Joint Beamforming Design and Bit Allocation in Massive MIMO With Resolution-Adaptive ADCs

Low-resolution analog-to-digital converters (ADCs) have emerged as a promising technology for reducing power consumption and complexity in massive multiple-input multiple-output (MIMO) systems while maintaining satisfactory spectral and energy efficiencies (SE/EE). In this work, we first present the fundamental properties of optimal quantization and leverage them to derive a more accurate approximation of the covariance matrix of the quantization distortion. This theoretical finding facilitates the analysis of the system’s SE in the presence of low-resolution ADCs. Then, considering resolution-adaptive ADCs, we focus on the joint optimization of the transmit-receive beamforming and bit allocation to maximize the SE under constraints on the transmit power and the total number of active ADC bits. To solve the resulting mixed-integer problem, we first develop an efficient beamforming design for fixed ADC resolutions. Subsequently, we propose a low-complexity heuristic algorithm to iteratively optimize the ADC resolutions and beamforming matrices. Numerical results for a 64 × 64 MIMO system demonstrate that the proposed design offers 6% improvements in both SE and EE with 40% fewer active ADC bits compared with uniform bit allocation. Furthermore, it is unveiled that receiving more data streams with low-resolution ADCs can lead to higher SE and EE compared with receiving fewer data streams with high-resolution ADCs.