سال انتشار:
2020
عنوان انگلیسی مقاله:
Towards self-adaptive bandwidth allocation for low-latency communications with reinforcement learning
ترجمه فارسی عنوان مقاله:
به سمت تخصیص پهنای باند خود سازگار برای ارتباطات با تأخیر کم با یادگیری تقویتی
منبع:
Sciencedirect - Elsevier - Optical Switching and Networking, 37 (2020) 100567. doi:10.1016/j.osn.2020.100567
نویسنده:
Lihua Ruan ∗, Maluge Pubuduni Imali Dias, Elaine Wong
چکیده انگلیسی:
Emerging applications such as remotely-controlled human-to-machine and tactile-haptic applications in the Internet
evolution demand stringent low-latency transmission. In realising these applications, current communication
networks need to reduce their latency towards a millisecond order. In our previous study, we exploited supervised
learning-based machine learning techniques in analysing and optimising bandwidth allocation decisions
in access networks to achieve low latency. In this paper, we propose a reinforcement learning-based solution
to facilitate adaptive bandwidth allocation in access networks, without needing supervised training and prior
knowledge of the underlying networks. In our proposed scheme, the central office estimates the rewards of different
bandwidth decisions based on the network latency resulting from executing these decisions. The reward
estimates are then used to select decisions that reduce the latency in turn. In particular, we discuss the algorithms
that can be used to estimate the rewards and achieve decision selection in the proposed scheme. With
extensive simulations, we analyse the performance of these algorithms in diverse network scenarios and validate
the effectiveness of the proposed scheme in reducing network latency over existing schemes.
Keywords: Tactile Internet | Low-latency communication | Reinforcement learning | Resource allocation | Optical access networks
قیمت: رایگان
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