Issue No.08 - Aug. (2012 vol.11)
Eric Jung , Department of Electrical and Computer Engineering, University of California, Kemper Hall, One Shields Avenue, Davis, CA 95617
Dan Xu , Department of Computer Science, University of California, Kemper Hall, One shields Avenue, Davis, CA 95617.
Xin Liu , Department of Computer Science, University of California, Kemper Hall, One shields Avenue, Davis, CA 95617.
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2011.168
Cognitive radio (CR) improves spectrum efficiency by allowing secondary users (SUs) to dynamically exploit the idle spectrum owned by primary users (PUs). This paper studies optimal bandwidth allocation of SUs for throughput efficiency. Consider the following tradeoff: an SU increases its instantaneous throughput by accessing more spectrum, but channel access/switching overhead, contention among multiple SUs, and dynamic PU activity create higher liability for larger bandwidths. So how much is too much? In this paper, we study the optimal bandwidth allocation for multiple SUs. Our approach is twofold. We first study the optimal bandwidth an SU should use to maximize the per-SU throughput in the long term. The optimal bandwidth is derived in the context of dynamic PU activity, where we consider both independent and correlated PU channel scenarios while accounting for the effects of channel switching overhead. We further consider the case of suboptimal spectrum use by SUs in the short term due to PU activity dynamics. We propose an efficient channel reconfiguration (CREC) scheme to improve SUs' performance. We use real PU channel activity traces in the simulations to validate our results. The work sheds light on the design of spectrum sharing protocols in cognitive radio networks.
Bandwidth, Throughput, Cognitive radio, Channel allocation, Radio spectrum management, Channel allocation, channel correlation., Cognitive radio, opportunistic spectrum access, bandwidth allocation
Eric Jung, Dan Xu, Xin Liu, "Efficient and Fair Bandwidth Allocation in Multichannel Cognitive Radio Networks", IEEE Transactions on Mobile Computing, vol.11, no. 8, pp. 1372-1385, Aug. 2012, doi:10.1109/TMC.2011.168