Category Archives: Beyond 5G

Real-time Reconfigurable Metasurfaces

I have written several blog posts about how the hardware concepts of reconfigurable reflectarrays and metasurfaces are gaining interest in the wireless communication community, for example, to create a new type of full-duplex transparent relays. The technology is also known as reconfigurable intelligent surfaces and intelligent reflecting surfaces.

In my latest magazine paper, I identified real-time reconfigurability as the key technical research challenge: we need fast algorithms for over-the-air channel estimation that can handle large surfaces and complex propagation environments. In other words, we need hardware that can be reconfigured and algorithms to find the right configuration.

The literature contains several theoretical algorithms but it is a very different thing to demonstrate real-time reconfigurability in lab experiments. I was therefore impressed when finding the following video from the team of Dr. Mohsen Khalily at the University of Surrey:

The video shows how a metasurface is used to reflect a signal from a transmitter to a receiver. In the second half of the video, they move the receiver out of the reflected beam from the metasurface and then press a button to reconfigure the surface to change the direction of the beam.

I asked Dr. Khalily to tell me more about the setup:

“The metasurface consists of several conductive printed patches (scatterers), and the size of each scatterer is a small proportion of the wavelength of the operating frequency. The macroscopic effect of these scatterers defines a specific surface impedance and by controlling this surface impedance, the reflected wave from the metasurface sheet can be manipulated. Each individual scatterer or a cluster of them can be tuned in such a way that the whole surface can reconstruct radio waves with desired characteristics without emitting any additional waves.”

The surface shown in the video contains 2490 patches that are printed on a copper ground plane. The patches are made of a new micro-dispersed ceramic PTFE composite and designed to support a wide range of phase variations along with a low reflection loss for signals in the 3.5 GHz band. The design of the surface was the main challenge according to Dr. Khalily:

Fabrication was very difficult due to the size of the surface, so we had to divide the surface into six tiles then attach them together. Our surface material has a higher dielectric constant than the traditional PTFE copper-clad laminates to meet the design and manufacturing of circuit miniaturization. This material also possesses high thermal conductivity, which gives an added advantage for heat dissipation of the apparatus.”

The transmitter and receiver were in the far-field of the metasurface in the considered experimental setup. Since there is an unobstructed line-of-sight path, it was sufficient to estimate the angular difference between the receiver and the main reflection angle, and then adjust the surface impedance to compensate for the difference. When this was properly done, the metasurface improved the signal-to-noise ratio (SNR) by almost 15 dB. I cannot judge how close this number is to the theoretical maximum. In the considered in-room setup with highly directional horn antennas at the transmitter and receiver, it might be enough that the reflected beam points in roughly the right direction to achieve a great SNR gain. I’m looking forward to learning more about this experiment when there is a technical paper that describes it.

This is not the first experiment of this kind, but I think it constitutes the state-of-the-art when it comes to bringing the concept of reconfigurable intelligent surfaces from theory to practice.

Who is Who in Massive MIMO?

I taught a course on complex networks this fall, and one component of the course is a hands-on session where students use the SNAP C++ and Python libraries for graph analysis, and Gephi for visualization. One available dataset is DBLP, a large publication database in computer science, that actually includes a lot of electrical engineering as well.

In a small experiment I filtered DBLP for papers with both “massive” and “MIMO” in the title, and analyzed the resulting co-author graph. There are 17200 papers and some 6000 authors.  There is a large connected component, with over 400 additional much smaller connected components!

Then I looked more closely at authors who have written at least 20 papers. Each node is an author, its size is proportional to his/her number of “massive MIMO papers”, and its color represents identified communities. Edge thicknesses represent the number of co-authored papers.  Some long-standing collaborators, former students, and other friends stand out.  (Click on the figure to enlarge it.)

To remind readers of the obvious, prolificacy is not the same as impact, even though they are often correlated. Also, the study is not entirely rigorous. For one thing, it trusts that DBLP properly distinguishes authors with the same name (consider e.g., “Li Li”) and I do not know how well it really does that. Second, in a random inspection all papers I had filtered out dealt with “massive MIMO” as we know it. However, theoretically, the search criterion would also catch papers on, say, MIMO control theory for a massive power plant.  Also, the filtering does miss some papers written before the “massive MIMO” term was established, perhaps most importantly Thomas Marzetta’s seminal paper on “unlimited antennas”.  Third, the analysis is limited to publications covered by DBLP, which also means, conversely, that there is no specific quality threshold for the publication venues. Anyone interested in learning more, drop me a note. 

