IAB Topology Design: A Graph Embedding and Deep Reinforcement Learning Approach

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.

Automated summary

As cellular networks continue to grow in density, traditional fiber backhaul access to each cell site becomes difficult. Utilizing millimeter wave communication and beamforming, high-speed wireless backhaul can be achieved. Our proposed topology formation approach, based on deep reinforcement learning and graph embedding, offers a less complex and more scalable solution with significant performance gains compared to baseline approaches.