An Opportunistic Approach to Capacity Enhancement in Wireless Multimedia Networks

Project supported by the Shun Hing Institute of Advanced Engineering (#MMT 25/09)

Investigators

Principal Investigator (PI): Anthony Man-Cho So , Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong.
Co-Investigator (Co-I): Angela Yingjun Zhang, Department of Information Engineering, The Chinese University of Hong Kong.

Project Description

It has long been realized that many communication and networking problems can be formulated as optimization problems, for which deep theories and efficient algorithms are available. As it turns out, most if not all of those formulations either assume that the data defining the optimization problem – such as channel state information, users’ preferences, etc. – are exactly known, or simply ignore the stochastic nature of the data. However, such an approach would often lead to sub-optimal or even infeasible solutions for practical systems. In this project we propose to use stochastic programming techniques to deal with uncertain data in communication and network optimization problems, particularly those that arise in wireless multimedia communication systems. Our goal is to develop a framework for formulating such problems and to design efficient algorithms for them. We have tested our approach in a recent preliminary study on resource allocation in wireless channels, and simulation results indicate that our approach can offer a substantial gain in performance when compared to existing approaches. We believe that our proposed study will lead to better use of distributional information of the data arising from communication and networking applications, thus resulting in better performance of the systems in question.

Project Objectives

Develop algorithmic machineries to handle data uncertainties and to exploit their distributional properties in optimization problems that arise from wireless multimedia communication applications.
Demonstrate the benefit of incorporating distributional information in optimization by comparing the performance of existing approaches – most of which do not exploit such information – to the performance of our proposed algorithms.

Preprints/Publications

Chung Chan, Lizhong Zheng: Mutual Dependence for Secret Key Agreement. To appear in Proceedings of the 44th Annual Conference on Information Sciences and Systems, 2010.
William Wei-Liang Li, Ying Jun (Angela) Zhang, Anthony Man-Cho So, Moe Z. Win: Slow Adaptive OFDMA Systems through Chance Constrained Programming. Accepted for publication in IEEE Transactions on Signal Processing, 2010.
Ying Jun (Angela) Zhang, Anthony Man-Cho So: Optimal Spectrum Sharing in MIMO Cognitive Radio Networks via Semidefinite Programming, Submitted, 2009.

Last Updated: March 10, 2010