Resource Allocation Algorithm For Energy-efficient Cognitive Radio System

With the continuous promotion of information technology in the world and the widespread use of a large number of intelligent terminal devices,people’s demand for data rate is increasingly high.At the same time,due to the development of the Internet of Things technology and 5G communication,many emerging technologies have gradually entered people’s lives,such as driverless,smart home appliances,etc.The application and development of such technologies has increased the demand for the number of terminal equipment,and the user’s requirements for the quality of communication service are also increasing,resulting in an increasingly shortage of available spectrum resources.To effectively solve this problem,cognitive radio(CR)technology has attracted much attention.In addition,in the context of limited global energy,cognitive radio systems are faced with high energy loss.However,the current research mainly focuses on how to improve system throughput,system efficiency and user communication quality,without too much research on system energy efficiency.In response to the call of sustainable green communication and low-carbon environmental protection advocated by the country,make full use of energy and reduce communication costs,this paper conducts in-depth research on resource allocation of cognitive radio systems using different models or technologies to improve system energy efficiency.(1)Aiming at the problems of low spectrum utilization,high energy consumption and low energy efficiency in cognitive radio system,a spectrum allocation strategy under the graph theory model is proposed to optimize the system energy efficiency through optimal allocation of system spectrum resources.The strategy takes the derived system energy efficiency as the target,and uses the improved Binary Pigeon-Inspired Optimization(IBPIO)algorithm to obtain the spectrum allocation strategy with the highest system energy efficiency.The algorithm adopts the scheme of decreasing dynamic inertia weight and introducing two information factors,and obtains better global search ability.Experimental simulation shows that IBPIO algorithm is able to effectively improve the energy efficiency of the system.(2)To further improve system energy efficiency,Non-Orthogonal Multiple Access technology is introduced into the cognitive radio system,and a power allocation algorithm based on joint user grouping is proposed to improve the energy efficiency of the system by optimally allocating the system power resources.First of all,in order to improve the spectral efficiency and reduce the error probability and delay of demodulation,secondary users access the system in the form of non-orthogonal multiple access,and use a strategy of uniform channel gain difference to group users.Secondly,considering the disadvantages of the traditional artificial fish swarm algorithm,such as power optimization easily falling into the local optimal solution,weak optimization ability and poor population diversity,the constraint operator mechanism and adaptive strategy are introduced into the original algorithm to jointly optimize the power between sub-channels and the power of sub-users in sub-channels,and to seek the optimal transmit power of sub-users to maximize the energy efficiency of the system.The experimental results show that the energy efficiency of the improved artificial fish swarm algorithm is higher than that of Additional power allocation algorithms under the condition of a certain number of secondary users.