Energy Efficient Computation Offloading for Energy Harvesting-Enabled Heterogeneous Cellular Networks (Workshop)

  • Mengqi MaoEmail author
  • Rong Chai
  • Qianbin Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 313)


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.


Mobile edge computing Heterogeneous cellular network Computation offloading Resource allocation Hotbooting Q-learning 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  1. 1.School of Communication and Information EngineeringChongqing University of Posts and TelecommunicationsChongqingChina

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