On the Uplink Distributed Detection in UAV-enabled Aerial Cell-Free mMIMO Systems

TL;DR

This paper proposes a two-layer distributed uplink detection scheme for UAV-enabled cell-free massive MIMO systems, improving spectral efficiency by reducing signaling overhead and optimizing UAV deployment based on environment.

Contribution

It introduces a novel distributed detection method using local MMSE detection and statistical CSI, with asymptotic analysis via free probability theory for UAV-assisted aerial cell-free MIMO.

Findings

Distributed detection reduces signaling overhead.

More UAVs improve spectral efficiency in urban areas.

Optimal UAV-to-antenna ratio exists in suburban environments.

Abstract

In this paper, we investigate the uplink signal detection approaches in the cell-free massive MIMO systems with unmanned aerial vehicles (UAVs) serving as aerial access points (APs). The ground users are equipped with multiple antennas and the ground-to-air propagation channels are subject to correlated Rician fading. To overcome huge signaling overhead in the fully-centralized detection, we propose a two-layer distributed uplink detection scheme, where the uplink signals are first detected in the AP-UAVs by using the minimum mean-squared error (MMSE) detector depending on local channel state information (CSI), and then collected and weighted combined at the CPU-UAV to obtain the refined detection. By using the operator-valued free probability theory, the asymptotic expressions of the combining weights are obtained, which only depend on the statistical CSI and show excellent accuracy. Based on the proposed distributed scheme, we further investigate the impacts of different distributed deployments on the achieved spectral efficiency (SE). Numerical results show that in urban and dense urban environments, it is more beneficial to deploy more AP-UAVs to achieve higher SE. On the other hand, in suburban environment, an optimal ratio between the number of deployed UAVs and the number of antennas per UAV exists to maximize the SE.