By deploying many distributed antennas instead of a few multi-antenna base stations, a more uniform communication performance can be achieved over a coverage area. The peak rates might go down but there is a much higher chance of getting a decent rate with 95% probability. This is the main motivation behind Cell-free Massive MIMO, which is the new name for Network MIMO with a large number of antennas (many more than the number of users). The key difference from conventional ultra-dense networks is that the distributed antennas are cooperating to transmit phase-coherently in the downlink and process the received uplink signals coherently. One promising way to deploy these systems is by using radio stripes.

The first papers on Cell-free Massive MIMO assumed that all antennas have access to the downlink data of all users and take part in the uplink signal detection of all users. This is both impractical and unnecessary in a large network, where each user is only physically close to a subset of the antennas. Hence, it makes practical sense that only those antennas that can reach the user with a signal power that is non-negligible compared to the thermal noise should transmit to that user and participate in the detection of its uplink data. 

I designed a framework for this 10 years ago, which I called “dynamic cooperation clusters” (DCC) and it can be readily applied to Cell-free Massive MIMO. The main idea was that every user selects which antennas should serve it in a user-centric manner, which means that any antenna subset can be selected. This stands in contrast to the conventional network-centric approach (which dominated the 4G CoMP literature) where only certain predefined disjoint groups of antennas are allowed to cooperate.

Although the DCC framework is a perfect fit for Cell-free Massive MIMO, the performance analysis that we did 10 years ago was admittedly simplified compared to what is possible with the latest methodology. We considered TDD systems that utilize reciprocity but assumed slowly fading channels that can be estimated without error, thereby avoiding pilot contamination and the computation of ergodic rates. To provide a bridge to the past, we wrote the paper “Scalable Cell-Free Massive MIMO Systems” which revisits the DCC framework in the context of Cell-free Massive MIMO, using the latest analytical methods from the Massive MIMO literature. 

Most importantly, the new paper provides an intuitive way to select the user-centric cooperation clusters based on the uplink pilot transmissions. When a user connects to the network, we suggest that the antenna with the best channel condition is given the responsibility to guarantee the user service. The user is assigned to the pilot that is least affected by pilot contamination in that particular region. Moreover, all antennas serve as many users as there are pilots; at most one user per pilot to limit the negative effect of pilot contamination. Under these assumptions, we show that the users get nearly the same rates as if all the antennas serve all users, but with greatly reduced complexity and fronthaul requirements. In conclusion, scalable and well-performing implementations of Cell-free Massive MIMO are possible!

The following video explains the main ideas:

The new Cell-Free Massive MIMO concept has its roots in the classical Network MIMO concept, and has also been given many other names over the years (e.g., coordinated multipoint). When I started my research on the topic in 2009, the standard assumption was that a set of base stations were jointly transmitting to a set of users by sharing both the data signals and their respective channel state information (CSI). In my first journal paper, we showed that one can get away with only sharing the data signals between the base stations because each one only needs local CSI (between itself and the users) to beamform to the users. The price to pay is that the base stations cannot cancel each others’ interference, so each one should preferably have multiple antennas so it can control how much interference it causes. This was my first well-cited paper but, to be honest, I am still not sure how significant results are.

On the one hand, it is very convenient to only utilize local CSI at every base station, because it can be estimated from uplink pilots in a TDD system, which was a key motivation behind our 2010 paper. The time-critical precoding computation can then be initiated immediately after the pilots have been received, instead of waiting for the CSI to be shared between the base stations. This property was later utilized in the first Cell-Free Massive MIMO papers [Ngo, Nayebi] to alleviate the need for sharing CSI.

On the other hand, CSI is usually a small fraction of the signaling between a base station and the rest of the system in Network MIMO. The majority of the signaling consists of the data signals; for example, if a coherence block with 200 channel uses consists of 20 pilot symbols and 180 data symbols, then there is 180/20 = 9 times more data than CSI. Interestingly, our recent paper “Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation” shows that if Cell-Free Massive MIMO is implemented by sending all CSI to an edge-cloud processor that takes care of all the signal processing, both the communication performance and the signaling load can be greatly improved as compared to the fully distributed approach (which was considered in my 2010 paper and then became the standard assumption in the Cell-Free Massive MIMO literature).

