Performance Analysis of Cell Free Massive MIMO systems in LoS/ NLoS Channels

TL;DR

This paper investigates the uplink performance of cell-free massive MIMO systems with probabilistic LoS channels, deriving new channel statistics, comparing decoding schemes, and validating results through simulations.

Contribution

It introduces a probabilistic LoS channel model for CF massive MIMO, derives its effective statistics, and compares different data detection methods.

Findings

LoS probability significantly impacts system performance.

Joint decoding outperforms stream-wise decoding.

MMSE detection offers a good complexity-performance balance.

Abstract

In cellular communication systems, it is conventional to assume the absence of a line of sight (LoS) path between the users and their associated access points (APs). This assumption however becomes questionable in the context of recent developments in the direction of cell free (CF) massive MIMO systems. In the CF massive MIMO, the AP density is assumed to be comparable with the user density, which increases probability of existence of an LoS path between the users and their associated APs. In this paper, we analyze the performance of an uplink CF massive MIMO system, with a probabilistic LoS channel model. Here, we first derive the effective statistics of this channel model, and argue that their behaviour is fundamentally different from that of the conventional rich scattering channels. Utilizing these statistics, we next compare the rates achievable by CF massive MIMO systems, under both stream-wise and joint decoding at the central processing unit. Following this, we also discuss the centralized MMSE based data detection to obtain a complexity/ performance trade-off. Finally, using detailed Monte-Carlo simulations, we validate our analytical results, and evaluate the performance of the three data detection schemes.