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- W4377822290 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." @default.
- W4377822290 created "2023-05-24" @default.
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- W4377822290 date "2023-01-01" @default.
- W4377822290 modified "2023-09-26" @default.
- W4377822290 title "Machine Learning and Deep Reinforcement Learning in Wireless Networks and Communication Applications" @default.
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- W4377822290 doi "https://doi.org/10.1007/978-981-99-0109-8_5" @default.
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