Mobile Multiuser Detection Technique

In mobile / cellular networks the multiuser detection technology emerged in early 80s. it is now developed in to an important full-fledged field in multi-access communication. In the conventional single user detector in DS-CDMA system, MAI and near-far effect cause limitation of capacity. On the other hand the optimal MUD suffers from computational complexity that grows exponentially with number active user. During a last two decade there has been a lot of interest of sub optimal multiuser detector which are low in complexity but deliver negotiable performance. This topic highlighted various detection techniques. As in Multiuser MIMO system a base station equipped with multiple antennas serves a number of users. Conventionally the communication between the BS and the user is performed by orthogonalizing the channel so that the BS communicates with each user in separate time frequency resources. This is not optimal from an information theoretic point of view and high rate can be obtained, if the BS communicates with several users in same time frequency response. DOI: 10.17762/ijritcc2321-8169.15082

Compressive Sensing for Spread Spectrum Receivers

With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important power: efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in the two design parameters. This paper investigates the use of Compressive Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. This measurement scheme, named Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model. We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows that it is possible to subsample a CDMA signal using CSS and that in one example the CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE Transactions on Wireless Communication

Structure-Based Bayesian Sparse Reconstruction

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is relatively low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at a low sparsity rate.Comment: 29 pages, 15 figures, accepted in IEEE Transactions on Signal Processing (July 2012

Approximate Message Passing under Finite Alphabet Constraints

In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on the sparsity but by means of a prior distribution also on the discrete nature of the original signal. In our numerical experiments we test this algorithm in combination with a Rademacher measurement matrix and a measurement matrix derived from the random demodulator, which enables compressive sampling of analogue signals. Our results show in both cases significant performance gains over a linear programming based approach to the considered BPDN problem. We also compare the proposed algorithm to a similar message passing based algorithm without prior knowledge and observe an even larger performance improvement.Comment: 4 pages, 2 figures, to appear in IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 201