Abstract:

In the process of mobile communication, the service efficiency is low if the communication situation cannot be known in advance. Aiming at this question, this paper proposes a method for communication situation estimating based on the Hidden Markov Model(HMM). In general, user behaviors change as the time goes. It utilizing this feature gives a time slots division method and concrete algorithm according to the difference of efficiency, genetic algorithm or traversal algorithm. Then by mining the relationship between time, location and service, it builds HMM. This paper utilizes Viterbi algorithm to predict the communication situation. Simulation results show that the location predicting success rate is 73% and the behavior predicting success rate is 75% when the pattern feature value is 0.8.

Key words: Hidden Markov Model(HMM), communication situation, pattern feature, genetic algorithm, state transfer, behavior characteristic

摘要：

针对移动通信过程中通信态势无法被预知导致的服务效率较低问题，给出一种基于隐马尔可夫模型的区域通信态势估计方法。根据不同时间点的通信行为特征具有差异性的特点，对通信行为按不同的时间段进行划分，并自适应地给出具体的划分算法，即遗传法或遍历法。挖掘终端行为发生时间、地点以及通信行为之间的内在联系，构建隐马尔可夫模型，利用维特比译码算法对区域内终端位置及通信行为进行估计。仿真结果表明，当模式特征值取0.8时，该方法的终端位置预测成功率在73%左右，通信行为预测成功率在75%左右。

关键词: 隐马尔可夫模型, 通信态势, 模式特征, 遗传算法, 状态转移, 行为特征

CLC Number: 

• TN929.5