高速铁路场景中基于MAB模型的多信道选择算法

针对高速列车越区切换过程中, 切换带为多信道分布的问题, 提出一种基于多臂老虎机(multi-armed bandit, MAB)模型的信道选择算法. 首先, 以置信区间上界(UCB)算法为基础, 通过设置信道空闲差异因子, 使算法快速收敛于最优信道； 其次, 通过引入满意通信概率(SCP), 衡量移动列车的通信质量, 分析与切换过程中误码率之间的关系； 最后, 利用最优信道选择比率、成功传输率和累积接入损失作为评判标准, 分析算法的性能. 仿真结果表明, 该算法的累积接入损失比原始UCB算法减少了约18.5%； 对比随机选择算法与原始UCB算法, 成功传输率提高了约30.2%和3.3%； 最优选择比率提高了约88.3%和13.5%.

Multi-channel Selection Algorithm Based on MAB Model in High-Speed Railway Scene

Aiming at the problem of multi-channel distribution during the handover of high-speed trains, we proposed a channel selection algorithm based on the multi-armed bandit (MAB) model. Firstly, the model was based on the upper-confidence bound (UCB) algorithm, and the algorithm converged to the optimal channel quickly by setting the channel idle difference factor. Secondly, the satisfactory communication probability (SCP) was introduced to measure the communication quality of the mobile train, and the relationship between the communication quality and the bit error rate during the analysis and handover process was analyzed. Finally, the optimal channel selection rate, successful transmission rate and cumulativ e access loss were used as the evaluation criteria to analyze the performance of the algorithm. The simulation results show that the cumulative access loss of the algorithm is about 18.5% less than the original UCB algorithm, compared with the random selection algorithm and the original UCB algorithm, the successful transmission rate is increased by about 30.2% and 3.3%, and the optimal selection ratio is increased by about 88.3% and 13.5%.