The objective of this project is to develop a spectrum-efficient cloud platform, namely CogCloud, to enable Radio-as-a-Service (RaaS) over a cognitive radio substrate, and minimize its energy consumption by designing a two-level closed-loop control framework that leverages cloud-level and BS-level optimization for coarse-grained and fine-grained control respectively over radio resources and wireless users. The proposed research is organized into four cohesive research thrusts:
Thrust 1 System Architecture Design and Implementation: we will develop a software architecture for the proposed platform.
Thrust 2 Cloud-level Optimization: We plan to develop optimization algorithms for coarse-grained control over user association and resource sharing in the cloud level according to Service Level Agreements (SLAs) of BSs and runtime network states.
Thrust 3 BS-level Optimization for Fine-grained Control: We will develop optimized resource allocation strategies for fine-grained control over radio resources (such as spectrum, power, time, etc) at each BS under sensing uncertainty according to a cloud-level user association and resource sharing solution.
Thrust 4 Validation and Performance Evaluation: We plan to validate and evaluate the proposed platform, control framework and algorithms via extensive simulation as well as real experiments and measurements with a testbed that will be built using USRP2 Software Defined Radios (SDRs) and the GNU Radio.
Key Personnel
Jian Tang (PI)
M. Cenk Gursoy (Co-PI)
Chenfei Gao (Graduate Assistant)
Bingqian Lu (Graduate Assistant)
Gozde Ozcan (Graduate Assistant)
Mustafa Ozmen (Graduate Assistant)
Jielong Xu (Graduate Assistant)
Zhiyuan Xu (Graduate Assistant)
Yang Yang (Graduate Assistant)
Chuang Ye (Graduate Assistant)
Chengxiang Yin (Graduate Assistant)
Chen Zhong (Graduate Assistant)