Resource Allocation and Computation Offloading for Multi-Access Edge Computing with Fronthaul and Backhaul Constraints
Chen, J., Chang, Z., Guo, X., Li, R., Hämäläinen, T., & Han, Z. (2021). Resource Allocation and Computation Offloading for Multi-Access Edge Computing with Fronthaul and Backhaul Constraints. IEEE Transactions on Vehicular Technology, 70(8), 8037-8049. https://doi.org/10.1109/TVT.2021.3090246
Published inIEEE Transactions on Vehicular Technology
© Authors, 2021
Edge computing is able to provide proximity solutions for the future wireless network to accommodate different types of devices with various computing service demands. Meanwhile, in order to provide ubiquitous connectivities to massive devices over a relatively large area, densely deploying remote radio head (RRH) is considered as a cost-efficient solution. In this work, we consider a vertical and heterogeneous multi-access edge computing system. In the system, the RRHs are deployed for providing wireless access for the users and the edge node with computing capability can process the computation requests from the users. With the objective to minimize the total energy consumption for processing the computation task, a joint radio resource allocation and offloading decision optimization problem is presented under the explicit consideration of capacity constraints of fronthaul and backhaul links. Due to the non-convexity of the formulated problem, we divide the original problem into several sub-problems and address them accordingly to find the optimal solution. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme. ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN Search the Publication Forum0018-9545
Publication in research information system
MetadataShow full item record
Additional information about fundingThis work is partly supported by NSFC (No. 62071105), NSF of Hebei (No. E2017203351) and Key Research and Development Project of Hebei (No. 19252106D), NSF EARS-1839818, CNS1717454, CNS-1731424, and CNS-1702850.
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