Hieu Nguyen Doan
ABSTRACT
This paper investigates the scalability ability of Graph Neural Network (GNN) for solving resource allocation problems in wireless networks. Although GNNs are able to work on diverse network settings, the performance significantly decreases on unseen network settings. To overcome this issue, we propose the combination of GNN and Federated Learning (FL) framework, namely GraphFL. To illustrate the proposed framework, we consider the power control optimization problems of a small-cell wireless system, where each access point at each cell trains a local GNN model to manage the power allocation for its own users. A global GNN model is aggregated from local models following the FL procedure. Thereby, the global GNN is able to work on diverse network settings. Experimental results demonstrate the adaptability of the GNN model on both seen and unseen network settings.
- Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2020. Message passing neural networks. Machine learning meets quantum physics (2020), 199–214.Google Scholar
- M. Li and H. Li. 2020. Application of deep neural network and deep reinforcement learning in wireless communication. Plos one 15, 7 (2020), e0235447.Google Scholar
- B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273–1282.Google Scholar
- G. Serpen and Z. Gao. 2014. Complexity analysis of multilayer perceptron neural network embedded into a wireless sensor network. Procedia Computer Science 36 (2014), 192–197.Google ScholarCross Ref
- Y. Shen, Y. Shi, J. Zhang, and Kh. B Letaief. 2019. A graph neural network approach for scalable wireless power control. In 2019 IEEE Globecom Workshops (GC Wkshps). IEEE, 1–6.Google ScholarCross Ref
- Y. Shen, Y. Shi, J. Zhang, and Kh. B Letaief. 2020. Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis. IEEE Journal on Selected Areas in Communications 39, 1 (2020), 101–115.Google ScholarDigital Library
- Q. Shi, M. Razaviyayn, Z.Q. Luo, and Ch. He. 2011. An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel. IEEE Transactions on Signal Processing 59, 9 (2011), 4331–4340.Google ScholarDigital Library
- Y. Shi, J. Zhang, B. O’Donoghue, and Kh. B Letaief. 2015. Large-scale convex optimization for dense wireless cooperative networks. IEEE Transactions on Signal Processing 63, 18 (2015), 4729–4743.Google ScholarDigital Library
- H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos. 2018. Learning to optimize: Training deep neural networks for interference management. IEEE Transactions on Signal Processing 66, 20 (2018), 5438–5453.Google ScholarCross Ref
- Tr. Van Chien, E. Björnson, and E. G Larsson. 2016. Joint power allocation and user association optimization for massive MIMO systems. IEEE Transactions on Wireless Communications 15, 9 (2016), 6384–6399.Google ScholarDigital Library
- T. Yoon, T. H. Nguyen, X.T. Nguyen, D. Yoo, and B. Jang. 2018. Resource allocation for NOMA-based D2D systems coexisting with cellular networks. IEEE Access 6 (2018), 66293–66304.Google ScholarCross Ref
- Ch. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao. 2021. A survey on federated learning. Knowledge-Based Systems 216 (2021), 106775.Google ScholarCross Ref
- H. Zhang, Ch. Jiang, N. C. Beaulieu, X. Chu, X. Wang, and T. Quek. 2015. Resource allocation for cognitive small cell networks: A cooperative bargaining game theoretic approach. IEEE Transactions on Wireless Communications 14, 6 (2015), 3481–3493.Google ScholarDigital Library
Index Terms
- Graph Neural Network-based Federated Learning for Sum-rate Maximization in Small-cell Wireless Network
Recommendations
Edge Based Graph Neural Network to Recognize Semigraph Representation of English Alphabets
MIKE 2013: Proceedings of the First International Conference on Mining Intelligence and Knowledge Exploration - Volume 8284Graph Neural Network based on edges is introduced in this paper and is used to recognize the English uppercase alphabets treating their corresponding graphs as semigraphs. Graph Neural Network(GNN) is a connectionist model comprising of two feedforward ...
Incidence Rate of Hypertension Forecasting Based on Generalized Regression Neural Network
ISCID '10: Proceedings of the 2010 International Symposium on Computational Intelligence and Design - Volume 01Objective To study the superiority and application prospect of generalized regression neural network (GRNN) which is used in forecasting the incidence rate of hypertension. Methods Use meteorological data, including average temperature, average pressure ...
AdaBoost-based artificial neural network learning
A boosting-based method of learning a feed-forward artificial neural network (ANN) with a single layer of hidden neurons and a single output neuron is presented. Initially, an algorithm called Boostron is described that learns a single-layer perceptron ...
Comments