [2], [3] on the topic consider narrowband MIMO channels. In contrast, Massive MIMO will in practice be used in widebandsystems where the channel fading is different between subcarriers. That means that one antenna will give the highest SINR on one subcarrier, while another antenna will give the highest SINR on another subcarrier. If we apply the antenna selection principle on a per-subcarrier basis in a wideband OFDM system with thousands of subcarriers, we will probably use all the antennas on at least one of the subcarrier. Consequently, we cannot turn off any of the antennas and the power saving benefits are lost.
We can instead apply the antenna selection scheme based on the average received power over all the subcarriers, but most channel models assume that this average power is the same for every base station antenna (this applies to both i.i.d. fading and correlated fading models, such as the one-ring model). That means that if we want to turn off antennas, we can select them randomly since all random selections will be (almost) equally good, and there are no selection diversity gains to be harvested.
This is why we can forget about antenna selection diversity in Massive MIMO!
It is only when the average channel gain is different among the antennas that antenna subset selection diversity might have a role to play. In that case, the antenna selection is governed by variations in the large-scale fading instead of variations in the small-scale fading, as conventionally assumed. This paper takes a step in that direction. I think this is the only case of antenna (subset)selection that might deserve further attention, while in general, it is a concept that can be forgotten.
As this decade is approaching its end, so is the development of 5G technologies. The first 5G networks are currently begin deployed and, over the next few years, we will learn which features in the 5G standards that will actually be used and provide good performance.
When it comes to Massive MIMO for sub-6 GHz and mmWave bands, many of the previously open research problems have been resolved over the past five years – at least from an academic perspective. There are still important open problems at the border between theory and practical implementation. However, I strongly believe that this is a time when we should also look further into the future to identify the next big things.
To encourage more future-looking research, I joined as one of the guest editors of an upcoming special issue on Multiple Antenna Technologies for Beyond 5G in the IEEE Journal on Selected Areas in Communications (JSAC). The call for papers is available online and the submission deadline is 1 September 2019. Hence, if you start your research on this topic right away, you will have plenty of time to write a paper!
The call for papers identifies three promising directions: Cell-free Massive MIMO, Lens arrays, and Large intelligent surfaces. However, I am sure there are many other interesting research directions that are yet to be discovered. I recommend prospective authors to think creatively and look for the next big steps in the multiple antenna technologies. Remember that Massive MIMO was generally viewed as science fiction ten years ago, and now it is a reality!
Ever since I finished the writing of the book Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency, I have felt that I’m somewhat done with my research on conventional Massive MIMO. The spectral efficiency, energy efficiency, resource allocation, and pilot contamination phenomenon are well understood by now. This is not a bad thing—as researchers, we are supposed to solve the problems we are analyzing. But it means that this is a good time to look for new research directions. It should preferably be something where we can utilize our skills as Massive MIMO researchers to do something new and exciting!
With this in mind, I gathered a team consisting of myself, Luca Sanguinetti, Henk Wymeersch, Jakob Hoydis, and Thomas L. Marzetta. Each one of us has written about one promising new direction of research related to antenna arrays and MIMO, including the background of the topic, our long-term vision, and pertinent open problem. This resulted in the paper:
You can find the preprint on arXiv.org or by clicking on the name of the paper. I hope that you will find it as interesting to read as it was for us to write!
This chip-scale atomic clock (CSAC) device, developed by Microsemi, brings atomic clock timing accuracy (see the specs available in the link) in a volume comparable to a matchbox, and 120 mW power consumption. This is way too much for a handheld gadget, but undoubtedly negligible for any fixed installation powered from the grid. An alternative to synchronization through GNSS that works anywhere, including indoor in GNSS-denied environments.
I haven’t seen a list price, and I don’t know how much exotic metals and what licensing costs that its manufacture requires, but let’s ponder the possibility that a CSAC could be manufactured for the mass-market for a few dollars each. What new applications would then become viable in wireless?
The answer is mostly (or entirely) speculation. One potential application that might become more practical is positioning using distributed arrays. Another is distributed multipair relaying. Here and here are some specific ideas that are communication-theoretically beautiful, and probably powerful, but that seem to be perceived as unrealistic because of synchronization requirements. Perhaps CoMP and distributed MIMO, a.k.a. “cell-free Massive MIMO”, applications could also benefit.
Other applications might be applications for example in IoT, where a device only sporadically transmits information and wants to stay synchronized (perhaps there is no downlink, hence no way of reliably obtaining synchronization information). If a timing offset (or frequency offset for that matter) is unknown but constant over a very long time, it may be treated as a deterministic unknown and estimated. The difficulty with unknown time and frequency offsets is not their existence per se, but the fact that they change quickly over time.
It’s often said (and true) that the “low” speed of light is the main limiting factor in wireless. (Because channel state information is the main limiting factor of wireless communications. If light were faster, then channel coherence would be longer, so acquiring channel state information would be easier.) But maybe the unavailability of a ubiquitous, reliable time reference is another, almost as important, limiting factor. Can CSAC technology change that? I don’t know, but perhaps we ought to take a closer look.
Although there are nowadays many Massive MIMO testbeds around the world, there are very few open datasets with channel measurement results. This will likely change over the next few years, spurred by the need for having common datasets when applying and evaluating machine learning methods in wireless communications.
The Networked Systems group at KU Leuven has recently made the results from one of their measurement campaigns openly available. It includes 36 user positions and two base station configurations: one 64-antenna co-located array and one distributed deployment with two 32-antenna arrays.
The following video showcases the measurement setup:
