Abstract
Wireless networks and communication of the future will have to manage an ever-increasing density of mobile users using a wide range of services and apps, as well as a surge in mobile data traffic. Meanwhile, networks are becoming increasingly dense, heterogeneous, decentralized, and ad hoc, encompassing a wide range of network elements. As a result, a number of service goals, such as high throughput and low latency, must be met, as a result, resource allocation must be established and optimized. Traditional service and resource management methods that need complete and perfect system information are inefficient or inapplicable in wireless network environments due to the inherent dynamic and uncertainty. This chapter covers similar research that employs Deep reinforcement learning (DRL) to address various difficulties in 5G networks after first going through the fundamental notions of DRL. Finally, we look at some of the DRL techniques that have been presented to deal with emerging communications and networking difficulties.
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References
Ahmed QW, Garg S, Rai A, Ramachandran M, Jhanjhi NZ, Masud M, Baz M (2022) AI-Based resource allocation techniques in wireless sensor internet of things networks in energy efficiency with data optimization. Electronics 11(13):2071
Akhtar T, Tselios C, Politis I (2021) Radio resource management: Approaches and implementations from 4G to 5G and beyond. Wirel. Netw. 27:693–734
Du Z, Deng Y, Guo W, Nallanathan A, Wu Q (2020) Green deep reinforcement learning for radio resource management: architecture, algorithm compression, and challenges. IEEE Veh Technol Mag 16:29–39
François-Lavet V et al (2018) An Introduction to deep reinforcement learning. Found Trends Mach Learn 11:219–354
Fujimoto S, Hoof HV, Meger D (2018) Addressing function approximation error in actor-critic methods. ArXiv, abs/1802.09477
Gronauer S, Diepold K (2022) Multi-agent deep reinforcement learning: a survey. Artif Intell Rev 55:895–943
Haarnoja T, Zhou A, Abbeel P, Levine S (2018) Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. ICML
Hessel M, Modayil J, van Hasselt H, Schaul T, Ostrovski G, Dabney W, Horgan D, Piot B, Azar M, Silver D (2018) Rainbow: Combining improvements in deep reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11796
Jagannath J, Polosky N, Jagannath A, Restuccia F, Melodia T (2019) Machine learning for wireless communications in the Internet of Things: A comprehensive survey. Ad Hoc Netw 93:101913
Lazaridis, Aristotelis et al. (2020): Deep reinforcement learning: A state-of-the-art walkthrough. J. Artif. Intell. Res. 1421–1471
Li Y (2019). Deep reinforcement learning. ArXiv, abs/1810.06339
Li M, Li H (2020) Application of deep neural network and deep reinforcement learning in wireless communication. PLoS ONE 15(7):e0235447. https://doi.org/10.1371/journal.pone.0235447
Lillicrap TP, Hunt JJ, Pritzel A, Heess NM, Erez T, Tassa Y, Silver D, Wierstra D (2016) Continuous control with deep reinforcement learning. CoRR, abs/1509.02971
Liu Y, Wang DL (2017) Speaker-dependent multipitch tracking using deep neural networks. J Acoust Soc Am 141(2):710–721. https://doi.org/10.1121/1.4973687. PMID: 28253703
Marc G. Bellemare, Will Dabney, Rémi Munos, (2017). A distributional perspective on reinforcement Learning. In: Proceedings of the 34th International Conference on Machine Learning, PMLR 70:449–458
Mnih V, Badia AP, Mirza M, Graves A, Lillicrap TP, Harley T, Silver D, Kavukcuoglu K (2016) Asynchronous methods for deep reinforcement learning. ICML
Nair A, Srinivasan P, Blackwell S, Alcicek C, Fearon R, Maria AD, Panneershelvam V, Suleyman M, Beattie C, Petersen S, Legg S, Mnih V, Kavukcuoglu K, Silver D (2015) Massively parallel methods for deep reinforcement learning. ArXiv, abs/1507.04296
