Semi-blind adaptive beamforming for high-throughput quadrature amplitude modulation systems

A semi-blind adaptive beamforming scheme is proposed for wireless systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, equal to the number of receiver antenna arrays elements, are first utilised to provide a rough initial least squares estimate of the beamformer's weight vector. A concurrent constant modulus algorithm and soft decision-directed scheme is then applied to adapt the beamformer. This semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution, as demonstrated in our simulation study

QR factorization equalisation scheme for mode devision multiplexing transmission in fibre optics

Optical communication systems play a major role in handling worldwide Internet traffic. Internet traffic has been increasing at a dramatic rate and the current optical network infrastructure may not be able to support the traffic growth in a few decades. Mode division multiplexing is introduced as a new emerging technique to improve the optical network capacity by the use of the light modes as individual channels. One of the main issues in MDM is mode coupling which is a physical phenomenon when light modes exchange their energy between each other during propagation through optical fiber resulting in inter-symbol interference (ISI). Many studies based on Least Mean Square (LMS) and Recursive Least Square (RLS) have taken place to mitigate the mode coupling effect. Still, most approaches have high computational complexity and hinders high-speed communication systems. Blind equalisation approach does not need training signals, thus, will reduce the overhead payload. On the other hand, QR factorization shows low computational complexity in the previous research in the radio domain. The combination of these two concepts shows significant results, as the use of low complexity algorithms reduces the processing needed to be done by the communication equipment, resulting in more cost effective and smaller equipment, while having no training signal saves the bandwidth and enhances the overall system performance. To the best knowledge of the researcher, blind equalisation based on QR factorization technique has been not used in MDM equalisation to date. The research goes through the four stages of the design research methodology (DRM) to achieve the purpose of the study. The implementation stage is taken two different simulators has been used, the first one which is the optical simulator is used to collect the initial optical data then, MATLAB is used to develop the equalisation scheme. The development starts with the derivation of the system’s transfer function (H) to be used as the input to the developed equalizer. Blind equalisation based on QR factorization is chosen as a way to introduce an efficient equalization to mitigate ISI by narrowing the pulse width. The development stages include a stage where the channel estimation is taken place. Statistical properties based on the standard deviation (STD) of the powers of the input and output signals has been used for the blind equalisation’s channel estimation part. The proposed channel estimation way has the ability in estimating the channel with an overall mean square error (MSE) of 0.176588301 from the initial transmitted signal. It is found that the worst channel has an MSE of 0.771365 from the transmitted signal, while the best channel has and MSE of 0.000185 from the transmitted signal. This is done by trying to avoid the issues accompanied with the development of the previous algorithms that have been utilized for the same goal. The algorithm mentioned in the study reduces the computational complexity problem which is one of the main issues that accompany currently used tap filter algorithms, such as (LMS) and (RLS). The results from this study show that the developed equalisation scheme has a complexity of O(N) compared with O(N2) for RLS and at the same time, it is faster than LMS as its calculation CPU time is equal to 0.005242 seconds compared with 0.0077814 seconds of LMS. The results are only valid for invertible and square channel matrices

The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions

Bibliography: leaves. 63-66.Neural networks have been applied to a number of problems over the past few years. One of the emerging applications of neural networks is adaptive communication channel equalisation. This area of research has become prominent due to the reformulation of the equalisation problem as a classification problem. Viewing equalisation as a classification problem allows researchers to apply the knowledge gained from other fields to equalisation. A wide variety of neural network structures have been suggested to equalise communication channels. Each structure may in turn have a number of different possible algorithms to train the equaliser. A neural network is essentially a non-linear classifier; in general a neural network is able to classify data by employing a non-linear function. The primary subject of this dissertation is the comparative performance of neural networks employing non-localised basis (non-linear) functions (Multi-layer Perceptron) versus those employing localised basis functions (Radial Basis Function Network)

