A New Low-Complexity Near-ML Detection Algorithm for Spatial Modulation

In this letter, we propose a distance-based ordered detection (DBD) algorithm for spatial modulation (SM) to reduce the receiver complexity and achieve a near maximum likelihood (ML) performance. The proposed algorithm firstly compensates the channel attenuation and obtains the estimated symbols with transmit antenna indices, then orders the indices based on the distances between these symbols and their demodulation constellations. A searching method is developed to obtain the final decision through a trade-off between performance and complexity. The equivalence between DBD and ML algorithms is also proved by theoretical analysis. Furthermore, a new low-complexity soft output DBD (SODBD) algorithm is developed for coded SM systems. The simulation results show that DBD algorithm has a close performance to ML algorithm while effectively reducing the complexity compared to conventional near-ML algorithms.