Corpus ID: 235732178

Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

  title={Learning Decentralized Wireless Resource Allocations with Graph Neural Networks},
  author={Zhiyang Wang and Mark Eisen and Alejandro Ribeiro},
We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning… Expand
1 Citations
Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks
We consider the problem of resource allocation in large scale wireless networks. When contextualizing wireless network structures as graphs, we can model the limits of very large wireless systems asExpand


Decentralized Wireless Resource Allocation with Graph Neural Networks
Aggregation Graph Neural Networks (Agg-GNNs), which take a sequence of graph aggregated state information obtained locally at each transmitter from multi-hop neighbors as an input, are proposed, which are formed naturally through wireless transmission. Expand
Unsupervised Learning for Asynchronous Resource Allocation In Ad-Hoc Wireless Networks
An unsupervised learning method based on Aggregation Graph Neural Networks that can be learned globally and asynchronously while implemented locally and proposes a permutation invariance property which indicates the transferability of the trained Agg-GNN. Expand
Wireless Power Control via Counterfactual Optimization of Graph Neural Networks
It is shown how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and 5th percentile user rates throughout a range of network configurations. Expand
Learning Optimal Resource Allocations in Wireless Systems
DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parameterization of the resource allocation policy and optimizes the primal and dual variables. Expand
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
The proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents and is especially suitable for practical scenarios where the system model is inaccurate and CSI delay is non-negligible. Expand
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning
Simulation results demonstrate the superiority of the proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5th percentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized baseline. Expand
Spatial Deep Learning for Wireless Scheduling
It is shown that it is possible to bypass the channel estimation stage altogether and to use a deep neural network to produce a near optimal schedule based solely on geographic locations of the transmitters and receivers in the network. Expand
Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks
A reinforcement learning approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. Expand
Optimal resource allocation in wireless communication and networking
The article discusses the problem simplifications that arise by working in the dual domain and reviews algorithms that can determine optimal operating points with relatively lightweight computations. Expand
Deep Reinforcement Learning Based Resource Allocation for V2V Communications
From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure communications. Expand