Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks

TL;DR

This paper tackles robust downlink power allocation in user-centric cell-free massive MIMO under imperfect CSI. It reformulates the problem as a robust least-squares optimization with a Tikhonov regularization term, yielding a closed-form solution for the per-symbol power vector and integrating zero-forcing precoding. A principled setting of the regularization parameter ties robustness to the CSI error bound, while a projection step enforces power and non-negativity constraints. Empirical results show the proposed RLSPA method outperforms non-robust baselines with moderate computational complexity, indicating strong applicability for large-scale CF-mMIMO deployments under CSI uncertainty.

Abstract

In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncertainties resulting from imperfect channel state information (CSI). In this work, we propose a robust power allocation method that formulates the power optimization problem into a least-squares framework, enhanced by Tikhonov regularization to mitigate the adverse effects of channel estimation errors. We integrate our approach with zero-forcing precoding, enabling a design that is both computationally efficient and resilient to CSI imperfections. Numerical results indicate that the proposed method outperforms existing non-robust techniques while benefiting from low computational overhead, making it well-suited for large-scale deployments under CSI uncertainty.

Figures (2)

• Figure 1: Sum-rate comparison of RLSPA, RGDPA, and GDPA with $\alpha=0.15$, $L=25$, $N=4$, $K=200$, $n=25$, and ZF precoding.
• Figure 2: Complexity comparison of RLSPA, RGDPA, and GDPA in terms of FLOPs when 30 iterations are considered and $N_{\rm sym}=175$.