Globecom Tutorial on Cell-free Massive MIMO

I am giving a tutorial on “Beyond Massive MIMO: User-Centric Cell-Free Massive MIMO” at Globecom 2020, together with my colleagues Luca Sanguinetti and Özlem Tuğfe Demir. It is a prerecorded 3-hour tutorial that can be viewed online at any time during the conference and there will be a live Q/A session on December 11 where we are available for questions.

The tutorial is based on our upcoming book on the topic: Foundations on User-Centric Cell-free Massive MIMO.

Until December 11 (the last day of the tutorial), we are offering a free preprint of the book, which can be downloaded by creating an account at the NOW publishers’ website. By doing so, I think you will also get notified when the final version of the book is available early next year, so you can gain access to the final PDF and an offer to buy printed copies.

If you download the book and have any feedback that we can take into account when preparing the final version, we will highly appreciate to receive it! Please email me your feedback by December 15. You find the address in the PDF.

The abstract of the tutorial is as follows:

Massive MIMO (multiple-input multiple-output) is no longer a promising concept for cellular networks-in 2019 5G it became a reality, with 64-antenna fully digital base stations being commercially deployed in many countries. However, this is not the final destination in a world where ubiquitous wireless access is in demand by an increasing population. It is, therefore, time for MIMO and mmWave communication researchers to consider new multi-antenna technologies that might lay the foundations for beyond 5G networks. In particular, we need to focus on improving the uniformity of the service quality.

Suppose all the base station antennas are distributed over the coverage area instead of co-located in arrays at a few elevated locations, so that the mobile terminals are surrounded by antennas instead of having a few base stations surrounded by mobile terminals. How can we operate such a network? The ideal solution is to let each mobile terminal be served by coherent joint transmission and reception from all the antennas that can make a non-negligible impact on their performance. That effectively leads to a user-centric post-cellular network architecture, called “User-Centric Cell-Free Massive MIMO”. Recent papers have developed innovative signal processing and radio resource allocation algorithms to make this new technology possible, and the industry has taken steps towards implementation. Substantial performance gains compared to small-cell networks (where each distributed antenna operates autonomously) and cellular Massive MIMO have been demonstrated in numerical studies, particularly, when it comes to the uniformity of the achievable data rates over the coverage area.

Episode 3: Reconfigurable Intelligent Surfaces

We have now released the third episode of the podcast Wireless Future, with the following abstract:

The research towards 6G has already been initiated. One of the most hyped concepts in the research community is “reconfigurable intelligent surfaces”, which can be utilized to create smart walls that capture wireless signals and reflect them towards the user device. In this episode, Erik G. Larsson and Emil Björnson discuss the prospects and limitations of this new technology. Is it the next big thing in wireless? To learn more, they recommend their new overview article “Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions”, to appear in IEEE Communications Magazine, which can be downloaded at https://arxiv.org/pdf/2006.03377.

You can watch the video podcast on YouTube:

You can listen to the audio-only podcast at the following places:

Episode 2: Myths About Massive MIMO

We have now released the second episode of the podcast Wireless Future, with the following abstract:

There are often hypes and speculations around new wireless technologies, including “Massive MIMO”, which is the key new feature in 5G. In 2015, Emil Björnson and Erik G. Larsson wrote the article “Massive MIMO: Ten Myths and One Critical Question” together with Thomas Marzetta. It was an attempt to dispel some of the misconceptions that were floating around at the time. In this episode, they look back at the statements they claimed to be myths to see if they were right and whether the myths are still around. The article received the 2019 Fred W. Ellersick Prize from the IEEE Communications Society and can be downloaded at https://arxiv.org/pdf/1503.06854.

You can watch the video podcast on YouTube:

You can listen to the audio-only podcast at the following places:

New Podcast: Wireless Future

I am excited to announce the new podcast “Wireless Future“, where Emil Björnson and Erik G. Larsson are discussing current and future wireless technologies, as well as their impact on society. Each episode will focus on a particular topic and be available in two formats: A video podcast on YouTube and an audio-only podcast that can be downloaded from the major podcast apps (there is a list below). We intend to release one episode every other week, starting from today. We hope you will enjoy it! Please send us feedback, questions, and suggestions on future topics to podcast@ebjornson.com.

Episode 1: Massive MIMO: Where do we stand?

In the first episode of “Wireless Future”, Erik G. Larsson and Emil Björnson talk about the brand new 5G networks and what role the technology component “Massive MIMO” is playing. They reflect upon whether the practical implementation of the technology became as they envisioned in their textbooks “Fundamentals of Massive MIMO” and “Massive MIMO Networks”.

