Dual Transformer-Based Scalable Robust Precoding for Massive MIMO Transmission

This paper presents a dual transformer-based robust precoding scheme for massive multiple-input multiple-output systems with low computational complexity. By utilizing the a posteriori channel model, the imperfect channel state information (CSI) is modeled as the statistical CSI that incorporates channel mean and channel variance information with spatial correlation. Based on this, we formulate a robust precoding problem aimed at maximizing the expected sum rate and subsequently transform it into a robust weighted minimum mean square error problem. We prove the permutation equivariance and invariance satisfied by the mapping from the available CSI to the low-dimensional variables in the optimal closed-form solution. To fully exploit such properties, we design a dual transformer block with residual connection and introduce an invariant transformer module to construct a neural network, which is trained to approximate the mapping for precoding computation. Simulation results demonstrate that this method exhibits strong robustness, lower computational complexity, and high scalability in dynamic user/antenna scenarios compared to other approaches.