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

随着无线服务和应用的广泛普及以及带宽需求的增加，正在迅速造成频谱短缺。 然而，该问题主要是由低效的传统频谱分配和利用策略引起的，使得即使当一些应用遭受带宽不足时，在它们正在使用的频带或其他频带中也存在空闲容量。该项目开发并展示了一种革命性的方法，用于解决频谱稀缺问题并释放隐藏的通信容量，从而增加无线连接的覆盖范围和实用性。在这项研究工作中研究的非传统的通信技术检测传输机会，发生在现任主用户享受信噪比值高于所需的最小值，以保持他们的服务质量。然后，它在保持主要用户当前服务质量的同时，公正地利用这些机会。 该项目开发了新的变化检测方法，融合拟合优度测试和密度估计和相似性评估，使用信息论方法研究网络流量，并设计了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，在异步通信网络上实现。它比较了分布式拟合优度测试和密度估计和相似性评估技术与一类新的分布式累积和（cumsum）变化检测方法的性能，它构造了在其他应用领域的文献中已经提出的推广cumsum方法。最后，它证明了实验的好处，使用这些方法来解锁迄今隐藏的通信容量和量化的本地通信容量的增加，通过使用新的计划。

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.