Nguyen Cong Luong Et Al (2019.), Applications Of deep reinforcement learning in communications and networking: A survey, IEEE communications surveys & tutorials, 21(4), Fourth Quarter https://doi.org/10.1109/COMST.2019.2916583
Packer, Charles; Gao, Katelyn; Kos, Jernej; Krähenbühl, Philipp; Koltun, Vladlen; Song, Dawn (2019) Assessing generalization in deep reinforcement learning 2019. In: 33rd Conference on Neural Information Processing Systems
Priya A, Rai A, Singh RP (2021) Internet of things: architecture, applications and future aspects. In Advances in Smart Communication and Imaging Systems (pp 183–190). Springer, Singapore
Qin M, Yang Q, Cheng N, Zhou H, Rao RR, Shen X (2018) Machine learning aided context-aware self-healing management for ultra-dense networks with QoS provisions. IEEE Trans Veh Technol 67:12339–12351
Qiu J, Wu Q, Ding G et al (2016) A survey of machine learning for big data processing. EURASIP J Adv Signal Process 2016:67. https://doi.org/10.1186/s13634-016-0355-xA
Rai A, Sehgal A, Singal TL, Agrawal R (2020). Spectrum sensing and allocation schemes for cognitive radio. Mach Learn Cogn Comput Mob Commun Wirel Netw 91–129
Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. ArXiv, abs/1707.06347
Sharma D, Singhal S, Rai A, Singh A (2021) Analysis of power consumption in standalone 5G network and enhancement in energy efficiency using a novel routing protocol. Sustain Energy, Grids Netw 26:100427
Tanveer J, Haider A, Ali R, Kim A (2022) Machine learning for physical layer in 5G and beyond wireless networks: a survey. Electronics 11:121. https://doi.org/10.3390/electronics11010121
Van Hasselt H, Guez A, Silver D (2016). Deep reinforcement learning with double Q-Learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10295
Wang S, Liu H, Gomes PH, Krishnamachari B (2018) Deep reinforcement learning for dynamic multichannel access in wireless networks. IEEE Trans Cogn Commun Netw 4(2):257–265
Wang Z, Schaul T, Hessel M, can Hasselt H, Lanctot M, de Freitas N Dueling (2016). Network architectures for deep reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA
Wang S, Chen M, Liu X, Yin C, Cui S, Vincent Poor H (2021). A machine learning approach for task and resource allocation in mobile-edge computing-based networks. IEEE Internet Things J 8(3), 1358–1372. [9146372]. https://doi.org/10.1109/JIOT.2020.3011286
Xiang X, Foo S( 2021). Recent advances in deep reinforcement learning applications for solving partially observable markov decision processes (POMDP) Problems: Part 1—Fundamentals and applications in games, robotics and natural language processing. Mach Learn Knowl Extr, 3, 554–581. https://doi.org/10.3390/make3030029
Xiong Z, Zhang Y, Niyato D, Deng R, Wang P, Wang L-C (2019) Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges. IEEE Veh Technol Mag 14(2):44–52. https://doi.org/10.1109/MVT.2019.2903655
Yang K, Shen C, Liu T, (2020) Deep Reinforcement Learning based Wireless Network Optimization: A Comparative Study. In: IEEE INFOCOM 2020—IEEE Conference on Computer Communications Workshops, pp 1248–1253, doi: https://doi.org/10.1109/infocomwkshps50562.2020.9162925
Zhao X, Yang R, Zhang Y, Yan M, Yue L (2022) Deep reinforcement learning for intelligent Dual-UAV reconnaissance mission planning. Electronics 11:2031. https://doi.org/10.3390/electronics1113203
Zikria YB, Afzal MK, Kim SW, Marin A, Guizani M (2020) Deep learning for intelligent IoT: Opportunities, challenges and solutions. Comput Commun 164:50–53
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Prakash, O., Pattanayak, P., Rai, A., Cengiz, K. (2023). Machine Learning and Deep Reinforcement Learning in Wireless Networks and Communication Applications. In: Rai, A., Kumar Singh, D., Sehgal, A., Cengiz, K. (eds) Paradigms of Smart and Intelligent Communication, 5G and Beyond. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-99-0109-8_5
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