Call Admission Control (CAC) and Dynamic Channel Assignments (DCA) are important decision-making problems in mobile cellular communication systems. Current research in mobile communication considers them as two independent problems, although the former greatly depends on the resulting free channels obtained as the outcome of the latter. This book provides a solution to the CAC problem, considering DCA as an integral part of decision-making for call admission. Further, current technical resources ignore movement issues of mobile stations and fluctuation in network load (incoming calls) in the control strategy used for call admission. In addition, the present techniques on call admission offers solution globally for the entire network, instead of considering the cells independently. CAC here has been formulated by two alternative approaches. The first approach aimed at handling the uncertainty in the CAC problem by employing fuzzy comparators. The second approach is concerned with formulation of CAC as an optimization problem to minimize call drop, satisfying a set of constraints on feasibility and availability of channels, hotness of cells, and velocity and angular displacement of mobile stations. Evolutionary techniques, including Genetic Algorithm and Biogeography Based Optimization, have been employed to solve the optimization problems. The proposed approaches outperform traditional methods with respect to grade and quality of services.

Next generation wireless and mobile communication systems are rapidly evolving to satisfy the demands of various network users. Due to the great success and enormous impact of IP networks, high-speed transmission is now possible for both indoor and outdoor wireless systems, internet access and web browsing have become the ruling paradigm for next generation system. It is envisioned that new generation wireless networks and hand-held terminals will support a wide variety of multimedia services such as multimedia web browsing, video and news on demand, mobile office system, stock market information, and so on, to mobile users anywhere, anytime in an uninterrupted and seamless way with low-powered handsets. The characteristics of wireless links, as well as the desire to maintain connectivity while on the move, offer significant challenges to provisioning quality of service and the related performance is of central interest. Since the resources (such as time, frequency and code) in the wireless segments of such networks are very limited, over-dimensioning the network resource is equivalent to poor capital investment, while congestion at busy hours could mean lost calls and lost revenues. It is therefore critical for wireless network designers to utilise these resources efficiently and effectively. In response to the above demand for next generation wireless and mobile communication systems, this book aims at providing a timely and concise reference of the current activities and findings in the relevant technical fields. The primary goal is to address the key technical issues pertaining to the integrated new systems and present novel technical contributions. The book contains 14 invited chapters from prominent researchers working in this area around the world.

A relative newcomer to the field of wireless communications, ad hoc networking is growing quickly, both in its importance and its applications. With rapid advances in hardware, software, and protocols, ad hoc networks are now coming of age, and the time has come to bring together into one reference their principles, technologies, and techniques. The Handbook of Ad Hoc Wireless Networks does exactly that. Experts from around the world have joined forces to create the definitive reference for the field. From the basic concepts, techniques, systems, and protocols of wireless communication to the particulars of ad hoc network routing methods, power, connections, traffic management, and security, this handbook covers virtually every aspect of ad hoc wireless networking. It includes a section that explores several routing methods and protocols directly related to implementing ad hoc networks in a variety of applications. The benefits of ad hoc wireless networks are many, but several challenges remain. Organized for easy reference, The Handbook of Ad Hoc Wireless Networks is your opportunity to gain quick familiarity with the state of the art, have at your disposal the only complete reference on the subject available, and prepare to meet the technological and implementation challenges you'll encounter in practice.

ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.

Discusses Call Admission Control (CAC) is a standard operating procedure responsible for accepting or rejecting calls based on the availability of network resources. It is also used to guarantee good Quality of Service (QoS) to ongoing users. However, there are a number of imprecisions to consider during the admission and handoff processes. These uncertainties arise from the mobility of subscribers and the time-varying nature of key admission factors such as latency and packet loss.These parameters are often imprecisely measured, which has a negative impact on the estimation of a channel spectral efficiency. In mobile networking, greater emphasis is towards delivering good QoS to real-time (RT) applications. It has become increasingly necessary to develop a model capable of handling uncertainties associated with the network in order to improve the quality of decisions relating to CAC. Type-1 and Type-2 Fuzzy Logic Controllers (FLCs) were deployed to allow the CAC to make better decisions in the presence of numerous uncertainties. The model successfully proposed associated meanings and degrees of certainty to the measured values of loss and latency by means of fuzzy sets and Membership Functions (MFs). The results obtained show that the fuzzy-based CAC performs better by reducing the call blocking and call dropping probabilities which are some of the key measurement parameters of QoS in wireless networking.

Call Admission Control in Cellular Networks.