Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks
Authors: Mark Eisen, Alejandro Ribeiro
Abstract
We consider the problem of optimally allocating resources across a set of transmitters and receivers in a wireless network. The resulting optimization problem takes the form of constrained statistical learning, in which solutions can be found in a model-free manner by parameterizing the resource allocation policy. Convolutional neural networks architectures are an attractive option for parameterization, as their dimensionality is small and does not scale with network size. We introduce the random edge graph neural network (REGNN), which performs convolutions over random graphs formed by the fading interference patterns in the wireless network. The REGNN-based allocation policies are shown to retain an important permutation equivariance property that makes them amenable to transference to different networks. We further present an unsupervised model-free primal-dual learning algorithm to train the weights of the REGNN. Through numerical simulations, we demonstrate the strong performance REGNNs obtain relative to heuristic benchmarks and their transference capabilities.
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Closest papers
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Large Scale Wireless Power Allocation with Graph Neural NetworksMark Eisen, Alejandro Ribeiro · 2019 · International Workshop on Signal Processing Advances in Wireless Communications · cited 24 times · Not summarized yet
Abstract
We consider the optimal power allocation with a set of transmitter/receiver pairs in a large scale wireless network. Obtaining an optimal policy requires the solving of a non-convex, infinite dimensional optimization problem, motivating the use of a learning model parameterization of the optimal policy. Both for its theoretical properties and experimental performance, deep neural networks are a strong choice to parameterize the power allocation policy. However, fully connected neural networks are impractical to both train and implement for large scale networks, as their size grows quickly with network size. We propose using a convolutional neural network architecture, specifically one that e…
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Learning Optimal Resource Allocations in Wireless SystemsMark Eisen, Clark Zhang, Luiz F. O. Chamon et al. · 2018 · IEEE Transactions on Signal Processing · cited 245 times · Not summarized yet
Abstract
This paper considers the design of optimal resource allocation policies in wireless communication systems, which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constraint, motivating the development of learning methodologies to attempt their solution. To handle stochastic constraints, training is undertaken in the dual domain. It is shown that this can be done with small loss of optimality when using near-universal learning parameterizations. In particular, since deep neural networks (DNNs) are near universal, their use is advocated and ex…
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A Graph Neural Network Approach for Scalable Wireless Power ControlYifei Shen, Yuan-Ming Shi, Jun Zhang et al. · 2019 · 2019 IEEE Globecom Workshops (GC Wkshps) · cited 93 times · Not summarized yet
Abstract
Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in K-user interference channels. Specifically, a K-user …
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Reinforcement Learning-Based Resource Allocation and Request Routing Optimization Algorithm for Edge AIGC ServicesQian-Yu Cao, Chang Xu, Li Zhang et al. · 2026 · Symmetry · Not summarized yet
Abstract
With the explosive growth of artificial intelligence generated content (AIGC) applications, deploying lightweight tiny neural networks (TNNs) at the edge to mitigate the high latency and cost of large cloud models has become an important trend in mobile computing. However, resource heterogeneity in edge environments, the spatiotemporal randomness of user requests, and the complex association problem arising from overlapping base-station coverage challenge traditional static resource allocation methods in meeting multi-dimensional quality of service (QoS) requirements. Existing research often lacks a unified trade-off among model accuracy, service cost, and response latency. To address these …
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Graph Neural Network-Driven Deep Reinforcement Learning for Scalable RIS AllocationMartina Zan, Stefan Schwarz · 2026 · Not summarized yet
Abstract
Reconfigurable Intelligent Surfaces (RISs) offer a promising paradigm to mitigate blockage and extend millimeter-wave coverage in 6G multi-cell networks. However, dynamically allocating shared RIS infrastructure across competing base stations is an NP-hard problem posing severe scalability bottlenecks. In this paper, we propose a scalable framework combining Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) for dynamic shared RIS orchestration. By formulating allocation as a Markov Decision Process, we introduce a physical topology sparsification strategy that prunes dense channel matrices into a sparse tripartite graph. This pruning reduces edge density by 77% and removes …
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QoS-Aware Resource Allocation in LEO Satellite Networks: A Graph Neural Network ApproachWen-Bo Yu, Cheng Wang, Gao-Feng Cui et al. · 2026 · International Symposium on Networks, Computers and Communications · Not summarized yet
Abstract
