Indoor gigabit optical wireless communications: challenges and possibilities

Indoor Gigabit optical wireless communication systems have the potential to offer multiple high-speed data services that can be delivered to homes via an optical fibre cable in the near future. In this paper we will discuss the challenges involved in the design of such systems and future possible advances. Results from a recent cellular Gigabit prototype link will also be presented and discussed

Wavelet—Artificial Neural Network Receiver for Indoor Optical Wireless Communications

The multipath induced intersymbol interference (ISI) and fluorescent light interference (FLI) are the two most important system impairments that affect the performance of indoor optical wireless communication (OWC) systems. The presence of either incurs a high optical power penalty (OPP) and hence the interferences should be mitigated with suitable techniques to ensure optimum system performance. The discrete wavelet transform (DWT) and the artificial neural network (ANN) based receiver to mitigate the effect of FLI and ISI has been proposed in the previous study for the one-off keying (OOK) modulation scheme. It offers performance improvement compared to the traditional methods of employing a high pass filter (HPF) and a finite impulse response (FIR) equalizer. In this paper, the investigation of the DWT-ANN based receiver for baseband modulation techniques including OOK, pulse position modulation (PPM) and digital pulse interval modulation (DPIM) are reported. The proposed system is implemented using digital signal processing (DSP) board and results are verified by comparison with simulation data

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

Performance of the wavelet-transform-neural network based receiver for DPIM in diffuse indoor optical wireless links in presence of artificial light interference

Artificial neural network (ANN) has application in communication engineering in diverse areas such as channel equalization, channel modeling, error control code because of its capability of nonlinear processing, adaptability, and parallel processing. On the other hand, wavelet transform (WT) with both the time and the frequency resolution provides the exact representation of signal in both domains. Applying these signal processing tools for channel compensation and noise reduction can provide an enhanced performance compared to the traditional tools. In this paper, the slot error rate (SER) performance of digital pulse interval modulation (DPIM) in diffuse indoor optical wireless (OW) links subjected to the artificial light interference (ALI) is reported with new receiver structure based on the discrete WT (DWT) and ANN. Simulation results show that the DWT-ANN based receiver is very effective in reducing the effect of multipath induced inter-symbol interference (ISI) and ALI

Performance Evaluation of DS-CDMA Receivers Using Genetic Algorithm

Direct sequence-code division multiple access (DS-CDMA) technique is used in cellular systems where users in the cell are separated from each other with their unique spreading codes. In recent times DS-CDMA has been used extensively. These systems suffers from multiple access interference (MAI) due to other users transmitting in the cell, channel inter symbol interference (ISI) due to multipath nature of channels in presence of additive white Gaussian noise(AWGN). Spreading codes play an important role in multiple access capacity of DS-CDMA system. M-sequences, gold sequences etc., has been traditionally used as spreading codes in DS-CDMA. These sequences are generated by shift registers and periodic in nature. So these sequences are less in number and also limits the security. This thesis presents an investigation on use of new type of DS CDMA receiver called Genetic Algorithm based DS-CDMA receiver. Genetic Algorithm is robust optimization technique and does not fall into local minima hence this gives better weight optimization of any system. This Thesis investigates the performance of GA based DS-CDMA communication using gold code sequences. Extensive simulation studies demonstrate the performance of the different linear and nonlinear DS-CDMA receivers like RAKE receiver, matched filter (MF) receiver, minimum mean square error (MMSE) receiver using gold sequences and the performance have been compared with GA based receiver

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

Performance Evaluation of Different DS-CDMA Receivers Using Chaotic Sequences

Direct sequence-code division multiple access (DS-CDMA) technique is used in cellular systems where users in the cell are separated from each other with their unique spreading codes. In recent times DS-CDMA has been used extensively. These systems suffers from multiple access interference (MAI) due to other users transmitting in the cell, channel inter symbol interference (ISI) due to multipath nature of channels in presence of additive white Gaussian noise(AWGN). Spreading codes play an important role in multiple access capacity of DS-CDMA system. M-sequences, gold sequences etc., has been traditionally used as spreading codes in DS-CDMA. These sequences are generated by shift registers and periodic in nature. So these sequences are less in number and also limits the security. This thesis presents an investigation on use of new type of sequences called chaotic sequences for DS-CDMA system. These sequences are generated by chaotic maps. First of all, chaotic sequences are easy to generate and store. Only a few parameters and functions are needed even for very long sequences. In addition, an enormous number of different sequences can be generated simply by changing its initial condition. . Chaotic sequences are deterministic, reproducible, uncorrelated and random-like, which can be very helpful in enhancing the security of transmission in communication. This Thesis investigates the performance of chaotic sequences in DS-CDMA communication systems using various receiver techniques. Extensive simulation studies demonstrate the performance of the different linear and nonlinear DS-CDMA receivers like RAKE receiver, matched filter (MF) receiver, minimum mean square error (MMSE) receiver and Volterra receiver using chaotic sequences and the performance have been compared with gold sequences

A review of gallium nitride LEDs for multi-gigabit-per-second visible light data communications

The field of visible light communications (VLC) has gained significant interest over the last decade, in both fibre and free-space embodiments. In fibre systems, the availability of low cost plastic optical fibre (POF) that is compatible with visible data communications has been a key enabler. In free-space applications, the availability of hundreds of THz of the unregulated spectrum makes VLC attractive for wireless communications. This paper provides an overview of the recent developments in VLC systems based on gallium nitride (GaN) light-emitting diodes (LEDs), covering aspects from sources to systems. The state-of-the-art technology enabling bandwidth of GaN LEDs in the range of >400 MHz is explored. Furthermore, advances in key technologies, including advanced modulation, equalisation, and multiplexing that have enabled free-space VLC data rates beyond 10 Gb/s are also outlined

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