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

A novel reliability based routing protocol for power aware communications in wireless sensor networks

Abstract—In this paper a Rayleigh fading model based reliability-centric routing algorithm is proposed for Wireless Sensor Networks (WSNs). The proposed scheme is optimized with respect to minimal power consumption to improve longevity as well as to ensure reliable packet transmission to the Base Station (BS). Reliability is guaranteed by selecting path over which the probability of correct packet reception of the transmitted packet will exceed a predefined threshold at the BS. It will be pointed out that reliable and power efficient packet forwarding over WSN can be mapped into a constrained optimization problem. This optimization is then reduced to a shortest path problem with specific link metrics solved in polynomial time. Index Terms—wireless sensor networks, reliability, Rayleigh fading model, power awarenes