Category Archives: 5G

Active and Passive Antennas

If you are an academic physical-layer researcher, like me, you might be used to treating the base station as a single unit that takes a digital data signal as input and then outputs an electromagnetic radio wave (or the opposite in the uplink). The reality is quite different, or at least it used to be.

A traditional base station consists of three main components: a baseband unit (BBU) that takes care of digital signal processing, a radio unit that creates the analog radio-frequency (RF) signal, and a passive antenna that emits the RF signals with a constant radiation pattern. Due to the size and weight constraints of masts and towers, the radio and BBU are deployed underneath and there is a long RF feeder cable between the antenna and radio, resulting in substantial power losses. This is illustrated as “Step 1” in the figure below. A single BBU can support multiple radios that are deployed on the same site, which might cover different frequency bands or cell sectors (this is not illustrated).

Figure: The evolution of base station technology has gone through three main steps. In Step 1, the antenna is in the mast while the radio and BBU are underneath. The short blue cable sends digital baseband signals while the long purple cable sends analog RF signals. In Step 2, the radio is next to the antenna, so the purple RF cable is shorter. In Step 3, the antenna and radio are integrated into a single box. Multiple antennas and radios can be contained in the same box, which is called an AAS. The BBU can either be located underneath the AAS or “in the cloud”.

Now when the radio hardware has reduced in size, it is common to use remote radio units that are deployed in the tower, close to the antenna instead of close to the BBU. This is denoted as “Step 2” in the figure above and became common in the 4G era. Only a short RF feeder cable is then needed, while an optical fiber can be drawn from the BBU to the radio. The next step in the development is active antennas that integrate the antenna and radio into a single unit. There are many types of active antennas, from single-antenna units with constant radiation patterns to Massive MIMO antennas that adapt the radiation patterns by beamforming. To distinguish these things, the term advanced antenna system (AAS) is being used in the industry to refer to active Massive MIMO antenna arrays. This setup is denoted as “Step 3” in the figure and is becoming the dominating approach in the 5G era. To limit the capacity of the optical fiber between the AAS and BBU, an AAS might perform a limited set of baseband processing to compress/decompress the signals.

In summary, the latest radio-integrated active antennas are quite similar to what physical-layer researchers have been imaging for a while: A single unit that takes digital signals as input and emits an RF signal. Small cells can even include the BBU in the active antenna, while macro cell deployments purposely keep the BBU separate so it can be shared between multiple active antennas (it can even be moved to a nearby “cloud” computer). The advent of AAS technology is a key enabling factor for Massive MIMO deployment; a single box with 64 antennas and 64 radios can be made rather compact, while a deployment with 64 separate antenna boxes, 64 separate radio units, and an equal number of cables wouldn’t make practical sense.

Cross-Talk in MIMO Transmitters (In Memoriam of Professor Peter Händel)

I received an email in late August 2019 from my former boss at KTH, Professor Peter Händel. He had been working for many years on the modeling of hardware imperfections in wireless transceivers. Our research journeys had recently crossed since he had written several papers on the modeling of imperfections in MIMO transmitters and their impact on communication performance. I have been working on similar things but using far less sophisticated models.

The essence of the email was that he wanted us to write a paper together, but the circumstances came as a chock. Peter had been sick for a while and it turned out to be a terminal illness. He asked me to finalize a manuscript that he had initiated. I agreed and we exchanged a few emails but just as I and my postdoc were about to begin the editing of Peter’s manuscript, he passed away on September 15, 2019.

Impact of Backward Crosstalk in MIMO Transmitters

The manuscript considers a type of hardware impairment called backward crosstalk. It can be a major issue in the design of multi-antenna transmitters, but is generally overlooked by communication engineers. The issue arises when you build an antenna-integrated radio, for example, a Massive MIMO array with many antenna elements, power amplifiers, and radio-signal generators in a compact box. In this case, the output signal from one power amplifier will leak into the inputs of the neighboring power amplifiers. Even if the leakage is small in relative terms, it can still have a non-negligible impact since the output power of an amplifier is much higher than the input power. A small fraction of a large power value can still be rather large. In addition to this kind of backward crosstalk between amplifiers, there is also forward crosstalk in practice but it can be neglected for the very same reason.

This figure from the figure illustrates how the output signals r1, r2 from two neighboring power amplifiers are leaking into each other. The variables κ1, κ2 are representing the strength of this backward crosstalk.

