Coded Modulation Assisted Radial Basis Function Aided Turbo Equalisation for Dispersive Rayleigh Fading Channels

In this contribution a range of Coded Modulation (CM) assisted Radial Basis Function (RBF) based Turbo Equalisation (TEQ) schemes are investigated when communicating over dispersive Rayleigh fading channels. Specifically, 16QAM based Trellis Coded Modulation (TCM), Turbo TCM (TTCM), Bit-Interleaved Coded Modulation (BICM) and iteratively decoded BICM (BICM-ID) are evaluated in the context of an RBF based TEQ scheme and a reduced-complexity RBF based In-phase/Quadrature-phase (I/Q) TEQ scheme. The Least Mean Square (LMS) algorithm was employed for channel estimation, where the initial estimation step-size used was 0.05, which was reduced to 0.01 for the second and the subsequent TEQ iterations. The achievable coding gain of the various CM schemes was significantly increased, when employing the proposed RBF-TEQ or RBF-I/Q-TEQ rather than the conventional non-iterative Decision Feedback Equaliser - (DFE). Explicitly, the reduced-complexity RBF-I/Q-TEQ-CM achieved a similar performance to the full-complexity RBF-TEQ-CM, while attaining a significant complexity reduction. The best overall performer was the RBF-I/Q-TEQ-TTCM scheme, requiring only 1.88~dB higher SNR at BER=10-5, than the identical throughput 3~BPS uncoded 8PSK scheme communicating over an AWGN channel. The coding gain of the scheme was 16.78-dB

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Artificial neural networks for location estimation and co-cannel interference suppression in cellular networks

This thesis reports on the application of artificial neural networks to two important problems encountered in cellular communications, namely, location estimation and co-channel interference suppression. The prediction of a mobile location using propagation path loss (signal strength) is a very difficult and complex task. Several techniques have been proposed recently mostly based on linearized, geometrical and maximum likelihood methods. An alternative approach based on artificial neural networks is proposed in this thesis which offers the advantages of increased flexibility to adapt to different environments and high speed parallel processing. Location estimation provides users of cellular telephones with information about their location. Some of the existing location estimation techniques such as those used in GPS satellite navigation systems require non-standard features, either from the cellular phone or the cellular network. However, it is possible to use the existing GSM technology for location estimation by taking advantage of the signals transmitted between the phone and the network. This thesis proposes the application of neural networks to predict the location coordinates from signal strength data. New multi-layered perceptron and radial basis function based neural networks are employed for the prediction of mobile locations using signal strength measurements in a simulated COST-231 metropolitan environment. In addition, initial preliminary results using limited available real signal-strength measurements in a metropolitan environment are also reported comparing the performance of the neural predictors with a conventional linear technique. The results indicate that the neural predictors can be trained to provide a near perfect mapping using signal strength measurements from two or more base stations. The second application of neural networks addressed in this thesis, is concerned with adaptive equalization, which is known to be an important technique for combating distortion and Inter-Symbol Interference (ISI) in digital communication channels. However, many communication systems are also impaired by what is known as co-channel interference (CCI). Many digital communications systems such as digital cellular radio (DCR) and dual polarized micro-wave radio, for example, employ frequency re-usage and often exhibit performance limitation due to co-channel interference. The degradation in performance due to CCI is more severe than due to ISI. Therefore, simple and effective interference suppression techniques are required to mitigate the interference for a high-quality signal reception. The current work briefly reviews the application of neural network based non-linear adaptive equalizers to the problem of combating co-channel interference, without a priori knowledge of the channel or co-channel orders. A realistic co-channel system is used as a case study to demonstrate the superior equalization capability of the functional-link neural network based Decision Feedback Equalizer (DFE) compared to other conventional linear and neural network based non-linear adaptive equalizers.This project was funded by Solectron (Scotland) Ltd

Bacterial Foraging Based Channel Equalizers

A channel equalizer is one of the most important subsystems in any digital communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances ‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers perform better. Natural selection tends to eliminate animals with poor “foraging strategies” and favor the propagation of genes of those animals that have successful foraging strategies since they are more likely to enjoy reproductive success. After many generations, poor foraging strategies are either eliminated or shaped into good ones (redesigned). Logically, such evolutionary principles have led scientists in the field of “foraging theory” to hypothesize that it is appropriate to model the activity of foraging as an optimization process. This thesis presents an investigation on design of bacterial foraging based channel equalizer for digital communication. Extensive simulation studies shows that the performance of the proposed receiver is close to optimal receiver for variety of channel conditions. The proposed receiver also provides near optimal performance when channel suffers from nonlinearities

Performance Evaluation of Various Training Algorithms for ANN Equalization in Visible Light Communications with an Organic LED

This paper evaluates the effect of training algorithms in an artificial neural network (ANN) equalizer for a feedforward multi-layer perceptron configuration in visible light communication systems using a low bandwidth organic light source. We test the scaled conjugate-gradient, conjugate-gradient backpropagation and Levenberg-Marquardt back propagation (LM) algorithms with 5, 10, 20, 30, and 40 neurons. We show that, LM offers superior bit error rate performance in comparison to other training algorithms based on the mean square error. The training methods can be selected based on the trade-off between complexity and performance

Adaptive non linear system identification and channel equalization usinf functional link artificial neural network

In system theory, characterization and identification are fundamental problems. When the plant behavior is completely unknown, it may be characterized using certain model and then, its identification may be carried out with some artificial neural networks(ANN) like multilayer perceptron(MLP) or functional link artificial neural network(FLANN) using some learning rules such as back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The primary aim of the present thesis is to provide a framework for the systematic design of adaptation laws for nonlinear system identification and channel equalization. While constructing an artificial neural network the designer is often faced with the problem of choosing a network of the right size for the task. The advantages of using a smaller neural network are cheaper cost of computation and better generalization ability. However, a network which is too small may never solve the problem, while a larger network may even have the advantage of a faster learning rate. Thus it makes sense to start with a large network and then reduce its size. For this reason a Genetic Algorithm (GA) based pruning strategy is reported. GA is based upon the process of natural selection and does not require error gradient statistics. As a consequence, a GA is able to find a global error minimum. Transmission bandwidth is one of the most precious resources in digital communication systems. Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response. When the amplitude and the envelope delay response are not constant within the bandwidth of the filter, the channel distorts the transmitted signal causing intersymbol interference (ISI). The addition of noise during propagation also degrades the quality of the received signal. All the signal processing methods used at the receiver's end to compensate the introduced channel distortion and recover the transmitted symbols are referred as channel equalization techniques.When the nonlinearity associated with the system or the channel is more the number of branches in FLANN increases even some cases give poor performance. To decrease the number of branches and increase the performance a two stage FLANN called cascaded FLANN (CFLANN) is proposed.This thesis presents a comprehensive study covering artificial neural network (ANN) implementation for nonlinear system identification and channel equalization. Three ANN structures, MLP, FLANN, CFLANN and their conventional gradient-descent training methods are extensively studied. Simulation results demonstrate that FLANN and CFLANN methods are directly applicable for a large class of nonlinear control systems and communication problems