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

On the modeling of WCDMA system performance with propagation data

The aim of this study was to develop calculation methods for estimating the most important system level performance characteristics of the WCDMA radio network (i.e. network capacity and coverage) in the presence of interference from various sources. The calculation methods described in this work enable the fast design of radio systems with a reasonable degree of accuracy, where different system parameters, propagation conditions and networks as well as frequency scenarios can be easily tested. The work also includes the development and verification of a propagation model for a microcellular environment. Traditionally, system level performance figures have been retrieved using system simulations where the radio network has been modeled as accurately as possible. This has included base stations and mobile stations, propagation models, traffic models and mobility models. Various radio resource management (RRM) algorithms, such as power controls and handovers have also been modeled. However, these system simulations are very complex and time consuming and typically the models are difficult to modify. The idea behind this work is to use the main statistical parameters retrieved from accurate, case specific propagation models and to use these statistics as input for the developed analytical radio network models. When used as output from these analytical models we are able to obtain the performance measures of the network. The specific application area for the developed methods is the evaluation of the effect of the interference from the adjacent frequency channels. Adjacent channel interference decreases the efficiency of the usage of the electromagnetic spectrum i.e. the spectral efficiency. The aim of a radio system design is to ensure that the reduction in the spectral efficiency is as low as possible. This interference may originate from the same or a different radio system and from the same or another operator's network. The strength of this interference is dependent on the system parameters and the network layout. The standard questions regarding adjacent system interference between different operators' network are what guard band is needed between the radio carriers in order to maintain the quality of the network or what are the main mobile and network parameters, such as adjacent channel emission levels or adjacent channel selectivity, required in order to achieve satisfactory network performance. With the developed method proposed here it is possible to answer these questions with reasonable accuracy. One important aspect of network performance is the radio wave propagation environment for which the radio systems are designed. This thesis presents methods evaluating radio wave propagation, especially for cases where the base station antenna is below the rooftops, i.e. in the case of microcellular network environments. The developed microcellular propagation model has been developed for network planning purposes and it has been verified using numerous field propagation measurements. The model can be used in cases where the mobile station is located either indoors or outdoors.reviewe

Robust Schemes to Enhance Energy Consumption Efficiency for Millimeter Wave-Based Microcellular Network in Congested Urban Environments

Future wireless communication networks will be largely characterized by small cell deployments, typically on the order of 200 meters of radius/cell, at most. Meanwhile, recent studies show that base stations (BS) account for about 80 to 95 % of the total network power. This simply implies that more energy will be consumed in the future wireless network since small cell means massive deployment of BS. This phenomenon makes energy-efficient (EE) control a central issue of critical consideration in the design of future wireless networks. This paper proposes and investigates (the performance of) two different energy-saving approaches namely, adaptive-sleep sectorization (AS), adaptive hybrid partitioning schemes (AH) for small cellular networks using smart antenna technique. We formulated a generic base-model for the above-mentioned schemes and applied the spatial Poisson process to reduce the system complexity and to improve flexibility in the beam angle reconfiguration of the adaptive antenna, also known as a smart antenna (SA). The SA uses the scalable algorithms to track active users in different segments/sectors of the microcell, making the proposed schemes capable of targeting specific users or groups of users in periods of sparse traffic, and capable of performing optimally when the network is highly congested. The capabilities of the proposed smart/adaptive antenna approaches can be easily adapted and integrated into the massive MIMO for future deployment. Rigorous numerical analysis at different orders of sectorization shows that among the proposed schemes, the AH strategy outperforms the AS in terms of energy saving by about 52 %. Generally, the proposed schemes have demonstrated the ability to significantly increase the power consumption efficiency of micro base stations for future generation cellular systems, over the traditional design methodologies

Modeling and characterization of urban radio channels for mobile communications

Results of this thesis contribute in modeling and characterization of radio channels for future mobile communications. The results are presented mainly in three parts: a) modeling of propagation mechanisms, b) methodology of developing a propagation model, c) characterization of urban radio channel. One of the main propagation physical phenomena that have an important role in diverting signals to non line of sight scenarios is the diffraction process. This thesis proposes diffraction coefficients that have better agreement with finite difference time domain solution and rigorous diffraction theory than the coefficient commonly used in propagation predictions for mobile communications. The importance of diffuse scattering has also been investigated and showed that this physical process may have a key role in urban propagation, with a particular impact on the delay spread and angular spread of the signal at the receiver. This thesis proposes wideband propagation models for main and perpendicular streets of urban street grids. The propagation models are ray-based and are given in explicit mathematical expressions. Each ray is characterized in terms of its amplitude, delay, and angle of arrival, angle of departure for vertical and horizontal polarizations. Each of these characteristics is given in a closed mathematical form. Having wideband propagation model in explicit expression makes its implementation easy and computation fast. Secondary source modeling approach for perpendicular streets has also been introduced in this thesis. The last part of the thesis deals with characterization of urban radio channels for extracting parameters that help in successful design of mobile communication systems. Knowledge of channel characteristics enables reaching optimum trade off between system performance and complexity. This thesis analyzes measurement results at 2 GHz to extract channel parameters in terms of Rake finger characteristics in order to get information that helps to optimize Rake receiver design for enhanced-IMT2000 systems. Finger life distance has also been investigated for both micro- and small cell scenarios. This part of the thesis also presents orthogonality factor of radio channel for W-CDMA downlink at different bandwidths. Characterization of dispersion metrics in delay and angular domains for microcellular channels is also presented at different base station antenna heights. A measure of (dis-) similarity between multipath components in terms of separation distance in delay and angular domains is introduced by the concept of distance function, which is a step toward in development of algorithm extraction and analysis multipath clustering. In summary, the significant contributions of the thesis are in three parts. 1) Development of new diffraction coefficients and corrections of limitations of existing one for accurate propagation predictions for mobile communications. 2) Development of wideband propagation models for urban street grid. The novelty of the model is the development in explicit mathematical expressions. The developed models can be used to study propagation problem in microcellular urban street grids. 3) Presenting channel parameters that will help in the design of future mobile communication systems (enhanced-IMT2000), like number of active fingers, finger life distance, and orthogonality factors for different bandwidths. In addition, a technique based on multipath separation distance is proposed as a step toward in development of algorithms for extraction and analysis of multipath clusters.reviewe