Adaptive Reduced-Rank Receive Processing Based on Minimum Symbol-Error-Rate Criterion for Large-Scale Multiple-Antenna Systems

PROJECT TITLE:

Adaptive Reduced-Rank Receive Processing Based on Minimum Symbol-Error-Rate Criterion for Large-Scale Multiple-Antenna Systems

ABSTRACT:

During this work, we have a tendency to propose a novel adaptive reduced-rank receive processing strategy based mostly on joint preprocessing, decimation and filtering (JPDF) for massive-scale multiple-antenna systems. During this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based mostly on the minimization of the image-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a cluster of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and $M$-ary quadrature amplitude modulation (QAM) symbols. Additionally, an automatic parameter choice theme is proposed to more improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.

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