# GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-dense D2D mmWave Networks

arXiv: https://arxiv.org/abs/2107.02412 · HTML: https://mlsanity.com/paper/2107.02412/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2021.

## Built on by (most cited first, 5 of 5)

- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks](https://mlsanity.com/paper/2301.00757/) (2022)
- [Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?](https://mlsanity.com/paper/2307.12480/) (2023)
- [Designing Heterogeneous GNNs with Desired Permutation Properties for Wireless Resource Allocation](https://mlsanity.com/paper/2203.03906/) (2022)
- [GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising](https://mlsanity.com/paper/2511.06663/) (2025)

## Abstract (arXiv)

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of the GBLinks model. Numerical results show that the proposed GBLinks model can converges to a stable point with the number of iterations increases, in terms of the weighted sum rate. Furthermore, the GBLinks model can reach near-optimal solution through comparing with the exhaustive search scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying densities and coverage regions of ultra-dense D2D mmWave communication networks.