Low Complexity Maximum Likelihood Detection in Spatial Modulation Systems (1206.6190v2)

Abstract: Spatial Modulation (SM) is a recently developed low-complexity Multiple-Input Multiple-Output scheme that uses antenna indices and a conventional signal set to convey information. It has been shown that the Maximum-Likelihood (ML) detection in an SM system involves joint detection of the transmit antenna index and the transmitted symbol, and hence, the ML search complexity grows linearly with the number of transmit antennas and the size of the signal set. In this paper, we show that the ML search complexity in an SM system becomes independent of the constellation size when the signal set employed is a square- or a rectangular-QAM. Further, we show that Sphere Decoding (SD) algorithms become essential in SM systems only when the number of transmit antennas is large and not necessarily when the employed signal set is large. We propose a novel {\em hard-limiting} enabled sphere decoding detector whose complexity is lesser than that of the existing detector and a generalized detection scheme for SM systems with {\em arbitrary} number of transmit antennas. We support our claims with simulation results that the proposed detectors are ML-optimal and offer a significantly reduced complexity.