基于隐马尔可夫预测的非对称信息功率博弈机制

Asymmetric information power game mechanism based on hidden Markov prediction

为了解决无线资源竞争中功率博弈的博弈者获得的环境信息具有非对称性问题,提出了一种基于隐马尔可夫预测的功率博弈机制。该机制通过建立隐马尔可夫预测模型(HMPM)判断博弈的对手是否参与博弈,从而提高博弈的信息准确度;然后利用预测获得的信息通过代价函数计算最佳发射功率。仿真结果表明,与最大后验概率法(MAP)和不预测法(NP)相比,基于隐马尔可夫预测的功率博弈模型能够在满足目标容量的同时,较好地提高非授权用户的功率效率。

To solve the issue that,in wireless resource competition,the environment information which gamers get in power game is asymmetric,a power game mechanism based on hidden Markov prediction was proposed.By establishing a Hidden Markov Prediction Model （HMPM）,the proposed mechanism estimated whether competitors would take part in the game to improve the information accuracy of the game.Then,the predicted information was used to calculate the best transmission power via the cost function.The simulation results show that,compared with MAP （Maximum A Posteriori） method and NP （No Predicting） method,the power game model based on hidden Markov prediction can not only meet the target capacity,but also improve the power efficiency of the unauthorized users.