Research Article
Energy Efficient Computation Offloading for Energy Harvesting-Enabled Heterogeneous Cellular Networks (Workshop)
@INPROCEEDINGS{10.1007/978-3-030-41117-6_32, author={Mengqi Mao and Rong Chai and Qianbin Chen}, title={Energy Efficient Computation Offloading for Energy Harvesting-Enabled Heterogeneous Cellular Networks (Workshop)}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II}, proceedings_a={CHINACOM PART 2}, year={2020}, month={2}, keywords={Mobile edge computing Heterogeneous cellular network Computation offloading Resource allocation Hotbooting Q-learning}, doi={10.1007/978-3-030-41117-6_32} }- Mengqi Mao
Rong Chai
Qianbin Chen
Year: 2020
Energy Efficient Computation Offloading for Energy Harvesting-Enabled Heterogeneous Cellular Networks (Workshop)
CHINACOM PART 2
Springer
DOI: 10.1007/978-3-030-41117-6_32
Abstract
Mobile edge computing (MEC) is regarded as an emerging paradigm of computation that aims at reducing computation latency and improving quality of experience. In this paper, we consider an MEC-enabled heterogeneous cellular network (HCN) consisting of one macro base station (MBS), one small base station (SBS) and a number of users. By defining workload execution cost as the weighted sum of the energy consumption of the MBS and the workload dropping cost, the joint computation offloading and resource allocation problem is formulated as a workload execution cost minimization problem under the constraints of computation offloading, resource allocation and delay tolerant, etc. As the formulated optimization problem is a Markov decision process (MDP)-based offloading problem, we propose a hotbooting Q-learning-based algorithm to obtain the optimal strategy. Numerical results demonstrate the effectiveness of the proposed scheme.
