Abstract
This paper presents a performance comparison between a constrained least mean squared algorithm for MIMO CDMA decision feedback equalizer and linear equalizer. Both algorithms are constrained on the length of spreading sequence, number of users, variance of multiple access interference as well as additive white Gaussian noise (new constraint). An important feature of both algorithms is that multiple access interference together with noise variance is used as a constraint in MIMO CDMA linear and decision feedback equalization systems. Convergence analysis is performed for algorithm in both cases. From the simulation results shown at the end show that algorithm developed for decision feedback equalizer has outperformed the algorithm developed for linear equalizer in MIMO CDMA case
Highlights
It is shown in literature that performance of an adaptive algorithm may be enhanced if partial knowledge of the channel is included in algorithm design[1], [2], [3]
In oreder to analyze the performance of the proposed algorithm for multiple input; multiple output (MIMO) CDMA linear equalizer (LE) case, simulation results are presented
Performance of MNCLMS algorithm is compared to standard least mean squared (LMS), MCLMS noise constrained LMS and zero noise algorithms and later on performance of the algorithms in LE and decision feedback equalizer (DFE) cases is compared to each other
Summary
It is shown in literature that performance of an adaptive algorithm may be enhanced if partial knowledge of the channel is included in algorithm design[1], [2], [3]. Augmented technique was used in [6], incorporating the knowledge of the statistics (variance) of multiple access interference (MAI) and additive white noise and was named constrained LMS algorithm (MNCLMS) for single input, single ouput (SISO) CDMA system. Since the MAI together with the white Gaussian noise (AWGN) badly effects the MIMO-CDMA systems, it is required to design a receiver design that would negate the damaging effect of MAI and additive AWGN This necessitates an enactment of the MNCLMS algorithm derived in [7], [8] for the decision feedback equalizer (DFE) case.
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