Category Archives: Technical insights

More Bandwidth Requires More Power or Antennas

The main selling point of millimeter-wave communications is the abundant bandwidth available in such frequency bands; for example, 2 GHz of bandwidth instead of 20 MHz as in conventional cellular networks. The underlying argument is that the use of much wider bandwidths immediately leads to much higher capacities, in terms of bit/s, but the reality is not that simple.

To look into this,  consider a communication system operating over a bandwidth of $B$ Hz. By assuming an additive white Gaussian noise channel, the capacity becomes

     $$ C = B \log_2 \left(1+\frac{P \beta}{N_0 B} \right)$$

where $P$ W is the transmit power, $\beta$ is the channel gain, and $N_0$ W/Hz is the power spectral density of the noise. The term $(P \beta)/(N_0 B)$ inside the logarithm is referred to as the signal-to-noise ratio (SNR).

Since the bandwidth $B$ appears in front of the logarithm, it might seem that the capacity grows linearly with the bandwidth. This is not the case since also the noise term $N_0 B$ in the SNR also grows linearly with the bandwidth. This fact is illustrated by Figure 1 below, where we consider a system that achieves an SNR of 0 dB at a reference bandwidth of 20 MHz. As we increase the bandwidth towards 2 GHz, the capacity grows only modestly. Despite the 100 times more bandwidth, the capacity only improves by $1.44\times$, which is far from the $100\times$ that a linear increase would give.

Figure 1: Capacity as a function of the bandwidth, for a system with an SNR of 0 dB over a reference bandwidth of 20 MHz. The transmit power is fixed.

The reason for this modest capacity growth is the fact that the SNR reduces inversely proportional to the bandwidth. One can show that

     $$ C \to \frac{P \beta}{N_0}\log_2(e) \quad \textrm{as} \,\, B \to \infty.$$

The convergence to this limit is seen in Figure 1 and is relatively fast since $\log_2(1+x) \approx x \log_2(e)$ for $0 \leq x \leq 1$.

To achieve a linear capacity growth, we need to keep the SNR $(P \beta)/(N_0 B)$ fixed as the bandwidth increases. This can be achieved by increasing the transmit power $P$ proportionally to the bandwidth, which entails using $100\times$ more power when operating over a $100\times$ wider bandwidth. This might not be desirable in practice, at least not for battery-powered devices.

An alternative is to use beamforming to improve the channel gain. In a Massive MIMO system, the effective channel gain is $\beta = \beta_1 M$, where $M$ is the number of antennas and $\beta_1$ is the gain of a single-antenna channel. Hence, we can increase the number of antennas proportionally to the bandwidth to keep the SNR fixed.

Figure 2: Capacity as a function of the bandwidth, for a system with an SNR of 0 dB over a reference bandwidth of 20 MHz with one antenna. The transmit power (or the number of antennas) is either fixed or grows proportionally to the bandwidth.

Figure 2 considers the same setup as in Figure 1, but now we also let either the transmit power or the number of antennas grow proportionally to the bandwidth. In both cases, we achieve a capacity that grows proportionally to the bandwidth, as we initially hoped for.

In conclusion, to make efficient use of more bandwidth we require more transmit power or more antennas at the transmitter and/or receiver. It is worth noting that these requirements are purely due to the increase in bandwidth. In addition, for any given bandwidth, the operation at millimeter-wave frequencies requires much more transmit power and/or more antennas (e.g., additional constant-gain antennas or one constant-aperture antenna) just to achieve the same SNR as in a system operating at conventional frequencies below 5 GHz.

Channel Hardening Makes Fading Channels Behave as Deterministic

One of the main impairments in wireless communications is small-scale channel fading. This refers to random fluctuations in the channel gain, which are caused by microscopic changes in the propagation environments. The fluctuations make the channel unreliable, since occasionally the channel gain is very small and the transmitted data is then received in error.

The diversity achieved by sending a signal over multiple channels with independent realizations is key to combating small-scale fading. Spatial diversity is particularly attractive, since it can be obtained by simply having multiple antennas at the transmitter or the receiver. Suppose the probability of a bad channel gain realization is p. If we have M antennas with independent channel gains, then the risk that all of them are bad is pM. For example, with p=0.1, there is a 10% risk of getting a bad channel in a single-antenna system and a 0.000001% risk in an 8-antenna system. This shows that just a few antennas can be sufficient to greatly improve reliability.

