Reduced-Dimension Multiuser Detection

Abstract

Abstract-We present a reduced-dimension multiuser detector (RD-MUD) structure for synchronous systems 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, in some wireless systems, the number of active users may be small relative to the total number of users in the system. Hence, the ideas of analog compressed sensing may be used 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. We derive the probability-of-symbolerror 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 matching pursuit for active user detection and sign detection for data recovery. We derive probability of error bounds for both detectors, and show that the number of correlators needed to achieve a small probabilityof-symbol-error is on the order of the logarithm of the number of users in the system. The theoretical performance results are validated via numerical simulations.

Compressive detection of direct sequence spread spectrum signals

“…The theoretical FNR (i.e. probability of miss) was calculated using (2)- (5). The FNR versus signal-to-noise ratio (SNR) are plotted in Fig.…”

“…With the development of the compressive sensing (CS) theory [1,2], some more recent works studied the signal structures and developed the optimal compressive receiver to cooperatively detect whether anticipated DSSS signals (e.g. in code division multiple access (CDMA)) are present [3][4][5]. However, detecting the presence of a DSSS signal without the knowledge of the spreading sequence is challenging since the DSSS signals can be below background noise levels after spreading.…”

Info-Greedy Sequential Adaptive Compressed Sensing

“…In this case, higher power allocated to a measurement increases SNR of that measurement. (b) white Gaussian noise added prior to the measurement, a model that appears in some applications such as reduced dimension multi-user detection in communication systems [41] and also known as the "noise folding" model [36]:…”

Analog Compressed Sensing for Sparse Frequency Shift Keying Modulation Schemes

“…The performance of the recovery algorithms using correlating signals to recover analog signals such as FSK signals have previously been investigated, though primarily in additive white Gaussian noise (AWGN) channels or in scenarios where the receiver knows the CSI. The reduced dimension multiuser detection receiver used a bank of correlating receivers, which are generated from a linear combination of the users' signals, to detect active users signals that passed through an AWGN channel with CSI known at the receiver [21]. The parallel segmented compressed sensing structure contained a bank of filters where overlapping portions of the noisy signal were sampled in parallel and reconstructed using the OMP algorithm [22].…”