Algorithms and Subsystems for Next Generation Optical Networks

This thesis investigates algorithms and subsystems for digital coherent optical networks to alleviate system requirements and enable spectrally efficient systems. Spectral shaping of individual channel is investigated to mitigate backreflections in bi-directional Passive Optical Network (PON) enabling more than 1000 users operating at 10 Gbit/s. It is then shown that temporal delay skews, caused by misalignment in the coherent receiver, induce a large penalty for Nyquist filtered signals. An adaptive 4×4 equaliser is developed to compensate the imperfections dynamically. This is subsequently demonstrated experimentally with Polarisation Division Multiplexed (PDM) Quadrature Phase Shift Keying (QPSK) and 16-level Quadrature Amplitude Modulation (QAM). Furthermore, a modified blind equaliser is designed to adaptively compensate for unknown amount of Chromatic Dispersion (CD). The equaliser is demonstrated experimentally using 10.7 GBd PDM-QPSK transmission over 5,200 km. To simplify the computational complexity of the equalisers a multiplier free update scheme is explored in simulations. Optical frequency combs are investigated as phase and frequency synchronised sub- carrier sources for Dense Wavelength Division Multiplexing (DWDM) systems. The effect of phase synchronisation between sub-channels of a superchannel is examined in simulations without showing performance deviation when no additional optical or digital processing is applied. Afterwards, the transmission performance of two generation techniques implementing 400 Gbit/s superchannels, utilising PDM-16QAM, is evaluated. Although, the average performance of the two combs is identical subchannel fluctuations are observed due to uneven spectral profile. Carrier Phase Estimation (CPE) is explored for both single channel and superchannels systems. An equaliser interleaved with CPE, is explored for PDM-64QAM with minor improvement. Alternatively, Digital Coherence Enhancement (DCE) allowed the detection of 6 GBd PDM-64QAM with a 1.4 MHz linewidth laser, an order of magnitude improvement in linewidth tolerance. Finally, a joint CPE across a comb superchannel is demonstrated with a factor of 5 tolerance improvement

Development of Fuzzy System Based Channel Equalisers

Channel equalisers are used in digital communication receivers to mitigate the effects of inter symbol interference (ISI) and inter user interference in the form of co-channel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise (AWGN). An equaliser uses a large part of the computations involved in the receiver. Linear equalisers based on adaptive filtering techniques have long been used for this application. Recently, use of nonlinear signal processing techniques like artificial neural networks (ANN) and radial basis functions (RBF) have shown encouraging results in this application. This thesis presents the development of a nonlinear fuzzy system based equaliser for digital communication receivers. The fuzzy equaliser proposed in this thesis provides a parametric implementation of symbolby-symbol maximum a-posteriori probability (MAP) equaliser based on Bayes’s theory. This MAP equaliser is also called Bayesian equaliser. Its decision function uses an estimate of the noise free received vectors, also called channel states or channel centres. The fuzzy equaliser developed here can be implemented with lower computational complexity than the RBF implementation of the MAP equaliser by using scalar channel states instead of channel states. It also provides schemes for performance tradeoff with complexity and schemes for subset centre selection. Simulation studies presented in this thesis suggests that the fuzzy equaliser by using only 10%-20% of the Bayesian equaliser channel states can provide near optimal performance. Subsequently, this fuzzy equaliser is modified for CCI suppression and is termed fuzzy–CCI equaliser. The fuzzy–CCI equaliser provides a performance comparable to the MAP equaliser designed for channels corrupted with CCI. However the structure of this equaliser is similar to the MAP equaliser that treats CCI as AWGN. A decision feedback form of this equaliser which uses a subset of channel states based on the feedback state is derived. Simulation studies presented in this thesis demonstrate that the fuzzy–CCI equaliser can effectively remove CCI without much increase in computational complexity. This equaliser is also successful in removing interference from more than one CCI sources, where as the MAP equalisers treating CCI as AWGN fail. This fuzzy–CCI equaliser can be treated as a fuzzy equaliser with a preprocessor for CCI suppression, and the preprocessor can be removed under high signal to interference ratio condition

Machine learning for performance improvement of periodic NFT-based communication system

We compare performance of several machine learning methods, including support vector machine, k-nearest neighbours, k-means clustering, and Gaussian mixture model, used for increasing transmission reach in the optical communication system based on the periodic nonlinear Fourier transform signal processin

Adaptive equalisation for fading digital communication channels

This thesis considers the design of new adaptive equalisers for fading digital communication channels. The role of equalisation is discussed in the context of the functions of a digital radio communication system and both conventional and more recent novel equaliser designs are described. The application of recurrent neural networks to the problem of equalisation is developed from a theoretical study of a single node structure to the design of multinode structures. These neural networks are shown to cancel intersymbol interference in a manner mimicking conventional techniques and simulations demonstrate their sensitivity to symbol estimation errors. In addition the error mechanisms of conventional maximum likelihood equalisers operating on rapidly time-varying channels are investigated and highlight the problems of channel estimation using delayed and often incorrect symbol estimates. The relative sensitivity of Bayesian equalisation techniques to errors in the channel estimate is studied and demonstrates that the structure's equalisation capability is also susceptible to such errors. Applications of multiple channel estimator methods are developed, leading to reduced complexity structures which trade performance for a smaller computational load. These novel structures are shown to provide an improvement over the conventional techniques, especially for rapidly time-varying channels, by reducing the time delay in the channel estimation process. Finally, the use of confidence measures of the equaliser's symbol estimates in order to improve channel estimation is studied and isolates the critical areas in the development of the technique — the production of reliable confidence measures by the equalisers and the statistics of symbol estimation error bursts