Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation

The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported. Channel equalisation is achieved by training a multilayer perceptrons ANN. A comparative study of the unequalised `soft' decision decoding and the `hard' decision decoding along with the neural equalised `soft' decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalised `hard' decision decoding performs the worst for all values of normalised delayed spread, becoming impractical beyond a normalised delayed spread of 0.6. However, `soft' decision decoding with/without equalisation displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel, the signal-to-noise ratio requirement to achieve a BER of 10−5 for the ANN-based equaliser is ~10 dB lower compared with the unequalised `soft' decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalisation is an effective tool of mitigating the inter-symbol interference

Complex Neural Networks for Audio

Audio is represented in two mathematically equivalent ways: the real-valued time domain (i.e., waveform) and the complex-valued frequency domain (i.e., spectrum). There are advantages to the frequency-domain representation, e.g., the human auditory system is known to process sound in the frequency-domain. Furthermore, linear time-invariant systems are convolved with sources in the time-domain, whereas they may be factorized in the frequency-domain. Neural networks have become rather useful when applied to audio tasks such as machine listening and audio synthesis, which are related by their dependencies on high quality acoustic models. They ideally encapsulate fine-scale temporal structure, such as that encoded in the phase of frequency-domain audio, yet there are no authoritative deep learning methods for complex audio. This manuscript is dedicated to addressing the shortcoming. Chapter 2 motivates complex networks by their affinity with complex-domain audio, while Chapter 3 contributes methods for building and optimizing complex networks. We show that the naive implementation of Adam optimization is incorrect for complex random variables and show that selection of input and output representation has a significant impact on the performance of a complex network. Experimental results with novel complex neural architectures are provided in the second half of this manuscript. Chapter 4 introduces a complex model for binaural audio source localization. We show that, like humans, the complex model can generalize to different anatomical filters, which is important in the context of machine listening. The complex model\u27s performance is better than that of the real-valued models, as well as real- and complex-valued baselines. Chapter 5 proposes a two-stage method for speech enhancement. In the first stage, a complex-valued stochastic autoencoder projects complex vectors to a discrete space. In the second stage, long-term temporal dependencies are modeled in the discrete space. The autoencoder raises the performance ceiling for state of the art speech enhancement, but the dynamic enhancement model does not outperform other baselines. We discuss areas for improvement and note that the complex Adam optimizer improves training convergence over the naive implementation

Efficient channel equalization algorithms for multicarrier communication systems

Blind adaptive algorithm that updates time-domain equalizer (TEQ) coefficients by Adjacent Lag Auto-correlation Minimization (ALAM) is proposed to shorten the channel for multicarrier modulation (MCM) systems. ALAM is an addition to the family of several existing correlation based algorithms that can achieve similar or better performance to existing algorithms with lower complexity. This is achieved by designing a cost function without the sum-square and utilizing symmetrical-TEQ property to reduce the complexity of adaptation of TEQ to half of the existing one. Furthermore, to avoid the limitations of lower unstable bit rate and high complexity, an adaptive TEQ using equal-taps constraints (ETC) is introduced to maximize the bit rate with the lowest complexity. An IP core is developed for the low-complexity ALAM (LALAM) algorithm to be implemented on an FPGA. This implementation is extended to include the implementation of the moving average (MA) estimate for the ALAM algorithm referred as ALAM-MA. Unit-tap constraint (UTC) is used instead of unit-norm constraint (UNC) while updating the adaptive algorithm to avoid all zero solution for the TEQ taps. The IP core is implemented on Xilinx Vertix II Pro XC2VP7-FF672-5 for ADSL receivers and the gate level simulation guaranteed successful operation at a maximum frequency of 27 MHz and 38 MHz for ALAM-MA and LALAM algorithm, respectively. FEQ equalizer is used, after channel shortening using TEQ, to recover distorted QAM signals due to channel effects. A new analytical learning based framework is proposed to jointly solve equalization and symbol detection problems in orthogonal frequency division multiplexing (OFDM) systems with QAM signals. The framework utilizes extreme learning machine (ELM) to achieve fast training, high performance, and low error rates. The proposed framework performs in real-domain by transforming a complex signal into a single 2–tuple real-valued vector. Such transformation offers equalization in real domain with minimum computational load and high accuracy. Simulation results show that the proposed framework outperforms other learning based equalizers in terms of symbol error rates and training speeds

Collaborative adaptive filtering for machine learning

Quantitative performance criteria for the analysis of machine learning architectures and algorithms have long been established. However, qualitative performance criteria, which identify fundamental signal properties and ensure any processing preserves the desired properties, are still emerging. In many cases, whilst offline statistical tests exist such as assessment of nonlinearity or stochasticity, online tests which not only characterise but also track changes in the nature of the signal are lacking. To that end, by employing recent developments in signal characterisation, criteria are derived for the assessment of the changes in the nature of the processed signal. Through the fusion of the outputs of adaptive filters a single collaborative hybrid filter is produced. By tracking the dynamics of the mixing parameter of this filter, rather than the actual filter performance, a clear indication as to the current nature of the signal is given. Implementations of the proposed method show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. This is then extended (in the real domain) from dealing with nonlinearity in general, to a more specific example, namely sparsity. Extensions of adaptive filters from the real to the complex domain are non-trivial and the differences between the statistics in the real and complex domains need to be taken into account. In terms of signal characteristics, nonlinearity can be both split- and fully-complex and complex-valued data can be considered circular or noncircular. Furthermore, by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion. To produce online tests for sparsity, adaptive filters for sparse environments are investigated and a unifying framework for the derivation of proportionate normalised least mean square (PNLMS) algorithms is presented. This is then extended to derive variants with an adaptive step-size. In order to create an online test for noncircularity, a study of widely linear autoregressive modelling is presented, from which a proof of the convergence of the test for noncircularity can be given. Applications of this method are illustrated on examples such as biomedical signals, speech and wind data

Application of Wilcoxon Norm for increased Outlier Insensitivity in Function Approximation Problems

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) ) or Radial Basis Functions(RBF) using some learning rules such as the back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, for the use of a variety of approaches to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The first 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 or a radial basis function neural network, the designer is often faced with the problem of choosing a network of the right size for the task. Using a smaller neural network decreases the cost of computation and increases generalization ability. However, a network which is too small may never solve the problem, while a larger network might be able to. Transmission bandwidth being one of the most precious resources in digital communication, Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response