Category Archives: 5G

Is It Time to Forget About Antenna Selection?

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 wideband systems 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.

Commercial 5G Networks

Some of the first 5G phones were announced at the Mobile World Congress last week. Many of these phones are reportedly based on the Snapdragon 855 Mobile Platform from Qualcomm, which supports 5G with up to 100 MHz bandwidth in sub-6 GHz bands and up to 800 MHz bandwidth in mmWave bands.

Despite all the fuss about mmWave being the key feature of 5G, it appears that the first commercial networks will utilize conventional sub-6 GHz bands; for example, Sprint will launch 5G using the 2.5 GHz band in nine major US cities from May to June 2019. Sprint is using Massive MIMO panels from Ericsson, Nokia, and Samsung. The reason to use the 2.5 GHz band is to achieve a reasonably wide network coverage with a limited number of base stations. The new Massive MIMO base stations will initially be used for both 4G and 5G. The following video details Sprint’s preparations for their 5G launch:

Another interesting piece of news from the Mobile World Congress is that 95% of the base stations that Huawei is currently shipping contain Massive MIMO with either 32 or 64 antennas.

Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Come listen to Liesbet Van der Perre, Professor at KU Leuven (Belgium) on Monday February 18 at 2.00 pm EST.

She gives a webinar on state-of-the-art circuit implementations of Massive MIMO, and outlines future research challenges. The webinar is based on, among others, this paper.

In more detail the webinar will summarize the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It will explain how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes will be discussed. Open challenges and directions for future research are suggested.

Listen to the webinar by following this link.

Massive MIMO is Supporting the Super Bowl in Atlanta

When I went to high school in Sweden, some of my friends stayed up very late at night (due to the time difference) to watch the Super Bowl; the annual championship in the American football league. This game is generally not a big thing in Sweden, but it is huge in America.

This Sunday, the Super Bowl takes place in Atlanta and one million people are expected to come to downtown Atlanta, to either watch the game at the stadium or root for their teams in other ways. Hence, massive flows of images and videos will be posted on social media from people located in a fairly limited area. To prepare for the game, the telecom operators have upgraded their cellular networks and taken the opportunity to market their 5G efforts.

Massive MIMO in the sub-6 GHz band with 64 antennas (and 128 radiating elements) is a key technology to handle the given situation, where huge capacity can be achieved by spatially multiplexing a large number of users in the downtown. Massive MIMO is a “small box with a massive impact” Cyril Mazloum, Network Manager for Sprint in Atlanta, told Hypepotamus. This refers to the fact that the Massive MIMO equipment is, despite the naming, physically smaller than the legacy equipment it replaces. In the following video, Heather Campbell of the Sprint Network Team explains how a ten times higher capacity is achieved in the 2.5 GHz band by their Massive MIMO deployment, which I have also reported about before.

All the major cellular operators have upgraded their networks in preparation for the big game. AT&T has reportedly spent $43 million to deploy 1,500 new antennas. Verizon has installed 30 new macro sites, 300 new small cells, and upgraded the capacity of 150 existing sites. T-Mobile has reportedly boosted its network capacity by eight times. Massive MIMO and 5G are clearly one of the key technologies in all these cases.

Beamforming From Distributed Arrays

When an antenna array is used to focus a transmitted signal on a receiver, we call this beamforming (or precoding) and we usually illustrate it as shown to the right. This cartoonish illustration is only applicable when the antennas are gathered in a compact array and there is a line-of-sight channel to the receiver.

If we want to deploy very many antennas, as in Massive MIMO, it might be preferable to distribute the antennas over a larger area. One such deployment concept is called Cell-free Massive MIMO. The basic idea is to have many distributed antennas that are transmitting phase-coherently to the receiving user. In other words, the antennas’ signal components add constructively at the location of the user, just as when using a compact array for beamforming. It is therefore convenient to call it beamforming in both cases—algorithmically it is the same thing!

The question is: How can we illustrate the beamforming effect when using a distributed array?

The figure below shows how to do it. I consider a toy example with 80 star-marked antennas deployed along the sides of a square and these antennas are transmitting sinusoids with equal power, but different phases. The phases are selected to make the 80 sine-components phase-aligned at one particular point in space (where the receiving user is supposed to be):

Clearly, the “beamforming” from a distributed array does not give rise to a concentrated signal beam, but the signal amplification is confined to a small spatial region (where the color is blue and the values on the vertical axis are close to one). This is where the signal components from all the antennas are coherently combined. There are minor fluctuations in channel gain at other places, but the general trend is that the components are non-coherently combined everywhere except at the receiving user. (Roughly the same will happen in a rich multipath channel, even if a compact array is used for transmission.)

By looking at a two-dimensional version of the figure (see below), we can see that the coherent combination occurs in a circular region that is roughly half a wavelength in diameter. At the carrier frequencies used for cellular networks, this region will only be a few centimeters or millimeters wide. It is almost magical how this distributed array can amplify the signal at such a tiny spatial region! This spatial region is probably what the company Artemis is calling a personal cell (pCell) when marketing their distributed MIMO solution.

If you are into the details, you might wonder why I simulated a square region that is only a few wavelengths wide, and why the antenna spacing is only a quarter of a wavelength. This assumption was only made for illustrative purposes. If the physical antenna locations are fixed but we would reduce the wavelength, the size of the circular region will reduce and the ripples will be more frequent. Hence, we would need to compute the channel gain at many more spatial sample points to produce a smooth plot.

Reproduce the results: The code that was used to produce the plots can be downloaded from my GitHub.

Dataset with Channel Measurements for Distributed and Co-located Massive MIMO

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:

Can Every Company Be First With 5G?

If you are following the 5G news, you might have noticed the many claims from various operators and telecom manufactures of being first with 5G. How can more than one company be first?

One telling example from this week is that on Thursday, Sprint/Nokia/Qualcomm reported about the “First 5G Data Call Using 2.5 GHz” and on Friday, Ericsson/Qualcomm reported about a “5G data call on 2.6 GHz band (…) adding a new frequency band to those successfully tested for commercial deployment.” The difference in carrier frequency is so small that I suppose the same hardware could have been used in both bands; for example, the LTE Massive MIMO product that I wrote about last August is designed for the frequency range 2496-2690 MHz. Yet, there is no contradiction between the two press releases; there are many different frequency bands and 5G features that one can be the first to demonstrate the use of, so we will likely see many more reports like these ones.


The multitude of press releases of this kind is an indicator of: 1) The many tests of soon-to-be-released hardware that are ongoing; 2) The importance for the companies to push out a steady stream of 5G related news.

When it comes to Massive MIMO, Sprint has previously showcased their use of fully digital 64-antenna panels at sub-6 GHz frequencies. In the new press release, they mention that hundreds of such panels were deployed in their network in 2018. Dr. Wen Tong, Head of Wireless Research at Huawei, made a similar claim about China in his keynote at GLOBECOM 2018. These are of course very small numbers compared to the millions of LTE base stations that exist in the world, but it indicates how important Massive MIMO will be in 5G. In fact, there are good reasons to believe that some kind of Massive MIMO technology will be used in almost every 5G base station.