https://scholars.lib.ntu.edu.tw/handle/123456789/580596
Title: | A learning-based expected best offloading strategy in wireless edge networks | Authors: | Wu Y.-C DInh T.Q Fu Y Lin C Quek T.Q.S. CHE LIN |
Keywords: | Benchmarking; Convex optimization; Edge computing; Energy utilization; Mobile telecommunication systems; Reinforcement learning; Resource allocation; Latency; Nonconvex optimization; Offloading; Q-learning; Resource management; Green computing | Issue Date: | 2019 | Source: | 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings | Abstract: | Recently, Mobile-Edge Computing (MEC) has been considered as a powerful supplement to a wireless network by processing computationally intensive tasks for resource-limited mobile devices. However, despite saving computational energy at User Equipment (UE), there is additional transmission energy consumption. As a result, the joint offloading strategy should be carefully selected to save energy and computational time. In this work, we investigated a sum cost minimization problem in a multi-UE multi-computing access point (CAP) system with time-varying channels. Our approach combines the optimization-based resource allocation algorithm with a Q-learning-based strategy selection mechanism. Without the need for communication overhead for CSI and inter- neighborhood cost value exchange, our algorithm shows prominent performance over the benchmark schemes with moderate assumptions. ? 2019 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081980861&doi=10.1109%2fGLOBECOM38437.2019.9013836&partnerID=40&md5=0943967b0fe8a9007f8b6027f817ffad https://scholars.lib.ntu.edu.tw/handle/123456789/580596 |
DOI: | 10.1109/GLOBECOM38437.2019.9013836 | SDG/Keyword: | Benchmarking; Convex optimization; Edge computing; Energy utilization; Mobile telecommunication systems; Reinforcement learning; Resource allocation; Latency; Nonconvex optimization; Offloading; Q-learning; Resource management; Green computing |
Appears in Collections: | 電機工程學系 |
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