CAREER: Addressing Fundamental Challenges for Wireless Coverage Service in the TV White Space

该项目解决了白覆盖网络中的基本问题，该网络利用TV白色空间频谱在人口密集地区提供数据覆盖服务。它的动机是这样一个事实，即一个白色覆盖小区可能会托管数百个用户，同时支持高度动态的数据流量，这是一个超出现有网络协议能力的场景。此外，由于在人口稠密地区缺乏免费电视频道，白色封面设备可能不得不与异构设备共存于同一频道中。这些挑战通过新型物理层技术和高级机器学习技术的组合来解决，并通过进一步优化来增强解决方案。本项目研究：1）用于节点状态检测的有效物理层方案，利用该方案可以非常有效地调度小区中的传输，2）利用强化学习技术适应异质干扰条件的高级协议，3）用于功耗和集成部分分组恢复的优化协议，以及4）网络范围的性能分析和规划。本项目设计的白盖网络为电视白色空间提供了一种较好的替代技术。用户可以以比蜂窝电话网络低得多的成本享受数据覆盖，并且可以实现新的业务。该项目的预期结果包括针对White-Cover的独特功能进行优化并通过实验进行测试的协议和算法。研究结果将在会议和期刊上传播;源代码将在网上免费提供。

This project addresses fundamental issues in the White-Cover network which provides data coverage services in the densely populated areas with the TV White Space spectrum. It is motivated by the fact that a White-Cover cell will likely host hundreds of users while supporting highly dynamic data traffic, a scenario beyond the capabilities of existing network protocols. In addition, the White-Cover devices will likely have to coexist in the same channel with heterogeneous devices due to the lack of free TV channels in the densely populated areas. These challenges are addressed by a combination of novel physical layer techniques and advanced machine learning techniques, and the solutions are enhanced by further optimizations. This project studies: 1) efficient physical layer schemes for node state detection with which the transmissions in the cell can be scheduled very efficiently, 2) advanced protocols that adapt to the heterogamous interferences conditions with the reinforcement learning technique, 3) optimized protocol for power consumption and integrated partial packet recovery, and 4) network-wide performance analysis and planning. The White-Cover network designed in this project provides a better alternative to the current technologies for the TV White Space. Users may enjoy data coverage at much less cost than from the cellular phone networks and new business may be enabled. The expected results of this project include the protocols and algorithms optimized for the unique features of White-Cover and tested with experiments. The results will be disseminated in conferences and journals; the source code will be freely available online.