CIF:Small:Physical Layer Optimization for Cognitive Sensor Networks

认知传感器网络的物理层优化认知无线电概念是无线通信系统的革命性发展。 软件定义的认知无线电能够调整链路和网络资源，以优化通信性能。然而，高速率、健壮的通信通常只是许多可能的网络目标之一。例如，在传感应用中，目标是最大化覆盖范围，以高概率检测重要事件，并以高精度跟踪感兴趣的对象。 这些目标通常与最佳通信目标不一致;改善覆盖范围需要更广泛的分散传感器，使网络连接复杂化。高分辨率传感需要更多的信息位，这反过来又会对网络吞吐量造成压力。用于路由或数据包转发的功率减少了传感器的寿命。 显然，当感知性能是关键因素时，或者可能最有趣的是，当必须同时考虑通信和感知性能时，需要不同的范式。 这项研究工作引入了认知感知作为一个概念的双重认知通信，并调查了传感和通信网络的竞争目标。 认知传感器将响应于其所处的环境自适应地调整其操作参数，以便优化感测性能，或者可能是包括感测和通信功能的双重性能度量。与传感性能相关的参数可能包括传感器位置、速度和航向、天线波束图和极化、发射波形类型和带宽、成像相机变焦、方向、分辨率、指向角等。如何分配这些传感器资源的问题是这项工作的核心。

Physical Layer Optimization for Cognitive Sensor NetworksThe cognitive radio concept has been a revolutionary development in wireless communications systems. Cognitive, software-defined radios are able to adjust link and network resources in order to optimize communications performance. However, high rate, robust communications is often just one of many possible network objectives. For example, in sensing applications, the goal is to maximize coverage, detect important events with high probability, and track objects of interest with high accuracy. These goals are often at odds with those for optimum communications; improved coverage requires more widely dispersed sensors, complicating network connectivity. High resolution sensing requires more bits of information, which in turn place a strain on network throughput. Power devoted to routing or packet forwarding reduces a sensors lifetime. Clearly, a different paradigm is needed when sensing performance is the critical factor, or perhaps most interestingly, when both communications and sensing performance must be considered in tandem. This research effort introduces Cognitive Sensing as a concept dual to that of cognitive communications, and investigates the competing objectives of sensing and communications networks. A cognitive sensor would adaptively adjust its operating parameters in response to the environment it finds itself in so as to optimize sensing performance, or perhaps a dual performance metric that includes both sensing and communications functions. Parameters relevant to sensing performance could include sensor position, speed and heading, antenna beampattern and polarization, transmit waveform type and bandwidth, imaging camera zoom, orientation, resolution, pointing angle, etc. The question of how to allocate such sensor resources is central to this effort.