The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks
Abstract
Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep Q network to approximate the Q value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.
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Citations
Cites
Fundamentals of Wireless Communication
Tse, D., & Viswanath, P. (2005) · doi:10.1017/cbo9780511807213 · Cited by 13,162
Cache in the air: exploiting content caching and delivery techniques for 5G systems
Wang, X., Chen, M., Taleb, T., Ksentini, A., & Leung, V. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6736753 · Cited by 1,063 · cited 1×
“By effectively reducing the duplicate content transmissions in networks, caching has been recognized as one of the promising techniques for future wireless networks to improve spectral ef-ficiency, shorten latency, and reduce energy consumption [3]– [5].”
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Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924243 · Cited by 678
He, Y., Zhao, N., & Yin, H. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2760281 · Cited by 569
Self-citationSpectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Liang, L., Ye, H., & Li, G. Y. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933962 · Cited by 559
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
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
Metadata
- Authors
- He, Y., Zhang, Z., Yu, F. R., Zhao, N., Yin, H., Leung, V. C. M., & Zhang, Y.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2017-11
- Volume
- 66
- Issue
- 11
- Pages
- 10433-10445
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2756144809
- Cited by
- 288
- References
- 2
- FWCI
- 16.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- He, Y., Zhang, Z., Yu, F. R., Zhao, N., Yin, H., Leung, V. C. M., & Zhang, Y. (2017). Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2017.2751641
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