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Found 17 papers, 11 papers with code
Distributed and Rate-Adaptive Feature Compression
Our goal is to design a feature compression {scheme} that can adapt to the varying communication constraints, while maximizing the inference performance at the fusion center.
Learning Non-myopic Power Allocation in Constrained Scenarios
The problem of optimal power allocation -- for maximizing a given network utility metric -- under instantaneous constraints has recently gained significant popularity.
Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices.
Learnable Digital Twin for Efficient Wireless Network Evaluation
Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration.
Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks
We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks.
Delay-aware Backpressure Routing Using Graph Neural Networks
In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network.
Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks.
Delay-Oriented Distributed Scheduling Using Graph Neural Networks
In wireless multi-hop networks, delay is an important metric for many applications.
Link Scheduling using Graph Neural Networks
Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity.
Adaptive Contention Window Design using Deep Q-learning
We study the problem of adaptive contention window (CW) design for random-access wireless networks.
Distributed Scheduling using Graph Neural Networks
In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.
Efficient power allocation using graph neural networks and deep algorithm unfolding
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
Adversarial Examples in Constrained Domains
To assess how these algorithms perform, we evaluate them in constrained (e. g., network intrusion detection) and unconstrained (e. g., image recognition) domains.
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks
Modern machine learning systems are susceptible to adversarial examples; inputs which clearly preserve the characteristic semantics of a given class, but whose classification is (usually confidently) incorrect.
Attribution-Based Confidence Metric For Deep Neural Networks
These experiments demonstrate the effectiveness of the ABC metric to make DNNs more trustworthy and resilient.
Attribution-driven Causal Analysis for Detection of Adversarial Examples
We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood.
