IAB Topology Design: A Graph Embedding and Deep Reinforcement Learning Approach
Autor: | Oguz H. Elibol, Oner Orhan, Marcel Nassar, Meryem Simsek, Hosein Nikopour |
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Rok vydání: | 2021 |
Předmět: |
Computer science
business.industry Graph embedding Distributed computing ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS 020206 networking & telecommunications 02 engineering and technology Network topology Computer Science Applications Backhaul (telecommunications) Traffic flow (computer networking) Robustness (computer science) Modeling and Simulation Scalability 0202 electrical engineering electronic engineering information engineering Cellular network Wireless Reinforcement learning Electrical and Electronic Engineering business |
Zdroj: | IEEE Communications Letters. 25:489-493 |
ISSN: | 2373-7891 1089-7798 |
Popis: | As the density of cellular networks grows, it becomes exceedingly difficult to provide traditional fiber backhaul access to each cell site. Millimeter wave communication coupled with beamforming can be used to provide high-speed wireless backhaul to such cell sites. Therefore, the 3rd generation partnership project (3GPP) defines an integrated access and backhaul (IAB) architecture, in which the same infrastructure and spectral resources are shared to provide access and backhaul. However, this complicates the design of topologies in such networks as they need to enable efficient traffic flow and minimize congestion or increase robustness to backhaul link failure. We formulate this problem as a graph optimization problem that maximizes the lower bound of the network capacity and propose a topology formation approach based on a combination of deep reinforcement learning and graph embedding. Our proposed approach is significantly less complex, more scalable, and yields very close performance compared to the optimal dynamic programming approach and significant gains when compared with baseline approaches. |
Databáze: | OpenAIRE |
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