Category Archives: 5G

5G, Commentary, News, Technical insights

Superimposed Pilots?

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The concept of superimposed pilots is (at least 15 years) old, but clever and intriguing. The idea is to add pilot and data samples together, instead of separating them in time and/or frequency, before modulating with waveforms. More recently, the authors of this paper argued that in massive MIMO, based on certain simulations supported by asymptotic analysis, superimposed pilots provide superior performance and that there are strong reasons for superimposed pilots to make their way to practical use.

Until recently, a more rigorous analysis was unavailable. Some weeks ago the authors of this paper argued, that all things considered, the use of superimposed pilots does not offer any appreciable gains for practically interesting use cases. The analysis was based on a capacity-bounding approach for finite numbers of antennas and finite channel coherence, but it assumed the most basic form of signal processing for detection and decoding.

There still remains some hope of seeing improvements, by implementing more advanced signal processing, like zero-forcing, multicell MMSE decoding, or iterative decoding algorithms, perhaps involving “turbo” information exchange between the demodulator, channel estimation, and detector. It will be interesting to follow future work by these two groups of authors to understand how large improvements (if any) superimposed pilots eventually can give.

There are, at least, two general lessons to learn here. First, that performance predictions based on asymptotics can be misleading in practically relevant cases. (I have discussed this issue before.) The best way to perform analysis is to use rigorous capacity lower bounds, or possibly, in isolated cases of interest, link-level simulations with channel coding (for which, as it turns out, capacity bounds are a very good proxy). Second, more concretely, that while it may be tempting, to superimpose-squeeze multiple symbols into the same time-frequency-space resource, once all sources of impairments (channel estimation errors, interference) are accurately accounted for, the gains tend to evaporate. (It is for the same reason that NOMA offers no substantial gains in MIMO systems – a topic that I may return to at a later time.)

5G, Commentary, Technical insights

Six Differences Between MU-MIMO and Massive MIMO

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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) M ≈ K and both are small (e.g., below 10) M ≫ 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.

5G, Commentary, Technical insights

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

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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.

5G, Education, Technical insights

5.5 Hours of Massive MIMO Tutorials

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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:

5G, Commentary, Technical insights

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

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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.

5G, Beyond 5G, Education, Technical insights

Asymptomania

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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).