A Low-complexity Iterative GAMP-based Detection for Massive MIMO with Low-resolution ADCs

A performance-acceptable and low-complexity detection method for massive multiple-input multiple-output (MIMO) involving low-resolution analog digital converter (ADC) at each antenna is proposed. The proposed method combines the generalized approximate message passing (GAMP) detection with channel decoder and exchanges extrinsic information between them, by which the remaining information filtered by the ADCs can be recovered as accurate as possible. Contrasted to the iterative minimum mean squared error (MMSE) detection, our method circumvents large-scale matrix inverse operation and leverages the statistical properties of both quantization errors and transmitted symbols. Moreover, we analyze the computational complexity and storage occupation for both algorithms to authenticate the superiority of the proposed approach. For visualization, the numerical results reveal that 3-bit ADCs are capable of achieving the almost same performance as the full resolution ADCs and substantiate that the bit error ratio (BER) performance of the proposed method is equivalent to that of iterative MMSE but with less complexity for implementation.