Zi-Tsan Chou

Zi-Tsan Chou

National Sun Yat-Sen University, Taiwan

Title: An opportunistic cooperative MAC protocol for cognitive vehicular networks


Zi-Tsan Chou received the PhD degree in computer science and information engineering from National Taiwan University, Taipei, Taiwan, in 2003. He is currently an associate professor at the Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan. His industrial experience includes NEC Research Institute, America, and the Institute for Information Industry, Taiwan. His research interests include medium access control, power management, and quality-of-service control for wireless networks. He is a member of both the IEEE and the IEEE Communications Society.


In this paper, we can consider an application scenario in cognitive vehicular networks: Under the condition that different vehicles in different locations may have different set of available licensed channels, how do the vehicles (called potential relays) that correctly received data from the gateway choose the proper channel to concurrently relay the data to the vehicles (called potential destinations) that did not receive data from the gateway such that the network throughput can be maximized. We call this problem the interference-free multi-channel poly-matching (IMP) problem. To the best of our knowledge, this paper is the first one to seriously address this issue. The contributions of this paper are three-fold: (i) We use the integer mathematical programming to formally model the IMP problem, which is NP-complete. (2) We design a simple centralized greedy algorithm to efficiently solve this problem. (3) On the basis of our centralized greedy algorithm, we design a novel distributed medium access control protocol, named opportunistic cooperative MAC (OC-MAC for short), such that only via local information exchange between vehicles, the number of potential destinations that finally receive data from potential relays can be maximized. Simulation results show that the throughput of OC-MAC is approximately equal to that of centralized greedy algorithm and is greatly higher than that of Random MAC.

Speaker Presentations