Research On Green Energy-Saving Wireless Resource Allocation Management For Cognitive Radio Network

The cognitive radio network is composed of a cognitive network and a primary network.The secondary users of the cognitive network can reuse of the allocated spectrum through spectrum sensing and spectrum access,which greatly improves the spectrum utilization rate.In recent years,cognitive radio networks have been widely used in smart homes,military defense,smart grids,health care and other fields.However,the performance gains of cognitive radio networks generate high amounts of energy consumption,increasing network overhead and polluting the environment.In order to response to the call of the government,operators and industry associations for green energy-saving communication,focusing on the problem of green energy-saving wireless resource management in cognitive radio networks,this paper proposes a wireless resource allocation algorithm for energy efficiency optimization in a centralized cooperative spectrum sensing scenario of cognitive radio networks and an energy collection transmission-collection strategy optimization algorithm in cognitive radio networks.This paper mainly includes the following two aspects of research content:(1)The problem of resource allocation based on centralized cooperative spectrum sensing in cognitive radio networks is studied.According to the characteristics of centralized spectrum sensing mechanism and hybrid spectrum sharing mode,an energy efficiency optimization resource allocation algorithm that jointly optimizes the sensing time,the number of users participating in sensing and the transmission power is proposed.Firstly,according to the diversity of the centralized cooperative sensing mechanism fusion rules and the error characteristics of the common control channel,three expressions of global detection rate and global false alarm rate are derived;Considering the deviation between the detection results and the actual situation,four possible detection results are analyzed by using the global detection rate and the global false alarm rate.Secondly in response to these four results,combined with the characteristics of the secondary user transmission in the hybrid spectrum sharing mode,four throughput models and one energy consumption model are proposed,and finally the expression of the system energy efficiency is determined.Jointly considering the sensing time,the number of users participating in the sensing and the transmission power,the optimal energy efficiency can be achieved while ensuring the service quality of secondary users and not interfering with the primary users.Finally,according to Dinkelbach method,the objective function is transformed into a convex function,and Lagrange multipliers are introduced to solve the problem iteratively.The simulation results show that the proposed algorithm improves the energy efficiency,and also shows the influence of different sensing time,the number of users participating in the sensing and the transmission power on the energy efficiency.(2)This paper studies the optimization of transmission-collection strategy in the energy harvesting cognitive radio network.According to the highly dynamic characteristics of wireless communication environment,a strategy optimization algorithm using deep reinforcement learning is proposed,which can select the appropriate working mode and transmission power value according to the environment.Firstly,the Markov decision process is used to model the work of the secondary user by analyzing the state characteristics of the secondary user in each time slot;Secondly,an optimization algorithm based on deep reinforcement learning is proposed,which uses the data generated by the interaction between the secondary user and the environment to train the model;Finally,the trained model can select the appropriate operation mode and transmission power according to the different slot states.The simulation results show that the algorithm has better throughput and guarantees the system to implement self-powered work.