A novel adaptive diversity achieving channel estimation scheme for LTE

Abstract

As the LTE standard becomes more widespread for wireless communication of high-speed data for mobile phones, the importance of channel estimation algorithms for OFDM is increasing. Although OFDM is widely used as the signal bearer in many different applications including LTE due to its robustness to multipath fading and interference, its success heavily depends on accurate channel estimation, especially, in rapidly changing urban environments. In this paper, we introduce an adaptive diversity achieving combination scheme operating at the receiver that is mathematically proven to improve channel estimation performance. Here, we introduce an adaptive combination of adaptive channel estimation algorithms running in parallel considered as diversity branches. The channel estimates of these constituent branches are combined using a convexly constrained adaptive mixture. Unlike the well-known diversity achieving schemes, including selection combining, threshold combining, this algorithm is mathematically shown to improve estimation or receiver operating performance. To this end, we first derive a multiplicative update rule based on Bregman divergences to train the combination weights. We then present the steady-state MSE analysis of the combination algorithm and show that the mixture is universal with respect to the input channel estimators such that it performs as well as the best constituent estimator, and in some cases, outperforms both constituent channel estimators in the steady-state. We also show that the mixture diversity weight vector converges to the optimal combination weight vector in terms of minimizing MSE under the convex constraint. We analyze and validate our analysis and the introduced results through simulations.