Exploiting temporal and spatial diversities for spectrum sensing and access in cognitive vehicular networks Academic Article uri icon


  • Copyright 2014 John Wiley & Sons, Ltd. In cognitive vehicular networks (CVNs), spectrum sensing and access are introduced as the promising technologies to fully exploit the underutilized licensed spectrum. Because the sensing ability of a single secondary vehicular user (SVU) is affected by high mobility, dynamic topology, and unreliable wireless environment, collaborative sensing is developed to increase the sensing accuracy and efficiency. Generally, the synchronization is required in the collaborative sensing in CVN. However, it is difficult to keep all SVUs synchronized with others for sensing under the high dynamic network topology, and the sensing overhead of the synchronous cooperative action may be significant. In this paper, we first propose an asynchronous cooperative sensing scheme in which each SVU provides an energy information (EI) that is tagged with location and time information. The sensing decision will be made on account of the EI. Considering the temporal and spatial diversities of each SVU, we assign different weights to each EI and formulate the probabilities of detection and false alarm as the optimization problems to find the optimal weight of each EI. Then, based on the asynchronous sensing, the specifications of the opportunistic spectrum access mechanism are elaborated in both centralized and decentralized CVNs for the sake of practical implementation. We analyze the system performance in terms of achievable throughput and transmission delay. Numerical results show that the proposed scheme is able to achieve substantially higher throughput and lower delay, as compared with existing schemes.

published proceedings


author list (cited authors)

  • Liu, Y. i., Xie, S., Yu, R., Zhang, Y., Zhang, X. i., & Yuen, C.

citation count

  • 11

complete list of authors

  • Liu, Yi||Xie, Shengli||Yu, Rong||Zhang, Yan||Zhang, Xi||Yuen, Chau

publication date

  • December 2015