Throughput Analysis of Multi-channel TD-CSMA System and Reinforcement Learning
Sachin Bhutani1, Deepti Kakkar2, Arun Khosla3
1Sachin Bhutani, ECE Department, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India.
2Deepti Kakkar, ECE Department, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India.
3Arun Khosla, ECE Department, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India.
Manuscript received on April 11, 2012. | Revised Manuscript received on April 14, 2012. | Manuscript published on May 05, 2012. | PP: 513-516 | Volume-2 Issue-2, May 2012 . | Retrieval Number: B0631042212/2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Cognitive Radio, Monte Carlo Method, Reinforcement Learning, TD-CSMA System.