ProTOMAC: Proactive Transmit Opportunity Detection at the MAC Layer for Cognitive Radio Networks

随着带宽需求的增加，无线服务和应用的广泛普及正在迅速造成频谱短缺。然而，这个问题主要是由低效的传统频谱分配和利用策略造成的，因此即使一些应用程序遇到带宽不足的问题，它们正在使用的频段或其他频段中也有空闲容量。该项目开发和演示了一种革命性的方法，用于解决频谱稀缺问题和释放隐藏的通信能力，从而增加无线连接的覆盖范围和用途。这项研究工作中研究的非传统通信技术检测当现任主用户享受高于维持其服务质量所需的最小值的信噪比值时发生的传输机会。然后，它明智地利用这些机会，同时保持主要用户的当前服务质量。该项目开发了新的变化检测方法，利用信息论方法融合了拟合优度测试和密度估计以及相似性评估来研究网络流量，并设计了在异步通信网络上实现的创新的分布式拟合优度测试和密度估计和相似性评估技术。将分布拟合优度检验、密度估计和相似性估计技术与一类新的分布累积和(Cumsum)变化检测方法的性能进行了比较，该方法是文献中提出的在其他应用领域推广的Cumsum方法。最后，通过实验证明了使用这些方法来解锁迄今隐藏的通信容量并量化使用新方案所产生的本地通信容量增加的好处。

The wide proliferation of wireless services and applications with increasing bandwidth needs is rapidly creating a spectrum shortage. However, the problem is caused primarily by inefficient legacy spectrum allocation and utilization policies, so that even when some applications suffer from lack of bandwidth, there is idle capacity in the band they are using or other bands. This project develops and demonstrates a revolutionary approach for addressing spectrum scarcity and unlocking hidden communication capacity thereby increasing the reach and utility of wireless connectivity. The non-traditional communication technique studied in this research effort detects transmission opportunities that occur when incumbent primary users enjoy signal to noise ratio values that are higher than the minimum value required to maintain their quality of service. It then judicially exploits these opportunities while preserving the current quality of service of the primary users. The project develops novel change detection methods that fuse goodness of fit tests and density estimation and similarity assessment using information theoretic methods to study network traffic and designs innovative distributed goodness of fit tests and density estimation and similarity assessment techniques implemented over an asynchronous communications network. It compares the performance of the distributed goodness of fit tests and density estimation and similarity assessment techniques with those of a new class of distributed cumulative sum (cumsum) change detection methods that it constructs generalizing cumsum methods that have been proposed in the literature in other application domains. Finally, it demonstrates experimentally the benefit of using these approaches to unlock hitherto hidden communication capacity and quantify the increase in local communication capacity created by using the new schemes.