Deepak Das

Blind adaptive multiuser detection for cellular systems using stochastic approximation with averaging

We consider blind adaptive multiuser detection in correlated waveform multiple-access-based cellular radio networks. A common stochastic approximation (SA)-based framework is proposed from which three blind adaptive algorithms for linear minimum mean squared error detection are obtained. Two of them coincide with previously proposed algorithms and the third is shown to be best suited for implementation at a base station. The work here also improves these SA-based adaptation algorithms in the context of cellular radio networks, in terms of the convergence properties by using the results on the SA technique with averaging. Convergence issues of the different adaptations are investigated and numerical examples are presented to demonstrate the performance improvement due to averaging.

Noncoherent MMSE multiuser receivers and their blind adaptive implementations

Three noncoherent minimum mean-squared error (NMSE)-based multiuser receivers are proposed for multipulse modulation. These receivers have a common MMSE prefilter and are followed by one of three phase-independent decision rules. The simplest decision rule selects the maximum magnitude of the MMSE filter outputs, and the other two account for the second-order statistics of the residual multiple-access interference that remains after MMSE filtering. Blind adaptive algorithms are then proposed for the three noncoherent MMSE receivers. The common adaptive algorithm for the MMSE prefilter, which is based on the stochastic approximation method, is shown to converge in the mean-squared error sense to the nonblind NMSE prefilter. Our convergence analysis yields new insight into the tradeoff between the rate of convergence and the residual mean-squared error. The noncoherent blind receivers obtained here do not require the knowledge of the received signals of any of the interfering users, and are hence well-suited for distributed implementation in cellular wireless networks or in communication systems that must operate in noncooperative environments.

Blind adaptive multiuser detection with averaging for cellular systems

We consider blind adaptive multiuser detection in correlated waveform multiple access (CWMA)-based cellular radio networks. A common stochastic approximation (SA) based framework is proposed from which three blind adaptive algorithms for linear MMSE detection are obtained. Two of them coincide with previously proposed algorithms and the third is shown to be best suited for implementation at a base station. Improvement in terms of the convergence properties of these SA-based adaptation algorithms is sought by using previous results on the SA technique with averaging.

Distributed Algorithms for Joint Optimization of Multiuser Receivers and Power Control

Uplink communication in a cellular radio network is considered where the base station in each cell employs linear or decision feedback multiuser receivers. We address the problem of minimizing the total of the user transmit powers over all linear or decision feedback multiuser receivers, such that all the users achieve their signal-to-interference ratio (SIR) targets. A distributed algorithm is obtained that is shown to converge, both in the deterministic and stochastic formulations, to the jointly optimal pair of power allocations and multiuser receivers.