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On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance
Open accessAbstract
We consider a multi-channel opportunistic communication system where the states of these channels evolve as independent and statistically identical Markov chains (the Gilbert- Elliot channel model). A user chooses one channel to sense and access in each slot and collects a reward determined by the state of the chosen channel. The problem is to design a sensing policy for channel selection to maximize the average reward, which can be formulated as a multi-arm restless bandit process. In this paper, we study the structure, optimality, and performance of the myopic sensing policy. We show that the myopic sensing policy has a simple robust structure that reduces channel selection to a round-robin procedure and obviates the need for knowing the channel transition probabilities. The optimality of this simple policy is established for the two-channel case and conjectured for the general case based on numerical results. The performance of the myopic sensing policy is analyzed, which, based on the optimality of myopic sensing, characterizes the maximum throughput of a multi-channel opportunistic communication system and its scaling behavior with respect to the number of channels. These results apply to cognitive radio networks, opportunistic transmission in fading environments, downlink scheduling in centralized networks, and resource-constrained jamming and anti-jamming.
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Cited by
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Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2965856 · Cited by 486
Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks
Liang, L., Ye, H., Yu, G., & Li, G. Y. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2957798 · Cited by 282
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
Zhong, C., Lu, Z., Gursoy, M. C., & Velipasalar, S. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2952909 · Cited by 123
Learning State Selection for Reconfigurable Antennas: A Multi-Armed Bandit Approach
Gulati, N., & Dandekar, K. R. (2014) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2013.2276414 · Cited by 69
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y.-C., & Kim, D. I. (2018) · arXiv · doi:10.48550/arxiv.1810.07862 · Cited by 17
Metadata
- Authors
- Zhao, Q., Krishnamachari, B., & Liu, K.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2008-12
- Volume
- 7
- Issue
- 12
- Pages
- 5431-5440
- ISSN
- 1536-1276 (ISSN-L)
- OpenAlex
- W2171671264
- Cited by
- 360
- References
- 0
- FWCI
- 49.93
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhao, Q., Krishnamachari, B., & Liu, K. (2008). On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/t-wc.2008.071349
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