Signal detection is one of the fundamental problems in three-dimensional multiple-input multiple-output (3D-MIMO) wireless communication systems. This paper addresses a signal detection problem in 3D-MIMO system, in which spatial modulation (SM) transmission scheme is considered due to its advantages of low complexity and high-energy efficiency. SM based signal transmission typically results in the block-sparse structure in received signals. Hence, structured compressed sensing (SCS) based signal detection is proposed to exploit the inherent block sparsity information in the received signal for the uplink (UL). Moreover, normalization preprocessing is considered before iteration process with the purpose of preventing the noise from being overamplified by the column vector with inadequately large elements. Simulation results are provided to show the stable and reliable performance of the proposed algorithm under both Gaussian and non-Gaussian noise, in comparison with methods such as compressed sensing based detectors, minimum mean square error (MMSE), and zero forcing (ZF).
Low‐complexity signal detection for large‐scale MIMO systems with second‐order Richardson method
