Solving Integrated Access Backhaul Frequency Allocation Problem with Graph-Based Deep Reinforcement Learning 


Vol. 51,  No. 2, pp. 275-284, Feb.  2026
10.7840/kics.2026.51.2.275


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  Abstract

We present a novel deep reinforcement learning (DRL) framework for ultra-dense integrated access and backhaul (IAB) frequency allocation, formulated as a combinatorial optimization problem. Our approach leverages graph neural networks (GNNs) to model the network as a graph optimization task, capturing intricate spatial dependencies. To further enhance learning, we integrate a transformer-based attention mechanism that dynamically prioritizes critical factors such as channel states, interference patterns, and resource allocation states. Unlike conventional time-based DRL optimization methods, our framework employs an iterative optimization approach that allocates one resource per step, thereby effectively addressing the curse of dimensionality in DRL.

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[IEEE Style]

R. M. Syahran, W. W. Ro, K. W. Choi, "Solving Integrated Access Backhaul Frequency Allocation Problem with Graph-Based Deep Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 2, pp. 275-284, 2026. DOI: 10.7840/kics.2026.51.2.275.

[ACM Style]

Raihan Muhammad Syahran, Won Woo Ro, and Kae Won Choi. 2026. Solving Integrated Access Backhaul Frequency Allocation Problem with Graph-Based Deep Reinforcement Learning. The Journal of Korean Institute of Communications and Information Sciences, 51, 2, (2026), 275-284. DOI: 10.7840/kics.2026.51.2.275.

[KICS Style]

Raihan Muhammad Syahran, Won Woo Ro, Kae Won Choi, "Solving Integrated Access Backhaul Frequency Allocation Problem with Graph-Based Deep Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 2, pp. 275-284, 2. 2026. (https://doi.org/10.7840/kics.2026.51.2.275)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880