Model-Free Adaptive Rate Selection in Cognitive Radio Links
In this work we address the rate adaptation problem of a cognitive radio (CR) link in time-variant fading channels. Every time the primary users (PU) liberate the channel, the secondary user (SU) selects a transmission rate (from a finite number of available rates) and begins the transmission of fixed sized packets until a licensed user reclaims the channel back. After each transmission episode the number of successfully transmitted packets is used by the SU to update its optimal rate selection ahead of the next episode. The problem is formulated as an n-armed bandit problem and it is solved by means of a Monte Carlo control algorithm.
KeywordsCognitive radio (CR) rate control n-armed bandit problem reinforcement learning (RL)
Unable to display preview. Download preview PDF.
- 1.Chai, C.C.: On power and rate adaptation for cognitive radios in an interference channel. In: Proc. 71th IEEE Vehicular Technology Conference, PIMRC, Taipei, Taiwan, vol. 2, pp. 1–5 (May 2010)Google Scholar
- 3.Wang, H.H.J., Zhu, J., Li, S.: Optimal policy of cross-layer design for channel access and transmission rate adaptation in cognitive radio networks. EURASIP Journal on Advances in Signal Processing, vol. 2010 (2010)Google Scholar
- 4.Pérez, J., Khodaian, M.: Optimal rate and delay performance in non-cooperative opportunistic spectrum access. In: 9th International Symposium on Wireless Communication Systems (ISWCS 2012), Paris, France (August 2012)Google Scholar
- 5.Jouini, W., Ernst, D., Moy, C., Palicot, J.: Upper confidence bound based decision making strategies and dynamic spectrum access. In: 2010 IEEE International Conference on Communications (ICC), pp. 1–5 (2010)Google Scholar
- 6.Gonzalo-Ayuso, A., Pérez, J.: Dynamic rate adaptation in cognitive radio considering time-dependent channel access models. In: 8th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM (2013)Google Scholar
- 7.Gonzalo-Ayuso, Á., Pérez, J.: Rate adaptation in cognitive radio links with time-varying channels. In: 21st European Signal Processing Conference 2013, EUSIPCO 2013 (2013)Google Scholar
- 8.Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. M.I.T. Press (1998)Google Scholar
- 9.López-Benítez, M.: Spectrum usage models for the analysis, design and simulation of cognitive radio networks. Ph.D. dissertation, Universitat Politècnica de Catalunya (UPC), Barcelona (May 2011)Google Scholar