Research On Joint Optimization Of Transmission Performance And Energy Consumption Management For Backscatter Communication Systems

Compared with traditional internet of things communication devices,backscatter communication devices can transmit information by reflecting signals from other radio frequency sources,which means less energy consumption and longer working time.Therefore,backscatter communication devices have great potential in deployment of internet of things.However,the reliability of information transmission is difficult to be guaranteed due to the small size,simple circuit structure,limited battery capacity and lack of error control technology of existing backscatter communication equipment.In addition,the timeliness of information in the backscatter communication system cannot be effectively characterized by the traditional communication theory evaluation indexes such as rate or delay,and it is difficult to effectively analyze and optimize system’s timeliness of information by the model established based on the existing theories.In view of the above problems,this thesis jointly optimizes the transmission performance and energy consumption management of the backscatter communication systems.The main work and innovation are summarized as follows:Firstly,a retransmission optimization method of backscatter communication system is studied in this thesis.The transmission model of the backscatter communication system based on retransmission is established to reduce the transmission error rate by transmitting the same information multiple times.Then,based on the finite block length data transmission theory,the error rate of the system in one single transmission is analyzed,and a closed upper bound of the error probability of retransmission is derived.Based on the analysis of transmission error rate,a joint optimization problem is proposed to minimize transmission error rate and energy consumption.And an efficient joint optimization algorithm is designed,the corresponding simulation experiments are carried out.The simulation results show that compared with other transmission schemes,the proposed joint optimization algorithm can significantly reduce the total energy consumption while ensuring a high transmission reliability,and finally achieving a balance between transmission reliability and energy consumption.Secondly,this thesis studies a joint optimization method for backscatter communications and wireless power transmission to reduce the timeliness of information.The timeliness of information for multiple backscatter communication devices is analyzed and modeled,the age of information is introduced as the evaluation index of the system’s timeliness of information,while the wireless power collection and energy consumption management in the system are analyzed.The expression of the long-term average age of information for the system is derived,then,based on the analysis,an optimization problem of minimizing the longterm average age of information is proposed.An efficient joint optimization algorithm based on deep reinforcement learning is designed to solve the joint optimization problem,and simulation experiments are carried out.The simulation results show that compared with other algorithms,the proposed joint optimization algorithm can realize the reasonable allocation of time and communication resources in multi-device scenarios,and effectively reduce the long-term average age of information,and finally improving the timeliness of information for the backscatter communication systems.