Block-Adaptive Kernel-Based CDMA Multiuser Detector

The paper investigates the application of a recently introduced learning technique, called 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 can closely match the performance of the optimal Bayesian one-shot detector, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.