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Found 33 papers, 4 papers with code
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs
In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput while minimizing collisions of secondary users with the primary network.
Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP.
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
A recent line of research has been investigating deep learning approaches to wireless positioning (WP).
Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction
To alleviate these challenges, this paper introduces a novel simulation-enhanced data augmentation method for ML pathloss prediction.
Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches
In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users.
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading
While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services.
Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems
Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware.
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning
Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchange.
Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement
For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions.
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks.
Challenges and Opportunities for Beyond-5G Wireless Security
The demand for broadband wireless access is driving research and standardization of 5G and beyond-5G wireless systems.
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices.
Propagation Measurements and Analyses at 28 GHz via an Autonomous Beam-Steering Platform
This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28 GHz V2X propagation modeling.
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios.
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders
Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks.
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment.
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization
Moreover, we are interested in sparse sensor selection using a marginalized weighted kernel approach to improve network resource efficiency by disabling less reliable sensors with minimal effect on classification performance. To achieve our goals, we develop a multi-sensor online kernel scalar quantization (MSOKSQ) learning strategy that operates on the sensor outputs at the fusion center.
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients.
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility.
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing
To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs.
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL.
Compressed Training for Dual-Wideband Time-Varying Sub-Terahertz Massive MIMO
6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access.
Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
We consider a practical setting where (i) the IRS reflection coefficients are achieved by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate.
A Robotic Antenna Alignment and Tracking System for Millimeter Wave Propagation Modeling
In this paper, we discuss the design of a sliding-correlator channel sounder for 28 GHz propagation modeling on the NSF POWDER testbed in Salt Lake City, UT.
Challenges and Opportunities of Future Rural Wireless Communications
Broadband access is key to ensuring robust economic development and improving quality of life.
A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification
Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model.
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach
Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates.
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals.
Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach
Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently.
Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion
A data tensor is constructed by collecting beam-training measurements on a subset of positions and beams, and a hybrid noisy tensor completion (HNTC) algorithm is proposed to predict the received power across the coverage area, which exploits both the spatial smoothness and the low-rank property of MIMO channels.
Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale.
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks
We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e. g., the spectral radius) and the learning algorithm (e. g., the number of D2D rounds in different clusters).
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency.
Networking and Internet Architecture Signal Processing
