Multiple access interference plus noise constrained least mean square algorithm

In this work, a constrained least-mean-square (LMS) algorithm, which incorporates the knowledge of the number of users, spreading sequence length and additive noise variance, is developed subject to the new combined constraint comprising both the MAI and noise variance. The novelty of this constraint resides in the fact that the MAI variance was never used as a constraint. This constrained optimization technique results in an (MAI plus noise)-constrained LMS (MNCLMS) algorithm. Convergence analysis is carried out of the proposed algorithm in the presence of MAI. Finally, a number of simulations are conducted to compare performance of MNC-LMS algorithm with other adaptive algorithms.