Distributed Computation of A Posteriori Bit Likelihood Ratios in Cell-Free Massive MIMO

TL;DR

This paper introduces a decentralized method for computing a posteriori bit likelihood ratios in cell-free massive MIMO systems, enabling efficient soft detection through local processing and sequential fusion of statistics.

Contribution

It proposes a novel distributed approach for soft detection in cell-free massive MIMO, reducing centralized processing and enabling local computation of likelihood ratios.

Findings

Efficient algorithms for local computation of partial statistics.

Analytical derivation of distributed LLR computation.

Improved scalability in cell-free massive MIMO detection.

Abstract

This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forwards the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically.