Scalable Cell-Free Massive MIMO Systems

2019/08/31 by Emil Björnson, Emil Bjornson, Luca Sanguinetti · 937 citations
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Cellular network #Channel (broadcasting) #Computer network #Computer science #Cooperative Communication and Network Coding #Distributed computing #Energy Harvesting in Wireless Networks #Interference (communication) #MIMO #Multi-user MIMO #Precoding #Scalability #Telecommunications link #Zero-forcing precoding #cs.IT #eess.SP #math.IT

paper · pdf · doi:10.1109/tcomm.2020.2987311

published in IEEE Transactions on Communications 68(7), 4247-4261 (IEEE Communications Society) · To appear in IEEE Transactions on Communications, 14 pages, 6 figures

openalex publication_date 2020/04/14 · arxiv created 2020/05/08 · arxiv updated 2020/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide a novel algorithm for joint initial access, pilot assignment, and cluster formation that is proved to be scalable. Moreover, we adapt the standard channel estimation, precoding, and combining methods to become scalable. A new uplink and downlink duality is proved and used to heuristically design the precoding vectors on the basis of the combining vectors. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio (MR) processing and also performs closely to the best unscalable alternatives.

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