认知无线电网络中碎片频谱的高效广播

Pei Huang, Chin-Jung Liu, Xi Yang, Li Xiao
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引用次数: 8

摘要

为了提高频谱利用率,引入认知无线电(CR)技术对可用频谱资源进行自主检测和开发。频谱的灵活使用给广播带来了特殊的挑战,因为不同的CR设备可能在不同的位置检测到不同的可用频谱片段。发送方和接收方必须就用于广播的频谱片段达成一致。可能不存在可供所有接收器使用的公共频谱片段。大多数现有的工作都假设一个设备只能在一个频道中工作,因此发送者必须在不同的频道中多次广播才能到达所有的接收者。将广播问题作为信道会合和最小延迟调度问题进行研究。最近的频谱敏捷设计使设备能够利用部分占用的频谱。因此,我们将宽带信道视为可以独立评估的多个窄信道的聚合。本文提出了一种频谱分片敏捷广播(SFAB)方案,以支持在分片频谱上的高效广播。它旨在有效地实现发射器和接收器之间的频谱协议,并在不考虑接收器的频谱可用性差异的情况下最大化用于广播的信道宽度。我们通过在GNU Radio / USRP平台上的实现验证了SFAB的有效性,并使用ns-2模拟来评估大型部署中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient broadcast on fragmented spectrum in cognitive radio networks
To improve spectrum utilization, cognitive radio (CR) is introduced to detect and exploit available spectrum resources autonomously. The flexible spectrum use imposes special challenges on broadcast because different CR devices may detect different available spectrum fragments at different locations. The sender and the receivers have to agree on spectrum fragments that will be used for broadcast. There may not exist a common spectrum fragment that is available to all receivers. Most existing work assumes that a device works only in a single channel and thus the sender has to broadcast multiple times in different channels to reach all receivers. The broadcast problem is studied as a channel rendezvous and minimum latency scheduling problem. Recent spectrum-agile designs have enabled a device to utilize partially occupied spectrum. We thus view a wideband channel as an aggregation of multiple narrow channels that can be evaluated independently. A Spectrum Fragment Agile Broadcast (SFAB) scheme is introduced in this paper to support efficient broadcast on fragmented spectrum. It aims at achieving spectrum agreement between the transmitter and the receivers efficiently and maximizing the channel width used for broadcast regardless of the spectrum availability differences at receivers. We validate the effectiveness of SFAB through implementation on the GNU Radio / USRP platform and use ns-2 simulations to evaluate the performance in large deployments.
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