We managed to finalize the manuscript, thanks to the excellent work by my postdoc Özlem Tuğfe Demir. The paper is now available:

Peter Händel, Özlem Tuğfe Demir, Emil Björnson, Daniel Rönnow, “Impact of Backward Crosstalk in 2×2 MIMO Transmitters on NMSE and Spectral Efficiency,” IEEE Transactions on Communications, vol. 68, no. 7, pp. 4277-4292, July 2020.

The paper considers a system model containing backward crosstalk, as well as, power amplifier non-linearities and transmitter noise. We characterize the performance both at the transmitter side (the normalized mean-squared error) and at the receiver side (the spectral efficiency). In turns out that optimization based on these two metrics can lead to very different transmission strategies; from a spectral efficiency perspective, one can transmit at higher power and accept a higher level of distortion since the desired signal power is also growing. In the paper, we also demonstrate how the precoding can be adapted to partially compensate for the crosstalk.

This paper is just a first step towards modeling real hardware imperfections that are normally ignored in academia or lumped together into a single additive term characterized by the error-vector magnitude. In the last emails I received from Peter, he expressed his view that there is a lot of open problems to solve at the interface between proper modeling of communication hardware and the design of signal processing schemes. I agree with him and encourage anyone who is looking for open problems on MIMO communications to have a closer look at his final papers on this topic:

(I wrote this blog post in memoriam of Professor Peter Händel, who would have become 58 years today.)

Even Higher Spectral Efficiency in Massive MIMO Trials

There are basically two approaches to achieve high data rates in 5G: One can make use of huge bandwidths in mmWave bands or use Massive MIMO to spatially multiplex many users in the conventional sub-6 GHz bands.

As I wrote back in June, I am more impressed by the latter approach since it is more spectrally efficient and requires more technically advanced signal processing. I was comparing the 4.7 Gbps that Nokia had demonstrated over an 840 MHz mmWave band with the 3.7 Gbps that Huawei had demonstrated over 100 MHz of the sub-6 GHz spectrum. The former example achieves a spectral efficiency of 5.6 bps/Hz while the latter achieves 37 bps/Hz.

T-Mobile and Ericsson recently described a new field trial with even more impressive results. They made use of 100 MHz in the 2.5 GHz band and achieved 5.6 Gbps, corresponding to a spectral efficiency of 56 bps/Hz; an order-of-magnitude more than one can expect in mmWave bands!

The press release describes that the high data rate was achieved using a 64-antenna base station, similar to the product that I described earlier. Eight smartphones were served by spatially multiplexing and each received two parallel data streams (so-called layers). Hence, each user device obtained around 700 Mbps. On average, each of the 16 layers had a spectral efficiency of 3.5 bps/Hz, thus 16-QAM was probably utilized in the transmission.

I think these numbers are representative of what 5G can deliver in good coverage conditions. Hopefully, Massive MIMO based 5G networks will soon be commercially available in your country as well.

Machine Learning for Massive MIMO Communications

Since the pandemic made it hard to travel over the world, several open online seminar series have appeared with focus on different research topics. The idea seems to be to give researchers a platform to attend talks by international experts and enable open discussions.

There is a “One World Signal Processing Seminar Series” series, which has partially considered topics on multi-antenna communications. I want to highlight one such seminar. Professor Wei Yu (University of Toronto) is talking about Machine Learning for Massive MIMO Communications. The video contains a 45 minute long presentation plus another 30 minutes where questions are being answered.

There are also several other seminars in the series. For example, I gave a talk myself on “A programmable wireless world with reconfigurable intelligent surfaces“. On August 24, Prof. David Gesbert will talk about “Learning to team play”.

Chasing Data Rate Records

5G networks are supposed to be fast, to provide higher data rates than ever before. While indoor experiments have demonstrated huge data rates in the past, this has been the year where the vendors are competing in setting new data rate records in real deployments.

Nokia achieved 4.7 Gbps in an unnamed carrier’s cellular network in the USA in May 2020. This was achieved by dual connectivity where a user device simultaneously used 800 MHz of mmWave spectrum in 5G and 40 MHz of 4G spectrum.

