A New Paradigm of Radio Resource Management for Future Wireless Communication Networks Integrating Crowd Intelligence

无线通信已经迅速渗透到我们的日常生活中。预计到2020年，移动的数据量将比今天高出1000倍，连接设备将增加10至100倍。为了满足对无线服务的这种不断增长的需求，以更有效的方式管理稀缺的无线电资源以最大化整体网络容量是至关重要的。智能设备（诸如智能电话）的广泛部署的新兴趋势提供了通过提供群体智能（包括设备在通信、计算和感测方面的能力）来促进网络管理来实现该目标的机会。然而，“如何通过适当设计的资源分配算法，充分利用这样的人群智慧？”以及“如何鼓励自私的移动的设备参与网络管理？”在我们能够成功地利用这个机会之前，我们必须回答两个关键问题。 我的研究项目计划通过研究和开发新的无线电资源管理技术，通过人群智能的整合，为未来以设备为中心和以内容为中心的无线网络解决上述问题。与我的HQP一起，我们计划解决两个关键的开放问题，包括基于预测的未来信息开发主动调度策略（人群智能引入的新功能），以及激励机制设计，以鼓励自私的智能设备参与网络管理，同时防止策略行为，如不真实，从他们。研究重点将放在解决实际应用中提出的挑战，包括潜在的预测误差，涉及多个实体的层次资源管理，多维资源分配，资源可用性限制，动态网络环境和计算复杂性。本研究的主要任务包括：1）开发新的无线资源管理算法/协议; 2）数学建模和分析; 3）通过分析和仿真进行性能评估。 拟议的研究将实现无线通信网络技术的突破，对促进加拿大信息和通信技术产业在国际市场上的重要竞争优势具有重要意义，并将为加拿大用户带来重大利益，包括新的无线服务和改善的体验质量。

Wireless communications have been rapidly penetrating our daily life. It is expected that by 2020, the volume of mobile data will be 1000 times higher than today, and between 10 and 100 times more connected devices. To meet this ever-increasing demand on wireless services, it is critical to manage scarce radio resources in a more efficient way in order to maximize the overall network capacity. The emerging trend of wide deployment of smart devices, such as smart phones, provides opportunities for achieving this goal by facilitating network management through offering crowd intelligence, which includes devices' capabilities in communication, computation, and sensing. However, “How to make full use of such crowd intelligence through suitably designed resource allocation algorithms?” and “How to encourage the participation of selfish mobile devices in network management?” are two key questions we have to answer before we can successfully take advantage of this opportunity. My research program plans to address the aforementioned questions through studying and developing novel radio resource management techniques for future device-centric and content-centric wireless networks through the integration of crowd intelligence. Together with my HQP, we plan to address two critical open questions, including the development of strategies for proactive scheduling (a new feature introduced by crowd intelligence) based on predicted future information, and incentive mechanism design to encourage the participation of selfish smart devices in network management while at the same time preventing strategic behaviors, such as untruthfulness, from them. Research emphases will be put on addressing challenges raised from practical applications, including potential prediction errors, hierarchy resource management involving multiple entities, multi-dimensional resource allocation, limitation on resource availability, dynamic network environments, and computational complexity. The key tasks in this research involve: 1) development of novel radio resource management algorithms/protocols; 2) mathematical modeling and analyses; and 3) performance evaluation by analysis and simulation. The proposed research will achieve breakthroughs in wireless communication network technology, is significant to foster a vital competitive edge for Canadian Information and Communication Technology industry in the international market place, and will result in significant benefits to Canadian users, including new wireless services and improved quality of experience.