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Found 28 papers, 2 papers with code
Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task.
Robust and Decentralized Reinforcement Learning for UAV Path Planning in IoT Networks
We address three different practical scenarios, including single UAV path planning, UAV swarm path planning, and single UAV path planning in the presence of an intelligent mobile UAV jammer.
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution
Several differential privacy (DP) mechanisms have been proposed to provide provable privacy guarantees by introducing randomness into the framework, and majority of these mechanisms rely on injecting additive noise.
Anomaly Detection via Learning-Based Sequential Controlled Sensing
Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time with the goal to minimize the delay in making the decision and the total sensing cost.
Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
We evaluate the performance of the proposed policy ensemble algorithm by applying on the network slicing agents and the jammer agent in simulations to show its effectiveness.
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning
MKOR only requires the server to send secretly modified parameters to clients and can efficiently and inconspicuously reconstruct the input images from clients' gradient updates.
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning
In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches.
Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing
Based on the received observations, the decisionmaker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations.
Dynamic Channel Access via Meta-Reinforcement Learning
In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning.
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing
In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes.
LEARNING DISTRIBUTIONS GENERATED BY SINGLE-LAYER RELU NETWORKS IN THE PRESENCE OF ARBITRARY OUTLIERS
Further, we prove that ${O}(\frac{1}{\epsilon p^4}\log\frac{d}{\delta})$ samples are sufficient for our algorithm to estimate the NN parameters within an error of $\epsilon$ with probability $1-\delta$ when the probability of a sample being uncorrupted is $p$ and the problem dimension is $d$.
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control
Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications.
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing
In this setting, we develop an anomaly detection algorithm that chooses the process to be observed at a given time instant, decides when to stop taking observations, and makes a decision regarding the anomalous processes.
Anomaly Detection via Controlled Sensing and Deep Active Inference
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks.
Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task.
How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning
In this paper, reinforcement learning (RL) for network slicing is considered in NextG radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time.
Networking and Internet Architecture
Anomaly Detection and Sampling Cost Control via Hierarchical GANs
In order to improve the detection accuracy and reduce the delay in detection, we introduce a buffer zone in the operation of the proposed GAN-based detector.
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks
Beamforming is one of the key techniques in millimeter wave (mmWave) multi-input multi-output (MIMO) communications.
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention.
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning
Our objective is to design a sequential sensor selection policy that dynamically determines which processes to observe at each time and when to terminate the detection algorithm.
Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction
In this paper, a deep learning-based UAS instantaneous density prediction model is presented.
Deep Actor-Critic Reinforcement Learning for Anomaly Detection
Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks.
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously.
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks
The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning
Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session.
Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissions
In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied.
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access
We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP).
