【摘要】 针对无线电网络中频谱资源有限且利用率较低的问题,提出了基于双向拍卖结合贝叶斯推理模型的频谱共享算法。首先,主用户和次用户自适应地选择拍卖价格分享频段;然后,玩家基于反馈学习过程捕捉调整价格的策略;最后,进行重复拍卖过程直到达成共识。该算法采用了贝叶斯推理技术,能够自适应地响应不断变化的系统环境和玩家数量,具有良好的可扩展性。仿真结果表明,该算法在PU受益、交易成功率、频谱利用率、网络吞吐量等方面显著优于其他几种较新的频谱共享算法。更多还原

【Abstract】 Since the spectrum resource in radio networks is limited and its utilization is low,a spectrum sharing algorithm for double auction combining Bayesian inference model is proposed. Firstly,the primary users and the secondary users adaptively select their auction prices to share the spectrum bands. And then,based on feedback learning process,the players capture their adjustable price strategies. Finally,the auction process is repeated until the consensus is reached. The algorithm adopts Bayesian inference technique,which can adaptively response to the constantly changing system environment and players′ quantity. It has better scalability. The simulation results show that the proposed algorithm is superior to several other advanced spectrum sharing algorithms in the aspects of PU benefit,trade success rate,spectrum efficiency and network throughput.更多还原