Bayesian multiuser detection based on a network of NLMS filters

The Network of Kalman Filters structure was proposed, recently, to perform an optimal Bayesian symbol-by-symbol estimation in the multiuser detection context. By approximating the prediction error covariance matrix on each branch by a constant diagonal one, we show in this paper that the NKF structure can be expressed into a particular Network of normalized LMS filters exhibiting less computational complexity. The choice of the value of the step-size is also discussed. In order to overcome its heuristic choice, we, here, propose a new adaptive step size based on the second order moment of the estimated symbols. The form of the step-size still contains an information on the a priori state estimation. The performance of the resulted receiver structure is evaluated by means of computer simulations for very high asynchronous system load, in multipath fading channel, and compared to MAP, NKF, MMSE and Rake receivers.