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 new energy-efficient local metric for Channel-Aware Geographic-Informed Forwarding (CAGIF) in wireless sensor networks

This paper proposes a new energy-efficient local metric, termed as efficient advancement metric (EAM), for channel aware geographic-informed forwarding (CAGIF) algorithm, which studies the optimal selection of the relay nodes by taking into account the underlying channel conditions. The proposed metric considers not only the forward distance but also the packet successful probability under a certain channel condition, rather than purely maximizing the forward distance, to choose the most energy-efficient relay node in the geographic- informed routing protocol in a CDMA-based wireless sensor network. The proposed algorithm only requires that nodes have the knowledge of their own location information and the location information of the source and destination nodes. The protocol assumes that nodes could vary transmission range to search for the optimal relay nodes according to this metric. An analytical framework is provided and numerical results show that there exists an optimum distance of a relay node under a certain channel condition to maximize the EAM for any fading conditions.Accepted versio