Improving the spectral efficiency of Precoded-MIMO Systems

Abstract

Transmit Precoding is used in MIMO systems as it reduces the complexity of the MIMO receiver and simplifies the decoding procedure. A multiple-input multiple-output (MIMO) system with Precoding typically suffers from a drop in spectral efficiency when the number of receive antennas is less than the number of transmit antennas. To solve this problem, this paper introduces two new system models for MIMO. First, a Generalized Spatially Modulated (GSM-MIMO) system with Low-Density Parity Check (LDPC) Precoding at the Transmitter end and a SMUX-MIMO system with LDPC Precoding that is referred to as Sparse Matrix Precoded Massive Spatially Multiplexed MIMO (SMP-MIMO). A simplified version of GSM-MIMO which is the GSSK-MIMO system is considered for simulations. At the GSSK-MIMO receiver with LPDC Precoding, this work makes a thorough comparison of various Compressive Sensing (CS) techniques and implements two new CS-based belief propagation algorithms, the Independent Probability Evaluation (IPE) and the Joint Probability Evaluation (JPE), that show improved performance as compared to the conventional CS-techniques. At the SMP-MIMO receiver, using the concept of power multiplier, we propose an enhanced version of the IPE algorithm, that yields superior performance when compared to different Massive-MIMO algorithms available in the literature like MMSE-SIC and MMSE-AMP.