Towards Optimal Power Control via Ensembling Deep Neural Networks

Liang, Fei; Shen, Cong; Yu, Wei; Wu, Feng

doi:10.48550/arxiv.1807.10025

Cited by 17 publications

(30 citation statements)

References 41 publications

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“…Or, to be more on tune with the times, we make Φ(H, x, θ) the output of a neural network; e.g., [11], [18], [19]. In any event, with a given parametrization we can substitute (4) into (3) to obtain a problem in which the optimization over resource allocations p(H, x) is replaced with an optimization over the set of parameter vectors θ,…”

Section: Optimal Resource Allocation In Wirelessmentioning

confidence: 99%

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Eisen

Ribeiro

2020

IEEE Trans. Signal Process.

241

205

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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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Section: Optimal Resource Allocation In Wirelessmentioning

confidence: 99%

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Eisen

Ribeiro

2020

IEEE Trans. Signal Process.

241

205

View full text Add to dashboard Cite

show abstract

“…Inspired by the recent successes of deep learning, researchers have attempted to apply deep learning based methods to solve NP-hard optimization problems in wireless networks [1]- [3], [9]- [11]. As a classic wireless resource allocation problem, power control in the K-user interference channel has attracted most of the attention [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Graph Neural Network Approach for Scalable Wireless Power Control

Shen

Shi

Zhang

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

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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 interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one-layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE.

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“…Those problems involve optimizing a performance metric over a function, resulting in an infinite dimensional problem that is usually hard to solve. That formulation, however, resembles a statistical learning problem [9], which allows one to treat resource allocation in a model-free data-driven fashion [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation in Wireless Control Systems via Deep Policy Gradient

Lima

Eisen

Gatsis

et al. 2020

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limited resources, e.g. transmission power, across plants is critical for maintaining reliable operation. In this paper, we formulate the design of an optimal resource allocation policy that uses current plant states and wireless channel conditions to assign resources used to send control actuation information back to plants. While this problem is challenging due to its infinite dimensionality and need for explicit system model and state knowledge, we propose the use of deep reinforcement learning techniques to find data-driven resource allocation policies. In particular, we use model-free policy gradient methods to directly learn continuous power allocation policies without knowledge of plant dynamics or communication models. Numerical simulations demonstrate the strong performance of learned policies relative to baseline resource allocation methods. I. INTRODUCTION Wireless communication networks are frequently used to exchange data between plants, sensors and actuators in control systems. The use of wireless networks in lieu of wired communication makes the installation of components easier and maintenance more flexible, but also adds particular challenges to the design of control and communication policies [1], [2]. Wireless networks are in general more noisy than their wired counterparts [3], while reliable operation of control systems demands rapid communication and low message error rates-requirements in turn constrained by the limited resources available in that network. It is natural in this setting to look for an optimal way to distribute the resources available in the network among the plants sharing that communication medium. In wireless networks, resource allocation aims to balance power consumption, latency and reliability while taking into account stochastic noise and rapid variability [4], [5]. That involves optimizing a performance metric over a function, resulting in an infinite dimensional problem that is usually hard to solve. The formulation of the resource allocation problem, however, resembles a statistical learning problem [5], which allows one to treat resource allocation in a datadriven fashion [6], [7]. When control plants share a common communication medium, resource allocation policies should also take into account the dynamics of each plant. For a

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Towards Optimal Power Control via Ensembling Deep Neural Networks

Cited by 17 publications

References 41 publications

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

A Graph Neural Network Approach for Scalable Wireless Power Control

Resource Allocation in Wireless Control Systems via Deep Policy Gradient

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