In Massive MIMO systems, with a “massive” number of antennas at the base station, the spatial diversity also leads to something called “channel hardening”. This terminology was used already in a paper from 2004:

M. Hochwald, T. L. Marzetta, and V. Tarokh, “Multiple-antenna channel hardening and its implications for rate feedback and scheduling,” IEEE Transactions on Information Theory, vol. 50, no. 9, pp. 1893–1909, 2004.

In short, channel hardening means that a fading channel behaves as if it was a non-fading channel. The randomness is still there but its impact on the communication is negligible. In the 2004 paper, the hardening is measured by dividing the instantaneous supported data rate with the fading-averaged data rate. If the relative fluctuations are small, then the channel has hardened.

Since Massive MIMO systems contain random interference, it is usually the hardening of the channel that the desired signal propagates over that is studied. If the channel is described by a random M-dimensional vector h, then the ratio ||h||2/E{||h||2} between the instantaneous channel gain and its average is considered. If the fluctuations of the ratio are small, then there is channel hardening. With an independent Rayleigh fading channel, the variance of the ratio reduces with the number of antennas as 1/M. The intuition is that the channel fluctuations average out over the antennas. A detailed analysis is available in a recent paper.

The variance of ||h||2/E{||h||2} decays as 1/M for independent Rayleigh fading channels.

The figure above shows how the variance of ||h||2/E{||h||2} decays with the number of antennas. The convergence towards zero is gradual and so is the channel hardening effect. I personally think that you need at least M=50 to truly benefit from channel hardening.

Channel hardening has several practical implications. One is the improved reliability of having a nearly deterministic channel, which results in lower latency. Another is the lack of scheduling diversity; that is, one cannot schedule users when their ||h||2 are unusually large, since the fluctuations are small. There is also little to gain from estimating the current realization of ||h||2, since it is relatively close to its average value. This can alleviate the need for downlink pilots in Massive MIMO.

Pilot Contamination: an Ultimate Limitation?

Many misconceptions float around about the pilot contamination phenomenon. While existent in any multi-cellular system, its effect tends to be particularly pronounced in Massive MIMO due to the presence of coherent interference, that scales proportionally to the coherent beamforming gain. (Chapter 4 in Fundamentals of Massive MIMO gives the details.)

A good system design definitely must not ignore pilot interference. While it is easily removed “on the average” through greater-than-one reuse, the randomness present in wireless communications – especially the shadow fading – will occasionally cause a few terminals to be severely hit by pilot contamination and bring down their performance. This is problematic whenever we are concerned about the provision of uniformly great service in the cell – and that is one of the principal selling arguments for Massive MIMO. Notwithstanding, the impact of pilot contamination can be reduced significantly in practice by appropriate pilot reuse and judicious power control. (Chapters 5-6 in Fundamentals of Massive MIMO gives many details.)

A more fundamental question is whether pilot contamination could be entirely overcome: Does there exist an upper bound on capacity that saturates as the number of antennas, M, is increased indefinitely? Some have speculated that it cannot; much in line with known capacity upper bounds for cellular base station cooperation. While this question may be of more academic than practical interest, it has long been open except for in some trivial special cases: If the channels of two terminals lie in non-overlapping subspaces and Bayesian channel estimation is used, the channel estimates will not be contaminated; capacity grows as log(M) when M increases without bound.

A much deeper result is established in this recent paper: the subspaces of the channel covariances may overlap, yet capacity grows as log(M). Technically, a Rayleigh fading with spatial correlation is assumed, and the correlation matrices for the contaminating terminals must only be linearly independent as M goes to infinity (exact conditions in the paper). In retrospect, this is not unreasonable given the substantial a priori knowledge exploited by the Bayesian channel estimator, but I found it amazing how weak the required conditions on the correlation matrices are. It remains unclear whether the result generalizes to the case of a growing number of interferers: letting the number of antennas go to infinity and then growing the network is not the same thing as taking an “infinite” (scalable) network and increasing the number of antennas. But this paper elegantly and rigorously answers a long-standing question that has been the subject of much debate in the community – and is a recommended read for anyone interested in the fundamental limits of Massive MIMO.

Pilot Contamination in a Nutshell

One word that is tightly connected with Massive MIMO is pilot contamination. This is a phenomenon that can appear in any communication system that operates under interference, but in this post, I will describe its basic properties in Massive MIMO.

