Evolutionary Game Algorithm for channel estimation in Interleave-Division Multiple Access Systems

In this paper, a novel channel estimation approach based on the Evolutionary Game Algorithm (EGA) is proposed for the Interleave-Division Multiple Access Systems (IDMA). As a stochastic optimization algorithm based on the non-cooperative games, EGA maps the search space of channel state information (CSI) and objective function of log-likelihood function to the strategy profile space and utility function of non-cooperative game respectively, and achieves the optimization objective by exploring the equilibrium points of the corresponding games. Therefore the channel coefficients can be estimated with EGA for IDMA systems. The Turbo-like combination channel estimation is used in the proposed algorithm. At the first iteration we use the pilot sequences to estimate the initial channel information. And the soft estimates from the IDMA signal detector are combined with the pilot information to refine the channel coefficients in the following estimation iterations. The simulation results indicate that the bit-error-rate performance of the proposed algorithm is very close to that of ideal channel estimation. We also find the estimation performance of EGA is better than that of Expectation Maximization (EM) algorithm.