The data rate with the Nokia equipment was higher than the 4.3 Gbps that Ericsson demonstrated in February 2020, but they “only” used 800 MHz of mmWave spectrum. While there are no details on how the 4.7 Gbps was divided between the mmWave and LTE bands, it is likely that Ericsson and Nokia achieved roughly the same data rate over the mmWave bands. The main new aspect was rather the dual connectivity between 4G and 5G.

The high data rates in these experiments are enabled by the abundant spectrum, while the spectral efficiency is only 5.4 bps/Hz. This can be achieved by 64-QAM modulation and high-rate channel coding, a combination of modulation and coding that was available already in LTE. From a technology standpoint, I am more impressed by reports of 3.7 Gbps being achieved over only 100 MHz of bandwidth, because then the spectral efficiency is 37 bps/Hz. That can be achieved in conventional sub-6 GHz bands which have better coverage and, thus, a more consistent 5G service quality.

How “Massive” are the Current Massive MIMO Base Stations?

I have written earlier that the Massive MIMO base stations that have been deployed by Sprint, and other operators, are very capable from a hardware perspective. They are equipped with 64 fully digital antennas, have a rather compact form factor, and can handle wide bandwidths in the 2-3 GHz bands. These facts are supported by documentation that can be accessed in the FCC databases.

However, we can only guess what is going on under the hood – what kind of signal processing algorithms have been implemented and how they perform compared to ideal cases described in the academic literature. Erik G. Larsson recently wrote about how Nokia improved its base station equipment via a software upgrade. Are the latest base stations now as “Massive MIMO”-like as they can become?

My guess is that there is still room for substantial improvements. The following joint video from Sprint and Nokia explains how their latest base stations are running 4G and 5G simultaneously on the same 64-antenna base station and are able to multiplex 16 layers.

This is the highest number of multiuser MIMO layers achieved in the US” according to the speaker. But if you listen carefully, they are actually sending 8 layers on 4G and 8 layers 5G. That doesn’t sum up to 16 layers! The things called layers in 3GPP are signals that are transmitted simultaneously in the same band, but with different spatial directivity. In every part of the spectrum, there are only 8 spatially multiplexed layers in the setup considered in the video.

It is indeed impressive that Sprint can simultaneously deliver around 670 Mbit/s per user to 4 users in the cell, according to the video. However, the spectral efficiency per cell is “only” 22.5 bit/s/Hz, which can be compared to the 33 bit/s/Hz that was achieved in real-world trials by Optus and Huawei in 2017.

Both numbers are far from the world record in spectral efficiency of 145.6 bit/s/Hz that was achieved in a lab environment in Bristol, in a collaboration between the universities in Bristol and Lund. Although we cannot expect to reach those numbers in real-world urban deployments, I believe we can reach higher numbers by building 64-antenna arrays with a different form factor: long linear arrays instead of compact square panels. Since most users are separable in terms of having different azimuth angles to the base station, it will be easier to separate them by sending “narrower” beams in the horizontal domain.

Record 5G capacity via software upgrade!

In the news: Nokia delivers record 5G capacity gains through a software upgrade.   No surprise!  We expected, years ago, this would happen.

What does this software upgrade consist of?  I can only speculate.  It is, in all likelihood, more than the usual (and endless) operating system bugfixes we habitually think of as “software upgrades”.   Could it be even something that goes to the core of what massive MIMO is?  Replacing eigen-beamforming with true reciprocity-based beamforming?! Who knows. Replacing maximum-ratio processing with zero-forcing combining?!  Or even more mind-boggling, implementing more sophisticated processing of the sort that has been stuffing the academic journals in the last years? We don’t know!  But it will certainly be interesting to find out at some point, and it seems safe to assume that this race will continue.  

A lot of improvement could be achieved over the baseline canonical massive MIMO processing. One could, for example, exploit fading correlation, develop improved power control algorithms or implement algorithms that learn the propagation environment, autonomously adapt, and predict the channels.  

It might seem that research already squeezed every drop out of the physical layer, but I do not think so.  Huge gains likely remain to be harvested when resources are tight, and especially we are limited by coherence: high carriers means short coherence, and high mobility might mean almost no coherence at all.  When the system is starved of coherence, then even winning a couple of samples on the pilot channel means a lot.  Room for new elegant theory in “closed form”?  Good question. Could sound heartbreaking, but maybe we have to give up on that.  Room for useful algorithms and innovation? Certainly yes.  A lot.  The race will continue.