Power and performance trade-off in DS-CDMA receivers based on adaptive LMS-MMSE multi-user detector.

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2003.Third generation cellular communication systems based on CDMA techniques have shown great scope for improvement in system capacity. Over the last decade, there has been significant interest in DS-CDMA detectors. The conventional detector, the optimal detector and a number of sub-optimal multi-user detectors (MUD) have been extensively analyzed in the literature. Recently, the reduction of power consumption in DS-CDMA systems has also become another important consideration in both system design and in implementation. In order to support wireless multimedia services, all CDMA-based systems for third generation systems have a large bandwidth and a high data rate, therefore the power consumed by the digital signal processor (DSP) is high. This thesis focuses on power consumption in the adaptive Minimum Mean Square Error (MMSE) detector which is based on the Least Mean Square (LMS) algorithm. This thesis presents a literature survey on MUD and adaptive filter algorithms. A system model of the quantized LMS-MMSE MUD is proposed and its performance is analyzed. The quantization effects in the finite precision LMS-MMSE adaptive MUD including the steady-state weight covariance, mean square error (MSE) and bit error rate (BER) versus wordlength of data and coefficient are investigated when both the data and filter coefficients are quantized. The effects of wordlength size on power consumption are investigated and the tradeoff between the power consumption and performance degradation and the optimal allocation of bits to data and to LMS coefficients under power constraint is presented

Descodificação através de Machine Learning

In recent years, machine learning has become one of the most rapidly expanding technologies in a variety of technological fields. In general, it allows a computer to learn from data without being expressly designed for a particular purpose. This thesis investigates the application of decoding methods inspired by machine learning to linear block codes, such as Reed-Muller (RM) codes.Recentemente, o Machine Learning tornou-se uma das tecnologias em mais rápida expansão numa variedade de campos tecnológicos. Em geral, permite que um computador aprenda com os dados sem ser expressamente concebido para um fim específico. Esta dissertação investiga a aplicação de métodos de descodificação inspirados no Machine Learning a códigos de blocos lineares, tais como os códigos de Reed-Muller

Sparse graph codes on a multi-dimensional WCDMA platform

Digital technology has made complex signal processing possible in communication systems and greatly improved the performance and quality of most modern telecommunication systems. The telecommunication industry and specifically mobile wireless telephone and computer networks have shown phenomenal growth in both the number of subscribers and emerging services, resulting in rapid consumption of common resources of which the electromagnetic spectrum is the most important. Technological advances and research in digital communication are necessary to satisfy the growing demand, to fuel the demand and to exploit all the possibilities and business opportunities. Efficient management and distribution of resources facilitated by state-of-the-art algorithms are indispensable in modern communication networks. The challenge in communication system design is to construct a system that can accurately reproduce the transmitted source message at the receiver. The channel connecting the transmitter and receiver introduces detrimental effects and limits the reliability and speed of information transfer between the source and destination. Typical channel effects encountered in mobile wireless communication systems include path loss between the transmitter and receiver, noise caused by the environment and electronics in the system, and fading caused by multiple paths and movement in the communication channel. In multiple access systems, different users cause interference in each other’s signals and adversely affect the system performance. To ensure reliable communication, methods to overcome channel effects must be devised and implemented in the system. Techniques used to improve system performance and capacity include temporal, frequency, polarisation and spatial diversity. This dissertation is concerned mainly with temporal or time diversity. Channel coding is a temporal diversity scheme and aims to improve the system error performance by adding structured redundancy to the transmitted message. The receiver exploits the redundancy to infer with greater accuracy which message was transmitted, compared with uncoded systems. Sparse graph codes are channel codes represented as sparse probabilistic graphical models which originated in artificial intelligence theory. These channel codes are described as factor graph structures with bit nodes, representing the transmitted codeword bits, and bit-constrained or check nodes. Each constraint involves only a small number of code bits, resulting in a sparse factor graph with far fewer connections between bit and check nodes than the maximum number of possible connections. Sparse graph codes are iteratively decoded using message passing or belief propagation algorithms. Three classes of iteratively decodable channel codes are considered in this study, including low-density parity-check (LDPC), Turbo and repeat-accumulate (RA) codes. The modulation platform presented in this dissertation is a spectrally efficient wideband system employing orthogonal complex spreading sequences (CSSs) to spread information sequences over a wider frequency band in multiple modulation dimensions. Special features of these spreading sequences include their constant envelopes and power output, providing communication range or device battery life advantages. This study shows that multiple layer modulation (MLM) can be used to transmit parallel data streams with improved spectral efficiency compared with single-layer modulation, providing data throughput rates proportional to the number of modulation layers at performances equivalent to single-layer modulation. Alternatively, multiple modulation layers can be used to transmit coded information to achieve improved error performance at throughput rates equivalent to a single layer systemDissertation (MEng (Electronic Engineering))--University of Pretoria, 2007.Electrical, Electronic and Computer Engineeringunrestricte