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

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 (D
²
BF) 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 D
²
BF offers better performance than the state-of-the-art approach while meeting probabilistic data rate guarantees to the users.