Wireless Channel Equalization in Digital Communication Systems

Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential. The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation. The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4. For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research. Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases

Performance Analysis of Adaptive Equalizers Over Multipath Faded Channels: Error Vector Magnitudes

Due to the increasing popularity of digital transmission systems, the need for channel equalizers has been acknowledged. These techniques are designed to counteract the effects of the inter-symbol interference (ISI) caused by the communication channels. An adaptive equalizer is used to operate on the output of a channel in order to provide an approximation of the transmission medium. An adaptive equalizer usually requires a training period to operate successfully. This method eliminates the effects of the wireless transmission channel and allows the subsequent symbol modulation. The paper presents an overview of the various adaptive equalizers, such as the least mean squares (LMS), decision feedback equalizers (DFE), and the Recursive least squares (RLS). It also explores their performance in terms of error vector magnitudes (EVM) over Rician and Rayleigh channels. The paper looks into the effects of adaptive equalizers on various digital modulation techniques for rectangular quadrature phase shift, amplitude modulation, such as the BPSK, QPSK, 4-QAM, 16-QAM, 64-QAM and 256-QAM. These modulations are analysed and measured in terms of symbol error rates and number of incorrect symbols

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

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

Designing an equalizer structure using gradient descent algorithms

In recent years, a growing field of research in “Adaptive Systems” has resulted in a variety of adaptive automatons whose characteristics in limited ways resemble certain behaviors of living systems and biological adaptive processes. The essential and principal property of the adaptive systems is its time-varying, self-adjusting performance by using a process called “learning” from its environment. A channel equalizer is a very good example of an adaptive system, which has been considered in this work to assess its performance with reference to various novel learning algorithms developed. The two main threats for the digital communication systems are Inter-symbol Interference (ISI) and the presence of noise in the channels which are both time varying. So, for rapidly varying channel characteristics, the equalizer too need to be adaptive. In order to combat with such problems various adaptive equalizers have been proposed. Particularly, when the decision boundary is highly nonlinear, the classical equalizers (so called linear ones) do not perform satisfactorily. The use of Artificial Neural Networks (ANNs) provides the required nonlinear decision boundary. The Back Propagation (BP) algorithm revolutionized the use of ANNs in diverse fields of science and engineering. The main problem with this algorithm is its slow rate of convergence. But the high speed digital communication systems, in the presence of rapidly fading channels, demand for faster training. To overcome this problem a faster method of training the neural network using RLS algorithm is proposed in this thesis work. But both the BP and RLS based BP algorithms belong to the family of Gradient-based algorithms, which have the inherent problem of getting trapped in local minima. Since obtaining a global solution is the main criterion for any adaptive system, an efficient search technique is highly desirable. Tabu Search serves this purpose

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

Development of a Novel Equaliser for Communication Channels using Tabu search Technique in Neural Network Paradigm

In recent years, a growing field of research in “Adaptive Systems” has resulted in a variety of adaptive automatons whose characteristics in limited ways resemble certain behaviors of living systems and biological adaptive processes. The essential and principal property of the adaptive systems is its time-varying, self-adjusting performance by using a process called “learning” from its environment. A channel equalizer is a very good example of an adaptive system, which has been considered in this work to assess its performance with reference to various novel learning algorithms developed. The two main threats for the digital communication systems are Inter-symbol Interference (ISI) and the presence of noise in the channels which are both time varying. So, for rapidly varying channel characteristics, the equalizer too need to be adaptive. In order to combat with such problems various adaptive equalizers have been proposed. Particularly, when the decision boundary is highly nonlinear, the classical equalizers (so called linear ones) do not perform satisfactorily. The use of Artificial Neural Networks (ANNs) provides the required nonlinear decision boundary. The Back Propagation (BP) algorithm revolutionized the use of ANNs in diverse fields of science and engineering. The main problem with this algorithm is its slow rate of convergence. But the high speed digital communication systems, in the presence of rapidly fading channels, demand for faster training. To overcome this problem a faster method of training the neural network using RLS algorithm is proposed in this thesis work. But both the BP and RLS based BP algorithms belong to the family of Gradient-based algorithms, which have the inherent problem of getting trapped in local minima. Since obtaining a global solution is the main criterion for any adaptive system, an efficient search technique is highly desirable. Tabu Search serves this purpose. The popularity of Tabu Search (TS) has grown significantly in the past few years as a global search technique. In this dissertation, it is proposed to find the so-called optimal values of the ANN parameters (slopes and weights) for channel equalization. Results show that the use of TS for adapting the weights and slopes for an ANN not only improves the performance of the equalizer but also reduces the structural complexity of the ANN