A semi-blind channel estimation technique based on second-order blind method for CDMA systems

Abstract

Abstract-This paper aims at studying a semi-blind channel estimation scheme based on the subspace method or a carefully weighted linear prediction approach. The corresponding (composite) semi-blind cost functions result from a linear combination of the training-based cost function and a blind cost function. For each blind method, we show how to calculate the asymptotic estimation error. Therefore, by minimizing this error, we can properly tune the -dimensional regularizing vector introduced in the composite semi-blind criterion (for active users in the uplink). The asymptotic estimation error minimization is a -variable minimization problem, which is a complex issue with which to deal. We explicitly show under what conditions this problem boils down to single-variable minimization problems. Our discussion is not limited to theoretical analyses. Simulation results performed in a realistic context [universal mobile telecommunication system-time division duplex (UMTS-TDD) mode] are provided. In particular, we conclude about the potential of the proposed approach in real communication systems.

Taylor series approximation of semi-blind BLUE channel estimates with applications to DTV

“…Some other approaches to the problem of semi-blind channel estimation have encompassed [5][6][7][8].…”

“…In [6] the approach given in [7] of linearly combining the training-based criterion and the blind criterion, in the manner which is identical to the standard regularization method, is extended. The authors comment that in [7] an asymptotic argument was used to ''fine tune'' the regularization parameter by minimizing the channel estimation error.…”

“…This is done in [7] for the single-input multiple-output (SIMO) case. In [6] the results are extended to the case of the Multiple-input multiple-output channel in the context of a synchronized Code Division Multiple Access uplink (channel estimation for the multiple channels in this case). Since, in this case, there are several channels to be estimated in the uplink, then there is a vector of regularization parameters (or tuning parameters) to be estimated.…”

“…Since, in this case, there are several channels to be estimated in the uplink, then there is a vector of regularization parameters (or tuning parameters) to be estimated. Lasaulce et al [6] addresses this problem.…”

“…Several recent papers consider different aspects of semi-blind channel estimation. Notably [4], considers identifiability conditions, and [5][6][7][8] consider different aspects of the semi-blind approach.…”