Low-Complexity and Quantization-Aware Antenna Switching for Massive MIMO Systems: A Covariance-Based Greedy Approach

Massive multiple-input multiple-output (MIMO) technology is a key enabler for future wireless systems, but its practical implementation is hindered by high power consumption, the prohibitive overhead of acquiring instantaneous channel state information (CSI), and the inflexibility of fixed structures to dynamic traffic. To address these limitations, this letter proposes a dynamic antenna switching framework using low-resolution converters. The core challenge lies in solving the computationally complex switching problem under the effects of severe quantization distortion, using only statistical channel information. We solve this by developing a quantization-aware, covariance-based greedy algorithm with efficient sequential updates. Simulation results demonstrate the proposed method’s robustness and superior adaptability, achieving substantial performance gains that significantly outperform the baseline scheme across a wide range of asymmetric traffic conditions.