Particle Filter Based Nonlinear Data Detection for Frequency Selective mm-Wave MIMO-OFDM Systems

Abstract

Millimeter wave (mm-Wave) frequency band combined with multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) is a key enabler for fifth generation (5G) technology. However, the requirement of high power emission at the mm-Wave transmitter to combat the significant path loss, the enormous bandwidth, and the high frequency design limitations of the integrated circuits involved in mm-Wave systems, result in severe nonlinear distortion in the signals attributed by the RF power amplifier (PA) and other RF circuits. This nonlinear distortion causes non-orthogonality in OFDM subcarriers which in collusion with frequency selective channel may pose great challenges to signal detection. In this paper, we propose an efficient technique for channel estimation and data detection in the presence of nonlinear distortion for mm-Wave MIMO-OFDM systems. The nonlinear impairment causes the posterior distribution of data symbols as non-Gaussian and analytically intractable. To tackle it, an iterative algorithm based on particle filter (PF) for data detection is designed. The detected symbols are then used to estimate and update the channel gains using a sequential maximum likelihood (SQML) estimation. Simulation results validate the proposed algorithm.