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2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) by Kaneva, Kameliya (author);Aboutorab, Neda (author);Leu, George (author) is a nonfiction available to read on EtoBox.

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This work considers the problem of fronthaul offloading in a multi-hop fog network where the user devices (denoted as F-UEs) enhanced by learning capabilities, can communicate and collaborate with each other using network coded packets. The paper studies the achievable offloading in relation to different transmission deadlines and network densities comparing it to a single-hop communication. The F-UEs use Q-learning to find autonomously the optimal paths which are defined as the paths requiring the least amount of energy for a single transmission. However, due to the fact that the F-UEs are half-duplex devices and the multi-hop nature of the communication, transmission collisions are inevitable. Thus, the central processing unit is tasked not only with finding the best schedule of transmitting/receiving F-UEs and files in two transmission phases, but also with resolving any possible conflicts between the F-UEs. The central processing unit is able to detect those collisions and resolve them by adjusting the schedule while obeying the time deadlines and thus, maintaining high quality of service (QoS). Results obtained in simulations prove that allowing multi-hop communication achieve

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Author

Kaneva, Kameliya (author);Aboutorab, Neda (author);Leu, George (author)

Publisher

IEEE

Published

2021

Language

EN

Category

nonfiction