Blind adaptive detection of DS/CDMA signals on time-varying multipath channels with antenna arrays using high-order statistics

A new approach based on multiscale decomposition and higher-order statistics is presented for the simultaneous solution of multiuser interference and time-varying multipath propagation in the uplink of a cellular direct-sequence spread-spectrum code-division multiple-access (DS/CDMA) system. Each channel between the mobile transmitter and the base-station receiver is unknown and arbitrarily varying with time. The optimum filter achieving separation and multipath compensation is time-variant. The typical approach in many multiuser detectors previously proposed is to assume that the channel is almost static (time invariant) and attempt detection according to this model. Slow variations of the channels are then compensated using adaptive algorithms that force the estimates (of the channels or of the separating filters) to be constantly in search of a convergence point. If the channel coefficients variations in time are fast with respect to the convergence time of the adaptive algorithm, significant degradation may result. In this work, we depart from this traditional approach and we investigate new kernels that more accurately can characterize the time-varying nature of the detection problem. As a first step, we show that the super-exponential framework can still be applied in a time-variant environment. Then, we describe a multiresolution decomposition of the filter components, essentially constraining its variations in time to remain within the solution subspace. The resulting algorithm overcomes some of the drawbacks associated with slow convergence and insufficient tracking capability typical of many blind approaches and several nonblind methods based on the slow fading assumption.