Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

Cross-layer aided energy-efficient routing design for ad hoc networks

In this treatise, we first review some basic routing protocols conceived for ad hoc networks, followed by some design examples of cross-layer operation aided routing protocols. Specifically, cross-layer operation across the PHYsical layer (PHY), the Data Link layer (DL) and even the NETwork layer (NET) is exemplified for improving the energy efficiency of the entire system. Moreover, the philosophy of Opportunistic Routing (OR) is reviewed for the sake of further reducing the system's energy dissipation with the aid of optimized Power Allocation (PA). The system's end-to-end throughput is also considered in the context of a design example

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Intelligent Approaches for Routing Protocols In Cognitive Ad-Hoc Networks

This dissertation describes the CogNet architecture and five cognitive routing protocols designed to function within this architecture. In this document, I first provide detailed modeling and analysis of CogNet architecture and then provide the detailed approach, mathematical analysis, and simulation results for each of the developed cognitive routing protocols. The fundamental idea for these cognitive routing protocols is that a proper and adaptive network topology should be constructed from network nodes based on predictions using cognitive functions and past experience. The nodes in the cognitive radio network employ machine learning techniques to use past experience and make wise decisions by predicting future network conditions. The cognitive protocol architecture is a cross-layer optimized construct where the lower layer knowledge of the wireless medium is shared with the network layer. This dissertation investigates several intelligent approaches for cognitive routing protocols, such as the multi-channel optimized approach, the scalability optimized cognitive approach, the multi-path optimized approach, and the mobility optimized approach. Analytical and simulation results demonstrate that network performance can be increased significantly by applying cognitive routing protocols