Research On RFID Multi-Reader Anti-collision Algorithm Based On Sarsa

Radio Frequency Identification(Radio Frequency Identification)is an automated identification technology that uses wireless signals to communicate with communication objects to achieve the purpose of identifying targets.With the advantages of high efficiency,precision,non-contact,low cost,anti-pollution,and repeated use,RFID technology has become the leader in the era in the third wave of scientific and technological revolution,and gradually influences people's daily work and life.In UHF RFID systems,multiple readers are often deployed to work at the same time,and different tags often interfere with each other during the communication process with each reader,causing the readers to fail to recognize the tag information.This is the RFID technology.Multi-reader collision problem.The collision problem leads to low efficiency of the RFID system and serious missing tags,which greatly restricts the development of RFID technology.For the collision problem,it is of great significance to design a stable and efficient multi-reader anti-collision algorithm.In view of the above problems,this paper mainly discusses and studies the anti-collision algorithm in multi-readers on the basis of analyzing the principle of reader collision and anti-collision technology.With the rise of reinforcement learning,it provides new ideas for solving the collision problem under multiple readers.The agent in the RL algorithm can communicate with the environment abstracted by the algorithm model to continuously improve the behavior,and this feature is just suitable for the allocation of channel resources in the RFID system.The main research of this article is as follows:1.Analyze the anti-collision algorithm of multi-readers at home and abroad,and explain the working principle and corresponding advantages and disadvantages of the algorithm.2.Propose a multi-reader anti-collision algorithm based on Sarsa,the core algorithm of reinforcement learning.In this algorithm,the reward value function in the Hiq algorithm is improved,and a certain greedy strategy is adopted to feed back the reader collision information in the environment to System to make the system have the function of self-learning and reduce the frequency conflict between adjacent readers.Simulation experiments show that compared with Hiq algorithm and EHiq algorithm,this algorithm can effectively reduce the frequency collision probability of multiple readers,and accelerate the convergence process of the algorithm.3.In view of the increasing number of readers in complex scenarios,in order to reduce memory consumption in the algorithm,this paper combines the neural network with the Sarsa algorithm and uses the powerful decision-making and storage functions of the neural network to release the reader Increased Q table memory space.Simulation experiments show that when the number of readers increases,compared with the Hiq algorithm and the EHiq algorithm,the algorithm can still maintain the performance advantage while reducing system resource consumption.