Signal Processing Aspects of Cell-Free Massive MIMO

The fifth generation of mobile communication systems (5G) promises unprecedented levels of connectivity and quality of service (QoS) to satisfy the incessant growth in the number of mobile smart devices and the huge increase in data demand. One of the primary ways 5G network technology will be accomplished is through network densification, namely increasing the number of antennas per site and deploying smaller and smaller cells. Massive MIMO, where MIMO stands for multiple-input multiple-output, is widely expected to be a key enabler of 5G. This technology leverages an aggressive spatial multiplexing, from using a large number of transmitting/receiving antennas, to multiply the capacity of a wireless channel. A massive MIMO base station (BS) is equipped with a large number of antennas, much larger than the number of active users. The users are coherently served by all the antennas, in the same time-frequency resources but separated in the spatial domain by receiving very directive signals. By supporting such a highly spatially-focused transmission (precoding), massive MIMO provides higher spectral and energy efficiency, and reduces the inter-cell interference compared to existing mobile systems. The inter-cell interference is however becoming the major bottleneck as we densify the networks. It cannot be removed as long as we rely on a network-centric implementation, since the inter-cell interference concept is inherent to the cellular paradigm. Cell-free massive MIMO refers to a massive MIMO system where the BS antennas, herein referred to as access points (APs), are geographically spread out. The APs are connected, through a fronthaul network, to a central processing unit (CPU) which is responsible for coordinating the coherent joint transmission. Such a distributed architecture provides additional macro-diversity, and the co-processing at multiple APs entirely suppresses the inter-cell interference. Each user is surrounded by serving APs and experiences no cell boundaries. This user-centric approach, combined with the system scalability that characterizes the massive MIMO design, constitutes a paradigm shift compared to the conventional centralized and distributed wireless communication systems. On the other hand, such a distributed system requires higher capacity of back/front-haul connections, and the signal co-processing increases the signaling overhead. In this thesis, we focus on some signal processing aspects of cell-free massive MIMO. More specifically, we firstly investigate if the downlink channel estimation, via downlink pilots, brings gains to cell-free massive MIMO or the statistical channel state information (CSI) knowledge at the users is enough to reliably perform data decoding, as in conventional co-located massive MIMO. Allocating downlink pilots is costly resource-wise, thus we also propose resource saving-oriented strategies for downlink pilot assignment. Secondly, we study further fully distributed and scalable precoding schemes in order to outperform cell-free massive MIMO in its canonical form, which consists in single-antenna APs implementing conjugate beamforming (also known as maximum ratio transmission)