Research On Handoff Of Fully Automatic Operation System For Urban Rail Transit Based On TD-LTE

With the accelerating process of urbanization,the urban population is also growing rapidly.In order to alleviate the population pressure,major cities at home and abroad have also stepped up the pace of construction and operation for urban rail transit.Urban rail transit has become one of the main modes of travel because of convenient,fast and comfortable.Along with the development of science and technology,the driverless technology of urban rail transit is also becoming more and more perfect,and the FAO system has become the future development direction of urban rail transit.Because TD-LTE has the advantages of high-speed transmission and low latency,the FAO system will also use TD-LTE technology to carry wireless train-ground communication,ensuring that the train can achieve uninterrupted communication during the process of handoff,thereby ensuring the safety of passengers.Therefore,it is of great significance to research on the handoff of FAO system for urban rail transit based on TD-LTE.Firstly,this dissertation introduces the development status of urban rail transit in China and the current status of domestic and foreign research on issues of handoff,and analyzes the impact of environment on urban rail transit.And this dissertation introduced the overall architecture of the FAO system and the basic concepts of TD-LTE,expounded the signaling process of handoff,the architecture of TD-LTE train-ground wireless communication system,etc.,and introduced the common wireless channel model of environment for urban rail transit and A3 event.Secondly,aiming at the problem that the reference signal received power value received by the urban rail train during the handoff fluctuates greatly,which causes frequent occurrence of ping-pong handoff,combined with the A3 event in the TD-LTE standard,the improved grey prediction-BP neural network(IGM-BP)algorithm was proposed to improve RSRP fluctuations.The terminal access unit will receive the first four sets of RSRP values at time t to establish GM(1,1)model and get a set of predicted values.Take the average of the predicted value as the expected value,and finally the predicted values,modified by the BP neural network algorithm,are used to obtain the expected RSRP value.The simulation results show that the proposed algorithm,compared with the traditional handoff algorithm and the grey prediction algorithm,decreases the RSRP value fluctuation range,reduces the ping-pong handoff rate and improves the handoff success rate.Finally,by analyzing the expression of the hysteresis parameter,the constraint relationship between the train speed and the hysteresis parameter is obtained,and a handoff strategy for dynamically adjusting the hysteresis parameter according to different speed grades of the train is proposed.Firstly,a large number of hysteresis parameter sets with better effects of handoff at different speed levels of the train are collected and sent to the BP neural network for training.In order to solve the problem that BP neural network is easy to fall into local minimum,genetic algorithm is used to optimize the initial weight and threshold of BP neural network,and finally the non-linear fitting of train speed and hysteresis parameter by GA-BP algorithm to obtain a fitted curve.Simulation results show that the curve fitting effect is better,the optimal hysteresis parameter can be obtained according to different speeds,the ping-pong handoff rate is reduced,and the communication quality is effectively improved.