Sparsity-Boosted Detection for Large MIMO Systems

Abstract

In this letter, we propose a novel low-complexity detector for large MIMO systems, which is capable of achieving near-ML performance for low order constellation (such as BPSK, 4-QAM). The main idea of our algorithm is to successively boost the detection by leveraging the hidden sparsity in the residual error of received signal. Specifically, since the symbol error rate (SER) of the MMSE detector is usually not high (say, less than 10%), the residual error, which is the difference between the original transmitted signal and the recovered one, would exhibit significant sparsity. Therefore, by locating the non-zero entries (i.e., the incorrectly detected symbols) via compressive sensing algorithms, we can reduce the original MIMO system to a new one, whose input dimension is much less than the output dimension. This implies that a linear detector will suffice for achieving near-optimal performance, otherwise we can repeat the above procedures to iteratively boost the detection till satisfaction. Overall, our proposed algorithm can achieve performance close to the optimal ML detector, while its complexity is just on the order of the linear detectors (say, MMSE detector).