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LTE-R switching parameter selection method based on reinforcement learning

A technology of switching parameters and reinforcement learning, which is applied in the field of cognitive radio and intelligent transportation to achieve the effect of easy selection and improved switching performance

Active Publication Date: 2020-04-03
SUZHOU UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0008] The present invention aims at the deficiencies in the handover parameter selection method in the existing LTE-R system, and provides an LTE-R handover parameter that can effectively improve the handover success rate and average throughput of the LTE-R system, and achieve the goal of optimizing handover performance. Method of choosing

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  • LTE-R switching parameter selection method based on reinforcement learning
  • LTE-R switching parameter selection method based on reinforcement learning
  • LTE-R switching parameter selection method based on reinforcement learning

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Embodiment 1

[0035] See attached figure 1 , which is a schematic diagram of a user switching through adjacent base stations in an LTE-R network. When the user performs handover between the two base stations A and B, the vertical line in the middle is the handover boundary line for disconnecting the A base station and then connecting to the B base station when the train is handing over. In the LTE-R system, its bandwidth is determined by the number of resource blocks.

[0036] See attached figure 2 , which is a block flow diagram of a reinforcement learning-based LTE-R handover parameter selection method provided by the present invention; in this embodiment, it specifically includes the following steps:

[0037] 1. Establish the topology of the base station.

[0038] According to the LTE-R structure, the base station topology is established based on the power of the base station, the distance between adjacent base stations, the bandwidth of the uplink and downlink, the frequency band nu...

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Abstract

The invention discloses an LTE-R switching parameter selection method based on reinforcement learning. When a train performs switching between two adjacent base stations, the method constructs a corresponding state set S and an action set A according to switching algorithms under different events, acquires the switching success rates under different historical speeds and different parameters as empirical values, learns the empirical values by using a reinforcement learning method, and selects a reasonable switching algorithm and switching parameters, so that the optimal switching performance is achieved, and the switching success rate and the average throughput of the LTE-R system are improved. According to the method, the defect that the switching parameter accuracy is affected due to thefact that the switching parameter is kept unchanged once set in an existing switching mechanism is overcome, the switching parameter can be dynamically updated when the train speed is changed, the method has self-adaptability, and it is guaranteed that the LTE-R system has the optimal switching performance.

Description

technical field [0001] The present invention relates to the technical field of cognitive radio and intelligent transportation, in particular to a handover mechanism, a handover algorithm and machine learning in a handover algorithm based on an LTE-R system. Background technique [0002] At present, the technical standard of GSM-R (Global System for Mobile Communications - Railway) railway wireless communication system has achieved mature application in my country's railway wireless communication. However, with the improvement of train wireless communication requirements, higher requirements are also put forward for railway wireless communication technology. The LTE-R railway communication technical standard based on the LTE technical standard is the first choice for the next generation of wireless railway communication. LTE-R communication technology has the advantages of higher service capability, more mature technology, and stronger security performance. [0003] At pres...

Claims

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Application Information

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IPC IPC(8): H04W36/00H04W36/08H04W36/30H04W36/32H04W4/42
CPCH04W36/08H04W36/30H04W36/32H04W4/42H04W36/00837H04W36/0085
Inventor 吴澄盛洁汪一鸣蔡兴强
Owner SUZHOU UNIV
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