Learning Automata Based Spectrum Allocation in Cognitive Networks
Frequent channel-switching will bring many problems such as delay, packet loss and communication cost. To mitigate the influence of these problems, it is necessary to reduce the channel-switching times. After reviewing the prior works about spectrum allocation we propose a LAGSA (Learning Automata based Global Spectrum Allocation) algorithm in this paper. It can give guidance to the next allocation process by using the information obtained from the historical data transmission results. By the simulation we have discussed the relationship between algorithm astringency and spectrum idle probability, learning pace respectively. Comparing with Greedy allocation algorithm, fixed allocation algorithm and random allocation algorithm in terms of average successful transmission ratio and channel-switching times, AIGOSA has obvious advantage for improving the global spectrum utilization ratio.
cognitive network spectrum allocation channel-switching global optimal successful transmission ratio
LIU Lixia HU Gang XU Ming PENG Yuxing
Computer School of National University of Defense Technology, Changsha, China
国际会议
北京
英文
1-6
2010-06-25（万方平台首次上网日期，不代表论文的发表时间）