URLLC with Massive MIMO: Analysis and Design at Finite Blocklength
Journal article, 2021

The fast adoption of Massive MIMO for high-throughput communications was enabled by many research contributions mostly relying on infinite-blocklength information-theoretic bounds. This makes it hard to assess the suitability of Massive MIMO for ultra-reliable low-latency communications (URLLC) operating with short-blocklength codes. This paper provides a rigorous framework for the characterization and numerical evaluation (using the saddlepoint approximation) of the error probability achievable in the uplink and downlink of Massive MIMO at finite blocklength. The framework encompasses imperfect channel state information, pilot contamination, spatially correlated channels, and arbitrary linear spatial processing. In line with previous results based on infinite-blocklength bounds, we prove that, with minimum mean-square error (MMSE) processing and spatially correlated channels, the error probability at finite blocklength goes to zero as the number M of antennas grows to infinity, even under pilot contamination. However, numerical results for a practical URLLC network setup involving a base station with M = 100 antennas, show that a target error probability of 10-5 can be achieved with MMSE processing, uniformly over each cell, only if orthogonal pilot sequences are assigned to all the users in the network. Maximum ratio processing does not suffice.

asymptotic analysis

MR and MMSE processing

ultra-reliable low-latency communications

pilot contamination

finite blocklength information theory

Massive MIMO

saddlepoint approximation

outage probability


Johan Östman

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Alejandro Lancho Serrano

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Giuseppe Durisi

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Luca Sanguinetti

University of Pisa

IEEE Transactions on Wireless Communications

15361276 (ISSN) 15582248 (eISSN)

Vol. 20 10 6387-6401 21126691

SWIFT : short-packet wireless information theory

Swedish Research Council (VR) (2016-03293), 2017-01-01 -- 2020-12-31.

Subject Categories


Communication Systems

Signal Processing



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