Network Optimization And Resource Management Of Next Generation Wireless Systems

In recent years, new innovations at the physical layer of the communication systems have emerged and are becoming implementable due to the advances of technology (e.g., physical-layer network coding (PNC), full duplex, etc.). These developments have necessitated a thorough redefinition and design of efficient control and scheduling at the upper layers. For example, interference is treated as a destructive phenomenon so that an efficient MAC protocol tries to avoid. However if PNC is applied at the physical layer, useful information could be extracted from the superimposed electromagnetic waves so that ‘‘interference’’ is turned to good use. The ‘‘interference’’ term should be redefined in a proper way. The core challenge in the network optimization is how to use the scarce resources of‘‘space’’, ‘‘time’’, and ‘‘frequency’’ as efficiently as possible. It is important to understand the interactions between layers and a cross-layer design is desirable to maximize the network performance.

We have begun investigating the network throughput performance in the context of CSMA networks with the ability of performing physical-layer network coding. The network throughput could be significantly improved only if the CSMA protocol is properly adjusted. With our prior research experience on cross-layer design, and network utility maximization and decomposition, we are thrilled about the evolution of the wireless networks brought by the advanced physical-layer techniques. The goal is to establish a foundation for the analysis, design, and optimization of wireless communication systems.

Related Publications

  • Shengbo Liu, Liqun Fu, Wei Xie. "Hidden-node Problem in Full-duplex Enabled CSMA Networks", IEEE Transactions on Mobile Computing, 2019.[pdf]

  • Jiangbin Lyu, Yong Huat Chew, and Wai-Choong Wong. "Efficient and Scalable Distributed Autonomous Spatial Aloha Networks via Local Leader Election," IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 9954-9967, Dec. 2016.[pdf]

  • Jiangbin Lyu , Yong Huat Chew, and Wai-Choong Wong. "A Stackelberg Game Model for Overlay D2D Transmission With Heterogeneous Rate Requirements," IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8461-8475, Oct. 2016.[pdf]

  • Shijun Lin and Liqun Fu. "Throughput Capacity of IEEE 802.11 Many-to/from-one Bidirectional Networks with Physical-Layer Network Coding" IEEE Transactions on Wireless Communications, vol. 15, no. 11, pp. 217-231, Jan. 2016.[pdf]

  • Shijun Lin, Liqun Fu, Jianmin Xie,Xiju Wang. "Hybrid network coding for unbalanced slotted ALOHA relay networks", IEEE Transactions on Wireless Communications, 2015, 15(1): 298-313.[pdf]

  • Shijun Lin and Liqun Fu. "Unsaturated Throughput Analysis of Physical-Layer Network Coding Based on IEEE 802.11 Distributed Coordination Function," IEEE Transactions on Wireless Communications, vol. 12, no. 11, pp. 5544-5556, Nov. 2013. [pdf]

Bigdata: Large-Scale Data Networks With Scalable Performance

Large-scale systems continue to grow as one of the most important topics in communication, computing, and many interdisciplinary areas. It is important to develop the themes of ‘‘architecture’’ and ‘‘scalability’’ in large-scale distributed data networks:

  • Network Architecture Design: To tackle the largeness and heterogeneity of data networks, we need to build a unified communication architecture. The architecture decides the allocations of the functionalities, i.e., ‘‘who does what’’ and ‘‘how to connect them’’. The network architecture design is more fundamental, more influential, and less quantitatively understood than any specific network algorithm design.

  • Scalable Communication and Control: To tackle the distributed nature of data networks and many emerging wireless communications systems (e.g., device-to-device communication system), we need to design resource allocation and control schemes that only require loose coordination among communication units, and are able to adapt to the environmental changes with fast and robust convergence.

The aim of this project is to build scalable distributed data networks with throughput efficiency, low energy/power consumption, less message passing, and robust convergence. The tools and techniques from optimization, machine learning, stochastic processes, and control could be potentially useful.