The base station wants to know the channel responses of its user terminals and these are estimated in the uplink by sending pilot signals. Each pilot signal is corrupted by inter-cell interference and noise when received at the base station. For example, consider the scenario illustrated below where two terminals are transmitting simultaneously, so that the base station receives a superposition of their signals—that is, the desired pilot signal is contaminated.

When estimating the channel from the desired terminal, the base station cannot easily separate the signals from the two terminals. This has two key implications:

First, the interfering signal acts as colored noise that reduces the channel estimation accuracy.

Second, the base station unintentionally estimates a superposition of the channel from the desired terminal and from the interferer. Later, the desired terminal sends payload data and the base station wishes to coherently combine the received signal, using the channel estimate. It will then unintentionally and coherently combine part of the interfering signal as well. This is particularly poisonous when the base station has M antennas, since the array gain from the receive combining increases both the signal power and the interference power proportionally to M. Similarly, when the base station transmits a beamformed downlink signal towards its terminal, it will unintentionally direct some of the signal towards to interferer. This is illustrated below.

In the academic literature, pilot contamination is often studied under the assumption that the interfering terminal sends the same pilot signal as the desired terminal, but in practice any non-orthogonal interfering signal will cause the two effects described above.

Cell-Free Massive MIMO: New Concept

Conventional mobile networks (a.k.a. cellular wireless networks) are based on cellular topologies. With cellular topologies, a land area is divided into cells. Each cell is served by one base station. An interesting question is: shall the future mobile networks continue to have cells? My quick answer is no, cell-free networks should be the way to do in the future!

Future wireless networks have to manage at the same time billions of devices; each needs a high throughput to support many applications such as voice, real-time video, high quality movies, etc. Cellular networks could not handle such huge connections since user terminals at the cell boundary suffer from very high interference, and hence, perform badly. Furthermore, conventional cellular systems are designed mainly for human users. In future wireless networks, machine-type communications such as the Internet of Things, Internet of Everything, Smart X, etc. are expected to play an important role. The main challenge of machine-type communications is scalable and efficient connectivity for billions of devices. Centralized technology with cellular topologies does not seem to be working for such scenarios since each cell can cover a limited number of user terminals. So why not cell-free structures with decentralized technology? Of course, to serve many user terminals and to simplify the signal processing in a distributed manner, massive MIMO technology should be included. The combination between cell-free structure and massive MIMO technology yields the new concept: Cell-Free Massive MIMO.

What is Cell-Free Massive MIMO? Cell-Free Massive MIMO is a system where a massive number access points distributed over a large area coherently serve a massive number of user terminals in the same time/frequency band. Cell-Free Massive MIMO focuses on cellular frequencies. However, millimeter wave bands can be used as a combination with the cellular frequency bands. There are no concepts of cells or cell boundaries here. Of course, specific signal processing is used, see [1] for more details. Cell-Free Massive MIMO is a new concept. It is a new practical, useful, and scalable version of network MIMO (or cooperative multipoint joint processing) [2, 3]. To some extent, Massive MIMO technology based on the favorable propagation and channel hardening properties is used in Cell-Free Massive MIMO.

Cell-Free Massive MIMO is different from distributed Massive MIMO [4]. Both systems use many service antennas in a distributed way to serve many user terminals, but they are not entirely the same. With distributed Massive MIMO, the base station antennas are distributed within each cell, and these antennas only serve user terminals within that cell. By contrast, in Cell-Free Massive MIMO there are no cells. All service antennas coherently serve all user terminals. The figure below compares the structures of Cell-Free Massive MIMO and distributed Massive MIMO.

comami cellfree
Distributed Massive MIMO Cell-Free Massive MIMO

[1] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson, and T. L. Marzetta, “Cell-Free Massive MIMO versus Small Cells,” IEEE Trans. Wireless Commun., 2016 submitted for publication. Available: https://arxiv.org/abs/1602.08232

[2] G. Foschini, K. Karakayali, and R. A. Valenzuela, “Coordinating multiple antenna cellular networks to achieve enormous spectral efficiency,” IEE Proc. Commun. , vol. 152, pp. 548–555, Aug. 2006.

[3] E. Björnson, R. Zakhour, D. Gesbert, B. Ottersten, “Cooperative Multicell Precoding: Rate Region Characterization and Distributed Strategies with Instantaneous and Statistical CSI,” IEEE Trans. Signal Process., vol. 58, no. 8, pp. 4298-4310, Aug. 2010.

