A Bayesian Approach to the Design of Backhauling Topology for 5G IAB Networks

In this paper, the backhauling topology of an integrated and backhaul (IAB) network is designed to sustain bursty traffic with the highest probability. To ensure sequential DU-MT or MT-DU transmissions, the topology is characterized by a directed acyclic graph (DAG). First, the Bayes' theorem is used to transform the probability of sustaining the UE traffic into the probability of generating a DAG, and then the transformed problem is decomposed into two subproblems: 1) The link traffic load is determined under the condition that a DAG is formed; 2) Based on the link traffic load, the links that are critical for sustaining the UE traffic are identified, and then a new DAG is generated by maximizing the joint probability of links in the new DAG. Given random initial DAG, the two subproblems are iteratively solved until obtaining a final DAG. Theoretical analysis validates that the above iterative procedures converge to a single DAG, and simulations confirm that the convergence can be achieved within tens of iterations. Simulation results also show that the backhauling approach developed in this paper can support 56.41% higher traffic variations and achieve a 29.32% lower average hop-count than the existing topology generation schemes.