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Found 21 papers, 2 papers with code
A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks
We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as input the set of dual variables corresponding to the constraints.
State-Augmented Learnable Algorithms for Resource Management in Wireless Networks
We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network.
Learning Resilient Radio Resource Management Policies with Graph Neural Networks
We consider the problems of user selection and power control in wireless interference networks, comprising multiple access points (APs) communicating with a group of user equipment devices (UEs) over a shared wireless medium.
Communication-Control Co-design in Wireless Edge Industrial Systems
We consider the problem of controlling a series of industrial systems, such as industrial robotics, in a factory environment over a shared wireless channel leveraging edge computing capabilities.
Large-Scale Graph Reinforcement Learning in Wireless Control Systems
As the number of learnable parameters in a neural network grows with the size of the input signal, deep reinforcement learning may fail to scale, limiting the immediate generalization of such scheduling and resource allocation policies to large-scale systems.
Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks
We consider the problem of resource allocation in large scale wireless networks.
Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
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.
Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks
We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based resource allocation method.
Model-Free Design of Control Systems over Wireless Fading Channels
Wireless control systems replace traditional wired communication with wireless networks to exchange information between actuators, plants and sensors in a control system.
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications
This paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications.
Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks.
Wireless Power Control via Counterfactual Optimization of Graph Neural Networks
We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium.
Model-Free Learning of Optimal Ergodic Policies in Wireless Systems
Upon further assuming the use of near-universal policy parameterizations, we also develop explicit bounds on the gap between optimal values of initial, infinite dimensional resource allocation problems, and dual values of their parameterized smoothed surrogates.
Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks
We consider the problem of optimally allocating resources across a set of transmitters and receivers in a wireless network.
Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks.
Learning Optimal Resource Allocations in Wireless Systems
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.
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
In this paper, we propose a novel adaptive sample size second-order method, which reduces the cost of computing the Hessian by solving a sequence of ERM problems corresponding to a subset of samples and lowers the cost of computing the Hessian inverse using a truncated eigenvalue decomposition.
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate
This makes their computational cost per iteration independent of the number of objective functions $n$.
Stylometric Analysis of Early Modern Period English Plays
We first study the similarity of writing styles between Early English playwrights by comparing the profile WANs.
A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization
The resulting dual D-BFGS method is a fully decentralized algorithm in which nodes approximate curvature information of themselves and their neighbors through the satisfaction of a secant condition.
Authorship Attribution through Function Word Adjacency Networks
Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone.
