There are thousands of papers that analyze different aspects of Massive MIMO. Although many different algorithms and models have been considered, I would say that the most common ones are:
Independent Rayleigh fading channels;
Signal processing based on maximum ratio (MR) or zero-forcing (ZF).
These are, for example, the assumptions made in the textbook Fundamentals of Massive MIMO. The beautiful analysis and insightful closed-form expressions developed under these assumptions have had a profound impact on the adoption of Massive MIMO in 5G. I would, therefore, like to refer to this canonical form of the technology as Massive MIMO 1.0.
Taking the technology to the next level
It is possible to squeeze out even higher spectral efficiency out of multi-antenna systems if we design the systems differently. For example, the paper “Massive MIMO has unlimited capacity” showed that the upper limit on the capacity that appears in Massive MIMO 1.0, due to pilot contamination, can be alleviated by replacing the two above-mentioned assumptions by:
Spatially correlated Rayleigh fading;
Signal processing that cancels interference between the pilot-sharing users.
Spatial correlation is something that appears naturally in all communication systems, thus the main difference is to embrace this fact in the signal processing design instead of neglecting it. I believe that this can make such as a huge difference that it is appropriate to introduce the term Massive MIMO 2.0 to describe this development.
This is done in a recent review paper called “Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination“. The paper’s main conclusion is that the acquisition and utilization of spatial correlation information will be key in beyond-5G systems, to take the spectral efficiency to the next level. Since the largest gains appear when having even larger antenna arrays than in 5G, new antenna deployments concepts are bound to arise. Three promising examples are described in the paper: large intelligent surfaces, distributed post-cellular architectures, and the use of carrier frequencies beyond 100 GHz.
As a complement to the review paper, the basics of Massive MIMO 2.0 are also described in the following video:
As this decade is approaching its end, so is the development of 5G technologies. The first 5G networks are currently begin deployed and, over the next few years, we will learn which features in the 5G standards that will actually be used and provide good performance.
When it comes to Massive MIMO for sub-6 GHz and mmWave bands, many of the previously open research problems have been resolved over the past five years – at least from an academic perspective. There are still important open problems at the border between theory and practical implementation. However, I strongly believe that this is a time when we should also look further into the future to identify the next big things.
To encourage more future-looking research, I joined as one of the guest editors of an upcoming special issue on Multiple Antenna Technologies for Beyond 5G in the IEEE Journal on Selected Areas in Communications (JSAC). The call for papers is available online and the submission deadline is 1 September 2019. Hence, if you start your research on this topic right away, you will have plenty of time to write a paper!
The call for papers identifies three promising directions: Cell-free Massive MIMO, Lens arrays, and Large intelligent surfaces. However, I am sure there are many other interesting research directions that are yet to be discovered. I recommend prospective authors to think creatively and look for the next big steps in the multiple antenna technologies. Remember that Massive MIMO was generally viewed as science fiction ten years ago, and now it is a reality!
Distributed MIMO deployments combine the best of two worlds: The beamforming gain and spatial interference suppression capability of conventional Massive MIMO with co-located arrays, and the bigger chance of being physically close to a service antenna that small cells offer. Coherent transmission and reception from a distributed MIMO array is not a new concept but has been given many names over the years, including Distributed Antenna System and Network MIMO. Most recently, in the beyond-5G era, it has been called ubiquitous Cell-free Massive MIMO communications and been refined based on insights and methodology developed through the research into conventional Massive MIMO.
One of the showstoppers for distributed MIMO has always been the high cost of deploying a large number of distributed antennas. Since the antennas need to be phase-synchronized and have access to the same data, a lot of high-capacity cables need to be deployed, particularly if a star topology is used. Ericsson is showcasing their new take on distributed MIMO at the 2019 Mobile World Congress (MWC), which is taking place in Barcelona this week. It is called radio stripes and some details can be found in a recent press release. In particular, Jan Hederén, strategist at Ericsson 4G5G Development, says:
“Although a large-scale installation of distributed MIMO can provide excellent performance, it can also become an impractical and costly ‘spaghetti-monster’ of cables in case dedicated cables are used to connect the antenna elements. To be easy to deploy, we need to connect and integrate the antenna elements inside a single cable. We call this solution the ‘radio stripe’ which is an easy way to create a large scale distributed, serial, and integrated antenna system.”
One more reason to attend the IEEE CTW 2019: Participate in the Molecular MIMO competition! There is a USD 500 award to the winning team.
The task is to design a molecular MIMO communication detection method using datasets that contain real measurements. Possible solutions may include classic approaches (e.g., thresholding-based detection) as well as deep learning-based approaches.
Ever since I finished the writing of the book Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency, I have felt that I’m somewhat done with my research on conventional Massive MIMO. The spectral efficiency, energy efficiency, resource allocation, and pilot contamination phenomenon are well understood by now. This is not a bad thing—as researchers, we are supposed to solve the problems we are analyzing. But it means that this is a good time to look for new research directions. It should preferably be something where we can utilize our skills as Massive MIMO researchers to do something new and exciting!
With this in mind, I gathered a team consisting of myself, Luca Sanguinetti, Henk Wymeersch, Jakob Hoydis, and Thomas L. Marzetta. Each one of us has written about one promising new direction of research related to antenna arrays and MIMO, including the background of the topic, our long-term vision, and pertinent open problem. This resulted in the paper:
The object of the competition is to design and train an algorithm that can determine the position of a user, based on estimated channel frequency responses between the user and an antenna array. Possible solutions may build on classic algorithms (fingerprinting, interpolation) or machine-learning approaches. Channel vectors from a dataset created with a MIMO channel sounder will be used.
Competing teams should present a poster at the conference, describing their algorithms and experiments.
A $500 USD prize will be awarded to the winning team.
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.