You can listen to the audio-only podcast at the following places:

The End of Independent Rayleigh Fading

When researchers study the basic properties of multi-antenna technologies, it is a common practice to model the channels using independent and identically distributed (i.i.d.) Rayleigh fading. This practice goes back many decades and is convenient since: 1) every antenna observes an independent realization of the channel; 2) each antenna is statistically equally good, so the ordering doesn’t matter; 3) the channel coefficients are complex Gaussian distributed, which leads to convenient mathematics.

The i.i.d. Rayleigh fading model has become the baseline that is considered unless the research is explicitly focused on a different model. When using the model to study spatial diversity, the diversity gain becomes proportional to the number of antennas. When characterizing the ergodic capacity of Massive MIMO, one can derive simple closed-form bounds where the SINR is proportional to the number of antennas. Both results are correct, but their generality is limited by the generality of the underlying fading model. Hence, it is important to know under what conditions i.i.d. Rayleigh fading can be observed.

When i.i.d. fading might occur

In isotropic scattering environments, where the multi-path components are uniformly distributed over all directions (in three dimensions), the fading realizations observed at two points have a correlation determined by the distance d between them. More precisely, the cross-correlation is sinc(2d/λ), where λ is the wavelength. The sinc function is zero when the argument is a non-zero integer, thus the fading realizations at two different points are uncorrelated if and only if they are separated by an integer multiple of λ/2. For example, d = λ/2, λ, 3λ/2, etc. Since the channel coefficients are Gaussian distributed in isotropic fading, uncorrelated fading results in independent fading.

The figure above illustrates a setup where 3 antennas are deployed on the dashed line with a separation of λ/2. The red circles around the “red antenna” show at which locations one can observe fading realizations that are independent of the observation made at the red antenna. The circles have radius λ/2, λ, 3λ/2, etc. The blue and green circles have the same meanings for the blue and green antennas, respectively. Since all the antennas are deployed on the circles of the other antennas, they will observe mutually uncorrelated (independent) fading. This will give rise to i.i.d. Rayleigh fading.

Suppose we want to deploy a fourth antenna. To retain an i.i.d. fading distribution, we must put it at a point where a red, a blue, and a green circle intersect. As indicated by the figure, such points are only be found along the dashed line. Hence, a uniform linear array (ULA) with λ/2-separation between the adjacent antennas will observe i.i.d. fading if deployed in an isotropic scattering environment.

When i.i.d. fading cannot occur

Apart from the ULA example, there is essentially no other case where i.i.d. fading can occur. This is important since two-dimensional planar arrays are becoming standard, for example, when deploying Massive MIMO in cellular networks. Even if we allow ourselves to deviate from the isotropic scattering assumption, any physically accurate stochastic channel model for planar arrays exhibits correlation. This is proved in the paper “Spatially-Stationary Model for Holographic MIMO Small-Scale Fading“.

The horizontal and vertical ULAs in the figure above can observe i.i.d. fading, while the planar array cannot; even if the horizontal and vertical antenna spacing is λ/2, the spacings along the diagonals are different.

Looking further into the future, two new array concepts are currently receiving attention from the research community:

  1. Large intelligent surfaces (LIS);
  2. Reconfigurable intelligent surfaces (RIS).

LIS are large active arrays, while RIS are large passive arrays with elements that scatter incident signals in a semi-controllable fashion. In both cases, the word “surface” signifies that at a planar array, or even a three-dimensional array, is considered. Hence, these arrays can never observe i.i.d. fading—it is physically impossible. Moreover, a key characteristic of LIS and RIS is that the element spacing is smaller than λ/2 (to approximate a continuously controllable surface), which is yet another reason for obtaining spatial channel correlation. It is therefore worrying that several early papers on these topics are making use of the i.i.d. fading model: the analysis might be beautiful but the results are insignificant since they cannot be observed in practice.

The way forward

Even if we have reached the end of the road for the i.i.d. Rayleigh fading model, we don’t have to wander into the darkness. We just need to switch to utilizing the more general spatially correlated Rayleigh fading model. There is already a rich literature on how to design communication systems for such channels. My book “Massive MIMO networks” is one possible starting point, but not the only one.

To make the transition to physically accurate models easier, I have co-authored the paper “Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces“, which derives a spatial correlation model for LIS and RIS in isotropic scattering environments. It can take the role as the new baseline channel model that is used when no other specific channel model is studied. We also elaborate on why the classical “Kronecker approximation” of spatial correlation matrices is inaccurate; for example, it results in i.i.d. fading also for planar arrays.