D2BF - Data-Driven Beamforming in MU-MIMO with Channel Estimation Uncertainty

Abstract

Accurate estimation of Channel State Information (CSI) is essential to design MU-MIMO beamforming. However, errors in CSI estimation are inevitable in practice. State-of-the-art works model CSI as random variables and assume certain specific distributions or worst-case boundaries, both of which suffer performance issues when providing performance guarantees to the users. In contrast, this paper proposes a Data-Driven Beamforming (D2BF) that directly handles the available CSI data samples (without assuming any particular distributions). Specifically, we employ chance-constrained programming (CCP) to provide probabilistic data rate guarantees to the users and introduce ∞-Wasserstein ambiguity set to bridge the unknown CSI distribution with the available (limited) data samples. Through problem decomposition and a novel bilevel formulation for each subproblem, we show that each subproblem can be solved by binary search and convex approximation. We also validate that D2BF offers better performance than the state-of-the-art approach while meeting probabilistic data rate guarantees to the users.