文章基本信息

标题：Throughput Analysis of Multi-channel TD-CSMA System and Reinforcement Learning
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作者：Sachin Bhutani ; Deepti Kakkar ; Arun Khosla 等
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：2
期号：2
页码：513-516
出版社：International Journal of Soft Computing & Engineering
摘要：This study generates a cognitive radio scenario based on non-persistent carrier sense multiple access (CSMA) and time division multiple access (TDMA) systems sharing a multi-channel wireless network. TDMA users are considered as primary users who can access the channel at any time, and non-persistent CSMA users are considered as secondary users who can share the channel when it is free. Then system performance is evaluated for a variety of proportions of non-persistent CSMA and TDMA traffic levels. Simulation results are presented and effect on throughput for different traffic ratio is shown. Further effect of reinforcement learning on system model is shown how throughput increases.
关键词：Cognitive Radio; Monte Carlo Method;Reinforcement Learning; TD-CSMA System