Efficient radio resource allocation is pivotal for maximizing the service capability of Low Earth Orbit (LEO) satellite networks. However, the high mobility of satellites and the rapidly time-varying channel conditions pose significant challenges to traditional resource management schemes. Conventional optimization methods suffer from prohibitive computational complexity, while heuristic approaches typically prioritize users with superior channel gains to maximize the sum-rate. Consequently, this strategy severely degrades the service reliability and fairness for disadvantaged users. To address these challenges, this paper proposes a QoS-Aware Primal-Dual graph neural network (QPD-GNN) frame…
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Learning to optimize: Training deep neural networks for wireless resource managementHaoran Sun, Xiangyi Chen, Qing-Jiang Shi et al. · 2017 · International Workshop on Signal Processing Advances in Wireless Communications · cited 373 times · Not summarized yet
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Optimal resource allocation in wireless communication and networkingAlejandro Ribeiro · 2012 · EURASIP Journal on Wireless Communications and Networking · cited 95 times · Not summarized yet
Abstract
Optimal design of wireless systems in the presence of fading involves the instantaneous allocation of resources such as power and frequency with the ultimate goal of maximizing long term system properties such as ergodic capacities and average power consumptions. This yields a distinctive problem structure where long term average variables are determined by the expectation of a not necessarily concave functional of the resource allocation functions. Despite their lack of concavity it can be proven that these problems have null duality gap under mild conditions permitting their solution in the dual domain. This affords a significant reduction in complexity due to the simpler structure of the …
Prior work it builds on
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Energy-Efficient Subchannel and Power Allocation for HetNets Based on Convolutional Neural NetworkDi Xu, Xiao-Jing Chen, Chang-Hao Wu et al. · 2019 · IEEE Vehicular Technology Conference · cited 30 times · Not summarized yet
Abstract
Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while ne…
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Graph Embedding-Based Wireless Link Scheduling With Few Training SamplesMengyuan Lee, Guan-Ding Yu, G. Li · 2019 · IEEE Transactions on Wireless Communications · cited 198 times · Not summarized yet
Abstract
Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this article, we propose a novel graph embedding based method for link sch…
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Spatial Deep Learning for Wireless SchedulingWei Cui, Kaiming Shen, Wei Yu · 2018 · Global Communications Conference · cited 241 times · Not summarized yet
Abstract
The optimal scheduling of multiple interfering links in a densely deployed wireless network with full frequency reuse is a well-known challenging problem. The classical optimization approaches to this problem typically operate under the paradigm of first estimating all the interfering channel strengths then finding an optimum solution using the model. However, traditional scheduling methods are computationally and resource intensive, because channel estimation is expensive especially in dense networks, and further the optimization of link scheduling is typically a nonconvex problem. This paper takes a novel deep spatial learning approach to the scheduling problem. We show that it is possible…
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Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning ApproachesFan-Xu Meng, Peng Chen, Le-Nan Wu et al. · 2019 · IEEE Transactions on Wireless Communications · cited 275 times · Not summarized yet
Abstract
The model-based power allocation has been investigated for decades, but this approach requires mathematical models to be analytically tractable and it has high computational complexity. Recently, the data-driven model-free approaches have been rapidly developed to achieve near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as one such approach having great potential for future intelligent networks. In this paper, a dynamic downlink power control problem is considered for maximizing the sum-rate in a multi-user wireless cellular network. Using cross-cell coordinations, the proposed multi-agent DRL framework includes off-line and…
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Stability Properties of Graph Neural NetworksFernando Gama, Joan Bruna, Alejandro Ribeiro · 2019 · IEEE Transactions on Signal Processing · cited 282 times · Not summarized yet
Abstract
Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of graph signals, exhibiting success in recommender systems, power outage prediction, and motion planning, among others. GNNs consist of a cascade of layers, each of which applies a graph convolution, followed by a pointwise nonlinearity. In this work, we study the impact that changes in the underlying topology have on the output of the GNN. First, we show that GNNs are permutation equivariant, which implies that they effectively exploit internal symmetries of the underlying topology. Then, we prove that graph convolutions with integral Lipschitz filters, in combination with the frequency mixing effect of t…
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Updated on 5 November 2026.