认知无线网络(cognitive radio network,CRN)中,为降低认知用户对授权用户干扰,需尽可能的减少频谱切换次数。提出了一种基于预测信道空时间(prediction of the channel idle time,PCIT)的认知无线动态频谱切换方法。该方法基于已知状态序列的隐马尔可夫模型(known-state sequence hidden Markov model,KSS-HMM),利用信道状态的历史信息预测信道未来空闲时间期望及传输数据包的数量,并给出了备选信道的选择方法,通过比较每个备选信道的传输数据量来选择最佳信道进行数据传输。仿真结果表明,与随机信道选择和传统选择方法相比,该方法能明显减少信道切换次数,同时提高了认知用户的吞吐量。
This paper presents a Dynamic Cross-layer Data Queue Management approach (DC-DQM) based on priority to address the priority deviation problem in Delay-Tolerant Mobile Sensor Networks (DT-MSNs). Receiver-driven data delivery scheme is used for fast response to data transfers, and a priority based interaction model is adopted to identify the data priority. Three interactive parameters are introduced to prioritize and dynamically manage data queue. The experimental results show that it can ameliorate data delivery ratio and achieve good performance in terms of average delay.
