NeTS: Medium: AC-MWN: A Novel Architecture for Application-Aware Cognitive Multihop Wireless Networks

鉴于最近无线流量的上升趋势，容量需求的增长速度快于频谱效率和可用性。然而，众所周知，在无线通信中，一些频谱被严重利用不足。这一事实吸引了学术界和工业界对下一代认知无线电通信系统感兴趣的研究人员。尽管在这一研究课题上进行了巨大的努力，但认知无线电的研究与网络应用之间还存在着巨大的差距。该项目采用了一种新的无线网络模式，称为应用感知认知多跳无线网络(AC-MWN)。该体系结构包括一个适用于多跳无线网络的巨型网络层，它结合了传统分层无线网络模型中的媒体接入控制、网络和传输层的主要功能。巨型网络层对频谱利用率和网络应用的需求有认知。此外，新的设计可以有效地利用频谱资源，并有效地调整应用资源，如存储和计算能力。AC-MWN在适应不同网络应用需求的同时，也可以使其容量接近理论上的上限。因此，该体系结构满足了应用感知的认知多跳无线网络的要求。AC-MWN的研究工作将对更好地理解未来认知多跳无线网络的设计产生重大影响，并为许多应用领域提供改进，包括公共安全、灾难救援、环境监测和医疗应用。

Given the recent upward trend in wireless traffic, capacity demand increases faster than spectral efficiency and availability. However, it has been well-known that in wireless communications, some of the spectra are significantly under-utilized. This fact has attracted researchers from academia and industry who are interested in the next generation of cognitive radio communication systems. Despite the tremendous ongoing efforts on this research topic, there is a huge gap between the research on cognitive radios and the network applications. This project exploits a novel wireless networking paradigm called Application-Aware Cognitive Multihop Wireless Networks (AC-MWN). This architecture includes a mega network layer for multihop wireless networks, which combines the major functionalities of medium access control, network and transport layers in the traditional layered model of wireless networks. The mega network layer has cognition to the spectrum availability and to the requirements of the network applications. Additionally, the new design can efficiently utilize the spectrum resources and effectively adjust the application resources, such as storage and computational capabilities. While AC-MWN accommodates the requirements of different network applications, it also can drive the capacity closer to the upper theoretical limit for MWNs. Therefore, this architecture satisfies the requirements of application-aware cognitive multihop wireless networks. The AC-MWN research effort will have a significant impact on the better understanding of the designs for future cognitive multihop wireless networks and provide improvements for many application domains, including public safety, disaster rescues, environmental monitoring, and medical applications.