[4] K. T. Truong and R.W. Heath Jr., “The viability of distributed antennas for massive MIMO systems,” in Proc. Asilomar CSSC, 2013, pp. 1318–1323.

Definition of Massive MIMO

What is Massive MIMO?  The term has been used for many different systems and the only common denominator seems to be a multi-user MIMO system with everything between 10 to infinitely many antennas.  In the book [1], the authors give the following definition:

“Massive MIMO is a useful and scalable version of Multiuser MIMO.  There are three fundamental distinctions between Massive MIMO and conventional Multiuser MIMO. First, only the base station learns G. Second, M is typically much larger than K, although this does not have to be the case. Third, simple linear signal processing is used both on the uplink and on the downlink. These features render Massive MIMO scalable with respect to the number of base station antennas, M.”

(Note: M is the number of antennas, K is the number of users, and G denotes the channel matrix).

In [2], we find another definition:

“Massive MIMO is a multi-user MIMO system with M antennas and K users per BS. The system is characterized by M ≫ K and operates in TDD mode using linear uplink and downlink processing.”

Both are nice general definitions that cover most systems that commonly are called “Massive MIMO”.  However, their generality also makes them vague and they fail to pinpoint the essence of Massive MIMO.  Here, is my take on a slightly more precise definition:

“Massive MIMO is a multi-user MIMO system that (1) serves multiple users through spatial multiplexing over a channel with favorable propagation in time-division duplex and (2) relies on channel reciprocity and uplink pilots to obtain channel state information.”

Now, you might ask: So what is then “favorable propagation”?  We need a second definition:

“The propagation is said to be favorable when users are mutually orthogonal in some practical sense.”

Again you ask: in what practical sense?  If h∈ℂᴹ is the channel vector to one user and g∈ℂᴹ the channel vector to another, the users are said to be orthogonal if hg = 0.  Unfortunately, this is never true in a real system.  It can be practically true, however, if we say that users are practically orthogonal when hg/(‖h‖‖g‖) has mean zero and a variance that is much smaller than one.

There we go: a more-or-less rigorous definition of Massive MIMO.  Note that this definition does not require the number of users to be small in any sense.  So, to the big question: How many antennas does a base station need to be “massive”? The answer is given for the i.i.d. Rayleigh fading channel in the following curve that shows how the users’ channels become practically orthogonal as the number of antennas is increased.

Variance
Massive MIMO base stations have more than 100 antennas

 

  1. [1] T. L. Marzetta, E. G. Larsson, H. Yang, N. Q. Ngo. Fundamentals of Massive MIMO. Cambridge University Press, 2016.
  2. [2] T. V. Chien, E. Björnson, “Massive MIMO Communications,” in 5G Mobile Communications, W. Xiang et al. (eds.), pp. 77-116, Springer, 2017.

How Many Antennas are Useful?

One question tends to reoccur: How many antennas can a Massive MIMO base station usefully deploy? Current thinking for macro-cellular is that 100-200 antennas would be suitable. Will we in the future see a lot more, thousands or so?

In that application, I don’t think so. Here is why.

What ultimately limits Massive MIMO is mobility: no more than half of the coherence time-bandwidth product should be occupied by pilot transmission activities. (This is the “half and half rule”.) In macro-cellular at 3 GHz, with highway mobility we may have on the order of 200 kHz x 1 millisecond coherence; that is 200 samples. With pilot reuse of 3 (that practically does away with pilot contamination), we could, then ultimately learn the channel to some 30 simultaneously served terminals – assuming mutually orthogonal pilots. Once the number of base station antennas M reaches beyond twice this number, with some margin – say M=100, the spectral efficiency grows logarithmically with M. That means, even doubling M yields only a 3dB effective SINR increase, that is a single extra bit per second/Hz per terminal. Beyond M=100 or M=200, it may not be worth it. Multiple antennas are only truly useful if they are used to multiplex, and mobility limits the amount of multiplexing we can perform.

So why not quadruple the number of antennas for additional coverage? May not be worth it either. Going from M=200 to M=2000 gives 10 dB – that pays for a 75% range extension, or, alternatively, a tenth of the losses incurred by an energy-saving coated window glass.

In stationary environments, the story is different – a topic that we will be returning to.

How distant into the future?
How distant into the future?