Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks

Zhipeng Cai, S. Ji, Jing He, A. Bourgeois
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引用次数: 67

Abstract

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. First, we study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects data of a snapshot to the base station in a distributed manner without any time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Finally, extensive simulation results indicate that ADDC can effectively finish a data collection task and significantly reduce data collection delay.
异步认知无线网络的最优分布式数据采集
作为一种很有前途的通信范式,认知无线网络(crn)为辅助用户(su)在不给主用户(pu)造成不可接受的干扰的情况下利用未使用的许可频谱铺平了道路。本文研究了异步crn的分布式数据收集问题,这是以往没有解决的问题。首先,我们研究了SUs的合适的载体感应范围(PCR)。通过使用该PCR, SU可以成功地进行数据传输,而不会干扰pu和其他SU的活动。在此基础上,提出了一种考虑crn公平性的异步分布式数据采集(ADDC)算法。ADDC将快照的数据以分布式方式采集到基站,不需要时间同步。与集中式和同步式算法相比,该算法具有可扩展性和实用性。通过全面的理论分析,我们证明了只要SU具有正的接入概率,就延迟和容量而言,ADDC是顺序最优的。最后,大量的仿真结果表明,ADDC可以有效地完成数据采集任务,并显著降低数据采集延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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