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Rateless code based opportunistic multicasting over cognitive radio networks

机译:认知无线电网络上基于无速率代码的机会多播

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摘要

Cognitive radio (CR) represents an exciting new paradigm on spectrum utilization and potentially more bandwidth for exploding multimedia traffic. We focus on the layer encoded video multicast problem over CR and contribute 1) an opportunistic multicasting framework, based on rateless forward error correction (FEC) codes, 2) a mechanism for adaptation of data fragment size to improve transmission efficiency, 3) a joint secondary channel (SC) and video data selection algorithm. By adapting fragment size, tracking video group receiving rate, and adapting transmission parameters, we are able to realize the opportunistic multicasting advantage in a challenging CR environment. The proposed overall SC and video data selection algorithm finds the best rateless FEC code length, physical layer modulation and coding schemes (MCS), fragment size and SC combination to increase the effective throughput and heuristically reach maximum system utility. Favorable results comparing to other algorithms showcase the improved performance.
机译:认知无线电(CR)代表了一种令人兴奋的频谱利用新范例,并且潜在的更多带宽用于爆发多媒体流量。我们专注于CR上的层编码视频多播问题,并做出了以下贡献:1)基于无速率前向纠错(FEC)码的机会多播框架,2)适应数据片段大小以提高传输效率的机制,3)联合次要频道(SC)和视频数据选择算法。通过调整片段大小,跟踪视频组接收速率以及调整传输参数,我们能够在充满挑战的CR环境中实现机会性组播优势。提出的总体SC和视频数据选择算法找到了最佳的无速率FEC码长度,物理层调制和编码方案(MCS),片段大小和SC组合,以提高有效吞吐量并启发式地达到最大的系统实用性。与其他算法相比,良好的结果显示了改进的性能。

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