EARS: SpecSense: Bringing Spectrum Sensing to the Masses

点击翻译按钮获取中文摘要

With the explosion of mobile data, there is a growing realization that the radio frequency spectrum must be treated as an important resource that is in limited supply. Policy makers and researchers alike are promoting various forms of spectrum sharing models to improve spectrum utilization. Just like any other resource with mismatched demand and supply, all steps towards better utilization of radio spectrum have also increased the need for large scale spectrum monitoring. This serves two key purposes: (i) it helps identify available spectrum opportunities, making spectrum sharing systems more effective, (ii) it can help us develop deeper understanding of spectrum usage and demand over time and space. Large-scale spectrum monitoring can feed into multitudes of 'spectrum-aware' applications forming an entire ecosystem of spectrum data, analytics and apps. The proposed project develops an end-to-end enabling platform called SpecSense to support this vision. SpecSense (i) crowdsources spectrum monitoring using low-cost, low-power custom-designed hardware, and (ii) provides necessary library and interface support for spectrum-aware apps via a central spectrum server/database platform. This project is expected to foster interest in spectrum data marketplaces facilitated by crowdsourced spectrum sensing. This can engender commercial interests in various aspects of the spectrum data ecosystem. In many fields, e.g., healthcare, education, Internet-of-Things, there is a tremendous need for mobile bandwidth and innovation is stunted due to a lack of bandwidth. Success in this project will drive such innovations. The project will also contribute to various educational activities for students with a range of academic preparations.This project addresses several of the core intellectual challenges in developing SpecSense, viz., (1) Exploration of FPGA-based sensors where sensing algorithms are built into the FPGA, with accompanying tools to automatically implement and optimize these algorithms so that they provide the desired trade-off between power and performance; (2) Novel interpolation techniques to estimate spectrum occupancy in both spatial and temporal domains; (3) Algorithms to support optimized selection of sensors to minimize overall sensing cost; (4) Development of an end-to-end testbed and evaluation over a range of spectrum-aware applications. The project team has a range of expertise in topics relevant to the proposal, such as automated hardware design, digital signal processing, detection and estimation, wireless networking, networking algorithms, and networked systems design.