Abstract
This paper presents a constrained least mean squared (LMS) algorithm for MIMO CDMA linear equalizer is presented, which is constrained on spreading sequence length, number of subscribers, variances of the Gaussian noise and the multiple access interference (MAI) plus the additive noise (introduced as a new constraint). The novelty of the proposed algorithm is that MAI and MAI plus noise variance has never been used as a constraint in MIMO CDMA systems. Convergence analysis is performed for the proposed algorithm in case when statistics of MAI and MAI plus noise are available. Simulation results are presented to compare the performance of the proposed constrained algorithm with other constrained algorithms and it is proved that the new algorithm has outperformed the existing constrained algorithms.
Highlights
It is shown in the literature that performance of an adaptive algorithm may be enhanced if partial knowledge of a particular channel is blended in the algorithm design[1], [2]
A complementary pair least mean squared (LMS) (CPLMS) [3] was initiated by using constrained optimization technique named augmented Lagrangian (AL) which can be utilized to solve the problem of selecting an appropriate update step-size in LMS algorithm
The proposed constrained algorithm is developed by incorporating MIMO multiple access interference (MAI) and noise variances resulting in a generalized MIMO MAI plus noise constrained LMS (MIMO-MNCLMS) adaptive algorithm
Summary
It is shown in the literature that performance of an adaptive algorithm may be enhanced if partial knowledge of a particular channel is blended in the algorithm design[1], [2]. In MIMO-CDMA system with N transmitting and M receiving antennas, output of mth matched-filter, matched to intended subscriber’s (subscriber 1) spreading sequence consists of the desired user’s component, blm, MIMO-MAI, Uml and the white Gaussian noise, νml as ylm = blm + Uml + νml (4). Assumption 2: The noise sequence is a zero mean i.i.d. sequence, Gaussian random variable having variance σν2n This sequence is independent of the input process. Assumption 3: MAI in AWGN environment represented by Uml is zero mean Gaussian random variable with variance σU2m. It is is independent of the input process as well as the noise. In (22) and (23), E h2mn is the second moment of E [hmn]
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