Equality-Constrained Detector Using Probabilistic Approach for Massive GSSK-MIMO Systems

In this letter, we propose an equality-constrained detector using probabilistic approaches for massive multiple-input multiple-output (MIMO) systems with generalized space shift keying (GSSK) modulation. Probabilistic approaches derived from the Bayesian iteration can provide the soft decision for achieving the near-optimal performance and some other benefits such as combination of detection and channel decoding. However, these methods cannot be directly adopted to GSSK modulated MIMO systems without considering the equality-constraint of GSSK signals. To address this problem, we develop an augmented system model with a weighting parameter. Moreover, the relation between the augmented model and the quadratic penalty function is also derived to determine the value of weighting parameter. Consequently, probabilistic approaches based on the augmented system model can be directly used to solve the equality-constrained problems. Simulation results substantiate the high-performance and extensibility of the proposed detector.