Flexible XL-MIMO via Array Configuration Codebook: Codebook Design and Array Configuration Training

Extremely large-scale multiple-input multiple-output (XL-MIMO) emerges as a promising technology to achieve unprecedented enhancements in spectral efficiency and spatial resolution, via orders-of-magnitude increase in the antenna array size. However, the practical issues of high hardware cost and power consumption pose great challenges towards the cost-effective implementation of XL-MIMO. To address such challenges, this paper proposes a novel concept called array configuration codebook (ACC), which enables flexible XL-MIMO cost-effectively and improves the system performance compared with conventional antenna selection (AS) schemes with limited number of radio-frequency (RF) chains. Specifically, ACC refers to a set of pre-designed array configuration codewords, where each codeword specifies the positions of activated antenna pixels. Then, flexible XL-MIMO architecture can be enabled via dynamic pixel activation based on the designed ACC, without having to exhaustively try all possible combinations of the antenna pixels activations. As an illustration, we give a specific codebook design, encompassing the classic compact array (CA) where all activated pixels are separated by half wavelength, uniform sparse array (USA), modular array (MoA), nested array (NA), and co-prime array (CPA), and each codeword is specified by one array configuration parameter. With the designed ACC, array configuration training is considered for multi-user equipment (UE) communication to maximize the sum rate. To reduce the training overhead of exhaustive scanning, a two-stage scanning scheme is proposed, including the array- and pixel-level scanning. For comparison, the greedy AS scheme is proposed, where the resulting incremental signal-to-interference-plus-noise ratio (SINR) expression by activating antenna pixel sequentially is derived in closed-form. Subsequently, array configuration training is extended to the wireless localization scenario. Simulation results are provided to demonstrate the effectiveness of codeword optimization for scenarios of multi-UE communication and wireless localization.