Abstract

We present a reduced-dimension multiuser detector (RD-MUD) structure that significantly decreases the number of required correlation branches at the receiver front-end, while still achieving performance similar to that of the conventional matched-filter (MF) bank. RD-MUD exploits the fact that the number of active users is typically small relative to the total number of users in the system and relies on ideas of analog compressed sensing to reduce the number of correlators. The correlating signals used by each correlator are chosen as an appropriate linear combination of the users ’ spreading waveforms, which in turn are chosen from a large class of spreading codes. We derive the probability-of-error when using two methods for recovery of active users and their transmitted symbols: the reduced-dimension decorrelating (RDD) detector, which combines subspace projection and thresholding to determine active users and sign detection for data recovery, and the reduced-dimension decision-feedback (RDDF) detector, which combines decision-feedback orthogonal matching pursuit for active user detection and sign detection for data recovery. We identify conditions such that error is dominated by active user detection. We then show that the number of correlators needed to achieve a small probability-of-error under these conditions is on the order of the logarithm of the number of users in the system for a given projection method based on random discrete Fourier transform (DFT) matrices, which is significantly lower than the number of correlators required by the the conventional MUD using the MF-bank. Our theoretical results take into consideration the effects of correlated signature waveforms as well as near-far issues. The theoretical performance results for both detectors are validated via numerical simulations.