The work in this thesis concerns the use of reinforcement learning solutions to re-source allocation problems in channelised cellular networks. The methodology of re-inforcement learning techniques was chosen for application to these problems due to its capability of finding efficient policies in a fully on-line, adaptable manner, without requiring specific environment models. All of the presented agent architectures are assumed to simultaneously learn and perform network control functions in a totally on-line and unsupervised manner, and agents are developed with a view to real-world implementability by focussing on techniques that have low resource requirements and make use of only local system information.

Call quality has been crucial from the inception of telecommunication networks. Operators need to monitor call quality from the end-user's perspective, in order to retain subscribers and reduce subscriber 'churn'. Operators worry not only about call quality and interconnect revenue loss, but also about network connectivity issues in areas where mobile network gateways are prevalent. Bandwidth quality as experienced by the end-user is equally important in helping operators to reduce churn. The parameters that network operators use to improve call quality are mainly from the end-user's perspective. These parameters are usually ASR (answer seizure ratio), PDD (postdial delay), NER (network efficiency ratio), the number of calls for which these parameters have been analyzed and successful calls. Operators use these parameters to evaluate and optimize the network to meet their quality requirements. Analysis of speech quality is a major arena for research. Traditionally, users' perception of speech quality has been measured offline using subjective listening tests. Such tests are, however, slow, tedious and costly. An alternative method is therefore needed; one that can be automatically computed on the subscriber's handset, be available to the operator as well as to subscribers and, at the same time, provide results that are comparable with conventional subjective scores. QMeter® 'a set of tools for signal and bandwidth measurement that have been developed bearing in mind all the parameters that influence call and bandwidth quality experienced by the end-user' addresses these issues and, additionally, facilitates dynamic tariff propositions which enhance the credibility of the operator. This research focuses on call quality parameters from the end-user's perspective. The call parameters used in the research are signal strength, successful call rate, normal drop call rate, and hand-over drop rate. Signal strength is measured for every five milliseconds of an active call and average signal strength is calculated for each successful call. The successful call rate, normal drop rate and hand-over drop rate are used to achieve a measurement of the overall call quality. Call quality with respect to bundles of 10 calls is proposed. An attempt is made to visualize these parameters for better understanding of where the quality is bad, good and excellent. This will help operators, as well as user groups, to measure quality and coverage. Operators boast about their bandwidth but in reality, to know the locations where speed has to be improved, they need a tool that can effectively measure speed from the end-user's perspective. BM (bandwidth meter), a tool developed as a part of this research, measures the average speed of data sessions and stores the information for analysis at different locations. To address issues of quality in the subscriber segment, this research proposes the varying of tariffs based on call and bandwidth quality. Call charging based on call quality as perceived by the end-user is proposed, both to satisfy subscribers and help operators to improve customer satisfaction and increase average revenue per user. Tariff redemption procedures are put forward for bundles of 10 calls and 10 data sessions. In addition to the varying of tariffs, quality escalation processes are proposed. Deploying such tools on selected or random samples of users will result in substantial improvement in user loyalty which, in turn, will bring operational and economic advantages.

Call quality has been crucial from the inception of telecommunication networks. Operators need to monitor call quality from the end-user¿s perspective, in order to retain subscribers and reduce subscriber ¿churn¿. Operators worry not only about call quality and interconnect revenue loss, but also about network connectivity issues in areas where mobile network gateways are prevalent. Bandwidth quality as experienced by the end-user is equally important in helping operators to reduce churn. The parameters that network operators use to improve call quality are mainly from the end-user¿s perspective. These parameters are usually ASR (answer seizure ratio), PDD (postdial delay), NER (network efficiency ratio), the number of calls for which these parameters have been analyzed and successful calls. Operators use these parameters to evaluate and optimize the network to meet their quality requirements. Analysis of speech quality is a major arena for research. Traditionally, users¿ perception of speech quality has been measured offline using subjective listening tests. Such tests are, however, slow, tedious and costly. An alternative method is therefore needed; one that can be automatically computed on the subscriber¿s handset, be available to the operator as well as to subscribers and, at the same time, provide results that are comparable with conventional subjective scores. QMeter® ¿ a set of tools for signal and bandwidth measurement that have been developed bearing in mind all the parameters that influence call and bandwidth quality experienced by the end-user ¿ addresses these issues and, additionally, facilitates dynamic tariff propositions which enhance the credibility of the operator. This research focuses on call quality parameters from the end-user¿s perspective. The call parameters used in the research are signal strength, successful call rate, normal drop call rate, and hand-over drop rate. Signal strength is measured for every five milliseconds of an active call and average signal strength is calculated for each successful call. The successful call rate, normal drop rate and hand-over drop rate are used to achieve a measurement of the overall call quality. Call quality with respect to bundles of 10 calls is proposed. An attempt is made to visualize these parameters for better understanding of where the quality is bad, good and excellent. This will help operators, as well as user groups, to measure quality and coverage. Operators boast about their bandwidth but in reality, to know the locations where speed has to be improved, they need a tool that can effectively measure speed from the end-user¿s perspective. BM (bandwidth meter), a tool developed as a part of this research, measures the average speed of data sessions and stores the information for analysis at different locations. To address issues of quality in the subscriber segment, this research proposes the varying of tariffs based on call and bandwidth quality. Call charging based on call quality as perceived by the end-user is proposed, both to satisfy subscribers and help operators to improve customer satisfaction and increase average revenue per user. Tariff redemption procedures are put forward for bundles of 10 calls and 10 data sessions. In addition to the varying of tariffs, quality escalation processes are proposed. Deploying such tools on selected or random samples of users will result in substantial improvement in user loyalty which, in turn, will bring operational and economic advantages.