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SEVENTH
ANNUAL |
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Quadratically Constrained RLS Filtering for Adaptive
Beamforming and DS-CDMA Multiuser Detection Kristine L. Bell, Zhi Tian, and Harry L. Van Trees George Mason University 341 Science and Technology Building Fairfax, VA 22030-4444 tel: (703) 993-1707 email: kbell@gmu.edu Abstract The Minimum
Output Energy (MOE) detector used in Direct-Sequence Code Division Multiple Access
(DS-CDMA) communications systems is analogous to the Linearly Constrained Minimum Power
(LCMP) beamformer used in array processing. In both applications, a quadratic constraint
on the weight vector norm can improve robustness to mismatch in the temporal or spatial
signature vector. We describe a technique for implementing a quadratic inequality
constraint with Recursive Least Squares (RLS) updating in both the direct LCMP and the
Generalized Sidelobe Canceler (GSC) (also called the Partitioned Linear Interference
Canceler (PLIC)) structures. A variable diagonal loading term is incorporated at each
step, where the amount of loading required to satisfy the quadratic constraint is given in
closed form. Comparisons are made with several other robust RLS and Least Mean Squares
(LMS) algorithms. Simulations show that the variable loading RLS technique offers better
convergence and robust control over mismatch than other LMS and RLS implementations for
both beamforming and multiuser detection. |
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