Averaged stochastic gradient algorithms for adaptive blind multiuser detection in DS/CDMA systems

TitleAveraged stochastic gradient algorithms for adaptive blind multiuser detection in DS/CDMA systems
Publication TypeJournal Article
Year of Publication2000
AuthorsKrishnamurthy, V.
JournalCommunications, IEEE Transactions on
Pagination125 -134
Date Publishedjan.
Keywordsadaptive blind multiuser detection, adaptive signal detection, averaged gradient algorithm, averaged stochastic gradient algorithms, averaging, BAG algorithm, blind adaptive gradient algorithm, code division multiple access, code-aided suppression, computational complexity, computational cost, convergence, convergence of numerical methods, direct-sequence/code-division multiple-access, DS/CDMA systems, gradient methods, interference suppression, multiple-access interference, narrow-band interference, NBI suppression, spread spectrum communication, stochastic gradient algorithm, stochastic processes, tracking properties

In this paper, we present a blind adaptive gradient (BAG) algorithm for code-aided suppression of multiple-access interference (MAI) and narrow-band interference (NBI) in direct-sequence/code-division multiple-access (DS/CDMA) systems. This BAG algorithm is based on the concept of accelerating the convergence of a stochastic gradient algorithm by averaging. This ingenious concept of averaging was invented by Polyak and Juditsky (1992)-this paper examines its application to blind multiuser detection and NBI suppression in DS/CDMA systems. We prove that BAG has identical convergence and tracking properties to recursive least squares (LMS) but has a computational cost similar to the least mean squares (LMS) algorithm-i.e., an order of magnitude lower computational cost than RLS. Simulations are used to compare our averaged gradient algorithm with the blind LMS and LMS schemes


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