Efficient Resource Allocation in Software Defined Wireless Networks

未来几代的蜂窝网络将显著改变，以便科普日益增加的网络用户数量以及他们对具有高服务质量（包括低延迟要求）的高数据速率服务的请求。此外，蜂窝网络的能耗约占全球总能耗的0.5%。环境和经济学要求提高能源效率并保持或降低能源消耗水平，同时将数据速率提高至少1000倍。在该项目中，我们开发了一种新的无线接入网络架构，它是灵活的，流量和密度感知。该架构很好地反映了大规模MIMO基站、非正交多址接入（NOMA）方面的最新发展，并利用了软件定义无线电和网络的功能。从方法上讲，该项目将相应的无线电资源管理任务制定为可靠的编程问题，并集中或以分布式实现方式解决它们。毫米波、WiFi和蜂窝等多种网络技术的共存允许在相应的资源分配支持的情况下为用户提供多种无缝连接。该项目还考虑了不完美信道和业务状态信息的鲁棒资源分配。利用最新的优化工具，包括分式规划、凸序列逼近、半定松弛等，求解多目标规划问题。所有解决方案的性能，复杂性和收敛性进行了分析。开发的算法将为5G及更高网络中的软件实现奠定基础。

Future generations of cellular networks will change significantly in order to cope with the increasing number of network users as well as their request for high data rate services with high quality of service including low delay requirements. Additionally, the energy consumptions of cellular networks is about 0.5% of the total energy consumption world wide. Environment and economics require to increase the energy efficiency and keep or lower the level of energy consumption while increase the data rate by a factor of at least 1000. In the project, we develop a new radio access network architecture which is flexible and traffic and density aware. The architecture reflects well the recent developments in terms of massive MIMO base stations, non-orthogonal multiple access (NOMA) and exploits the features of software defined radio and networking. Methodologically, the project formulates the corresponding radio resource management tasks as solid programming problems and solves them either centralized or in a distributed implementation. Coexistence of several network technologies as mmWave, WiFi and cellular allows to supply users with multiple seamless connectivity if the corresponding resource allocation supports it. The project also considers robust resource allocation for imperfect channel and traffic state information. The multi-objective programming problems are solved by using the latest optimization tools including fractional programming, convex sequential approximation, semidefinite relaxation. All solutions are analyzed in terms of performance, complexity and convergence. The developed algorithms will build a basis for software implementation in 5G and beyond networks.