Joint Data Detection and Channel Estimation for Fading Unknown Time-Varying Doppler Environments

Abstract

This work considers a joint channel estimation and data detection technique for Multiple Space-Time Trellis Codes (MSTTCs) operating over unknown time-varying channels with large Doppler spread. We propose an algorithm, called Doppler Adaptive Smoothed Data Detection and Kalman Estimation (DA-SDD-KE), that jointly detects data and estimates the channel as well as the time-varying Doppler. In this scheme, an Adaptive Kalman Predictor (AKP) consisting of a KP and a covariance-based Doppler estimator is incorporated into a Per-Survivor Processing (PSP)-based algorithm that utilizes the past, present and future received symbols for smoothed data detection. For comparison purposes, we also develop a Doppler Adaptive version of the Delayed Mixture Kalman Filtering (DMKF) technique, referred to as DA-DMKF, where the adaptive estimations of the channel and the Doppler shift are based on sequences of importance samples. Moreover, we propose a model for generating a Rayleigh fading process with time-varying Doppler using the sum of sinusoids method. The performance of the DA-SDD-KE and DA-DMKF algorithms over channels with constant, linear and quadratic Doppler functions is evaluated using computer simulations, revealing that the DA-SDD-KE algorithm performs well for all considered Doppler functions, and provides a considerably gain over the DA-DMKF algorithm.