The bottom line is that it is hard to make a distributed network implementation competitive compared to a centralized one. Unless we can find a really clever implementation, there is a risk that we lose too much in communication performance and also raise the fronthaul capacity requirements.

In the research on Beyond Massive MIMO systems, a number of new terminologies have been introduced with names such as:

1. Reconfigurable reflectarrays;
2. Software-controlled metasurfaces;
3. Intelligent reflective surfaces.

These are basically the same things (and there are many variations on the three names), which is important to recognize so that the research community can analyze them in a joint manner.

Background

The main concept has its origin in reflectarray antennas, which is a class of directive antennas that behave a bit like parabolic reflectors but can be deployed on a flat surface, such as a wall. More precisely, a reflectarray antenna takes an incoming signal wave and reflects it into a predetermined spatial direction, as illustrated in the following figure:

Instead of relying on the physical shape of the antenna to determine the reflective properties (as is the case for parabolic reflectors), a reflectarray consists of many reflective elements that impose element-specific time delays to their reflected signals. These elements are illustrated by the dots on the surface in Figure 1. In this way, the reflected wave is beamformed and the reflectarray can be viewed as a passive MIMO array. The word passive refers to the fact that the radio signal is not generated in the array but elsewhere. Since a given time delay corresponds to a different phase shift depending on the signal’s frequency, reflectarrays are primarily useful for reflecting narrowband signals in a single direction.

Reconfigurability

Reconfigurable reflectarrays can change the time delays of each element to steer the reflected beam in different directions at different points in time. The research on this topic has been going on for decades; the book “Reflectarray Antennas: Analysis, Design, Fabrication, and Measurement” from 2014 by Shaker et al. describes many different implementation concepts and experiments.

Recently, there is a growing interest in reconfigurable reflectarrays from the communication theoretic and signal processing community. This is demonstrated by a series of new overview papers that focus on applications rather than hardware implementations:

• A New Wireless Communication Paradigm through Software-Controlled Metasurfaces by C. Liaskos et al.
• Smart Radio Environments Empowered By Reconfigurable AI Meta-Surfaces: An Idea Whose Time Has Come by M. Di Renzo, et al.
• Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network by Q. Wu and R. Zhang
• Massive MIMO is a Reality – What is Next? Five Promising Research Directions for Antenna Arrays by E. Björnson et al.

The elements in the reflecting surface in Figure 1 are called meta-atoms or reflective elements in these overview papers. The size of a meta-atom/element is smaller than the wavelength, just as for conventional low-gain antennas. In simple words, we can view an element as an antenna that captures a radio signal, keeps it inside itself for a short time period to create a time-delay, and then radiates the signal again. One can thus view it as a relay with a delay–and-forward protocol. Even if the signals are not amplified by a reconfigurable reflectarray, there is a non-negligible energy consumption related to the control protocols and the reconfigurability of the elements.

It is important to distinguish between reflecting surfaces and the concept of large intelligent surfaces with active radio transmitters/receivers, which was proposed for data transmission and positioning by Hu, Rusek, and Edfors. This is basically an active MIMO array with densely deployed antennas.

What are the prospects of the technology?

The recent overview papers describe a number of potential use cases for reconfigurable reflectarrays (metasurfaces) in wireless networks, such as range extension, improved physical layer security, wireless power transfer, and spatial modulation. From a conceptual perspective, it is indeed an exciting prospect to build future networks where not only the transmitter and receiver algorithms can be optimized, but the propagation environment can be partially controlled.

However, the research on this topic is still in its infancy. It is of paramount importance to demonstrate practically important use cases where reconfigurable reflectarrays are fundamentally better than existing methods. If it should be economically feasible to turn the technology into a commercial reality, we should not look for use cases where a 10% gain can be achieved but rather a 10x or 100x gain. This is what Marzetta demonstrated with Massive MIMO and this is also what it can deliver in 5G.

I haven’t seen any convincing demonstrations of such use cases of reflectarray antennas (metasurfaces) thus far. On the contrary, my new paper “Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?” shows that the new technology can indeed provide range extension, but a basic single-antenna decode-and-forward relay can outperform it unless the surface is very large. There is much left to do on this topic!