Soft-Decision-Aided, Smoothness-Constrained Channel Estimation over Time-Varying Fading Channels With No Channel Model Information
We consider frequency-flat time-varying fading channels with no channel model information (CMI). By introducing the smoothness function to measure the extent of channel fluctuation, we derive a robust soft-decision-aided (SDA) channel estimator based on Pareto optimality of the double-objective optimization of the likelihood function of the received signal sequence and the smoothness constraint of the channel estimates. Compared with the conventional maximum-likelihood-based channel estimators derived under the block-fading assumption, the newly derived SDA-Pareto estimator gives more freedom to the channel estimation process, allowing channel estimates to have controlled variations to track the time-varying channel more closely. Compared with estimators derived based on the maximum a posteriori probability or the minimum mean-square error criterion which require explicit acquisition of the CMI, the SDA-Pareto estimator significantly simplifies the channel measurement process by requiring only a suitable regularization parameter to balance the trade-off between the likelihood function and the smoothness condition. An adaptive algorithm is proposed to adjust the regularization parameter adaptively, enabling an efficient and effective implementation of the SDA-Pareto estimator in practical applications. Simulation studies are provided to demonstrate the advantage of the SDA-Pareto estimator over the conventional estimators in both channel estimation accuracy and error-rate performance.