Active user detection and channel estimation of uplink grant-free SCMA system based on variational free energy

Abstract

<p indent=0mm>Joint message passing algorithm (JMPA) that is based on Bayesian theory is a multi-user detection method for the 5G unlicensed SCMA system. This method allows to pass messages in the factor graph until convergence to achieve probability approximation. However, owing to the closed loop of factor graph messaging, there is a possibility of non-convergence for active user detection and channel estimation based on JMPA, which is fatal to the reliability impact of 5G communication systems. This study analyzes the process of active user detection and channel estimation in the uplink grant-free SCMA system that is based on the variational method and derives the fixed point equations that solve the user active probability and channel gain posterior probability. Through the theoretical analysis of the system of equations, sufficient conditions for the convergence of the system of fixed-point equations are obtained. On the basis of the analysis results, the original iterative process is improved. The simulation results show that the improved algorithm is more robust than the original JMPA algorithm and achieves performance without degradation.