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Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5954-434X
Univ Pisa, Italy.
2020 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 19, no 1, p. 77-90Article in journal (Refereed) Published
Abstract [en]

Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2020. Vol. 19, no 1, p. 77-90
Keywords [en]
Uplink; Fading channels; Channel estimation; Network topology; Cellular networks; Beyond 5G MIMO; cell-free massive MIMO; cellular massive MIMO; uplink; AP cooperation; MMSE processing; fronthaul signaling; non-linear decoding; small-cell networks
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:liu:diva-163459DOI: 10.1109/TWC.2019.2941478ISI: 000508384000007OAI: oai:DiVA.org:liu-163459DiVA, id: diva2:1393697
Note

Funding Agencies|Excellence Center at Linkoping-Lund in Information Technology (ELLIIT); Wallenberg AI, Autonomous Systems and Software Program (WASP); University of Pisa through the Research Project CONCEPT

Available from: 2020-02-17 Created: 2020-02-17 Last updated: 2020-02-17

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