Joint data detection and channel estimation for fading unknown time-varying Doppler environments

  • Authors:

  • Usa Vilaipornsawai;Harry Leib

  • Affiliations:

  • Institute for Systems and Robotics, University of Algarve, Faro, Portugal and Department of Electrical & Computer Engineering, McGill University, Montreal, Quebec, Canada;Department of Electrical & Computer Engineering, McGill University, Montreal, Quebec, Canada

  • Venue:
  • IEEE Transactions on Communications
  • Year:
  • 2010

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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.