LAUSR

This article proposes two low complexity decoders for non coherent differential space shift keying (DSSK) multiple input multiple output (MIMO) system by representing the detection problem as a hierarchical tree structure. Such presentation enables yielding the optimal maximum likelihood (ML) performance but with a massive reduction in computational complexity. Attained reduction is accomplished through 1) utilizing the error-repetitive property of DSSK detection to reduce the number of computed error terms and 2) developing two efficient searching strategies that avoid searching the entire alphabet as in ML detectors. These searching strategies enable a trade–off between performance and complexity. Unlike existing sparse recovery (SR) decoders, which require a projection phase to ensure the closure property (i.e., only single transmit antenna is active at each particular time instant and the multiplication of a constellation matrix with another one will produce a matrix from within the set), the new algorithms, inherently, maintain the closure property of DSSK system without additional procedures. Reported results reveal that optimal ML performance is obtained with a huge reduction in complexity by proper adjustment of parameters. It is also shown that the proposed algorithm performs better than state of the art SR algorithms, and the feasibility of massive MIMO configurations with low complexity is demonstrated.