Block-adaptive kernel-based CDMA multiuser detection

The paper investigates the application of a recently introduced learning technique, referred to as the relevance vector machine (RVM), to construct a block-adaptive kernel-based nonlinear multiuser detector (MUD) for direct-sequence code-division multiple-access (DS-CDMA) signals transmitted through multipath channels. It is demonstrated that the RVM MUD is capable of closely matching the performance of the optimal Bayesian one-shot detector, with the aid of a significantly more sparse kernel representation than that required by the state-of-the-art support vector machine (SVM) technique.