Low Complexity Iterative Multiuser Detection and Decoding for Real-Time Applications

This paper presents a low-complexity multiuser decoding technique that can be implemented in real time for a convolutionally coded direct sequence code division multiple access (DS-CDMA) system. The main contribution, denoted here as the iterative prior update (IPU), consists of iterative interference cancellation and prior updates on sequences of coded bits combined with M-algorithm and list decoding. We illustrate performance gains over other low-complexity sequence detection and decoding strategies and argue that the algorithm converges within a few iterations and requires only a small size buffer for keeping track of the priors along iterations. The fact that the we can use existing available architectures for Viterbi decoding with slight modifications and can meet the real-time processing constraints makes the IPU algorithm an attractive alternative for cellular systems

Proceedings of the Fall 1995 Advanced Digital Communication Systems

Coordinated Science Laboratory was formerly known as Control Systems Laborator

Improved successive interference cancellation schemes using selective pre-cancellation and cross- correlation

Master'sMASTER OF ENGINEERIN

Performance Evaluation of DS-CDMA Receivers Using Genetic Algorithm

Direct sequence-code division multiple access (DS-CDMA) technique is used in cellular systems where users in the cell are separated from each other with their unique spreading codes. In recent times DS-CDMA has been used extensively. These systems suffers from multiple access interference (MAI) due to other users transmitting in the cell, channel inter symbol interference (ISI) due to multipath nature of channels in presence of additive white Gaussian noise(AWGN). Spreading codes play an important role in multiple access capacity of DS-CDMA system. M-sequences, gold sequences etc., has been traditionally used as spreading codes in DS-CDMA. These sequences are generated by shift registers and periodic in nature. So these sequences are less in number and also limits the security. This thesis presents an investigation on use of new type of DS CDMA receiver called Genetic Algorithm based DS-CDMA receiver. Genetic Algorithm is robust optimization technique and does not fall into local minima hence this gives better weight optimization of any system. This Thesis investigates the performance of GA based DS-CDMA communication using gold code sequences. Extensive simulation studies demonstrate the performance of the different linear and nonlinear DS-CDMA receivers like RAKE receiver, matched filter (MF) receiver, minimum mean square error (MMSE) receiver using gold sequences and the performance have been compared with GA based receiver

Intelligent genetic algorithms for next-generation broadband multi-carrier CDMA wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This dissertation proposes a novel intelligent system architecture for next-generation broadband multi-carrier CDMA wireless networks. In our system, two novel and similar intelligent genetic algorithms, namely Minimum Distance guided GAs (MDGAs) are invented for both peak-to-average power ratio (PAPR) reduction at the transmitter side and multi-user detection (MUD) at the receiver side. Meanwhile, we derive a theoretical BER performance analysis for the proposed MC-CDMA system in A WGN channel. Our analytical results show that the theoretical BER performance of synchronized MC-CDMA system is the same as that of the synchronized DS-CDMA system which is also used as a theoretical guidance of our novel MUD receiver design. In contrast to traditional GAs, our MDGAs start with a balanced ratio of exploration and exploitation which is maintained throughout the process. In our algorithms, a new replacement strategy is designed which increases significantly the convergence rate and reduces dramatically computational complexity as compared to the conventional GAs. The simulation results demonstrate that, if compared to those schemes using exhaustive search and traditional GAs, (1) our MDGA-based P APR reduction scheme achieves 99.52% and 50+% reductions in computational complexity, respectively; (2) our MDGA-based MUD scheme achieves 99.54% and 50+% reductions in computational complexity, respectively. The use of one core MDGA solution for both issues can ease the hardware design and dramatically reduce the implementation cost in practice