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Found 41 papers, 9 papers with code
Reciprocity Calibration of Dual-Antenna Repeaters
We present a reciprocity calibration method for dual-antenna repeaters in wireless networks.
Faster Convergence with Less Communication: Broadcast-Based Subgraph Sampling for Decentralized Learning over Wireless Networks
Consensus-based decentralized stochastic gradient descent (D-SGD) is a widely adopted algorithm for decentralized training of machine learning models across networked agents.
Over-the-Air Federated Learning with Phase Noise: Analysis and Countermeasures
Wirelessly connected devices can collaborately train a machine learning model using federated learning, where the aggregation of model updates occurs using over-the-air computation.
Decentralized Learning over Wireless Networks with Broadcast-Based Subgraph Sampling
This work centers on the communication aspects of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD).
Dynamic Range Improvement in Bistatic Backscatter Communication Using Distributed MIMO
Backscatter communication (BSC) is a promising solution for Internet-of-Things (IoT) connections due to its low-complexity, low-cost, and energy-efficient solution for sensors.
Direct Link Interference Suppression for Bistatic Backscatter Communication in Distributed MIMO
Finally, simulation results show that the required dynamic range of the system is significantly decreased, and the detection performance of the BD symbol is increased, by the proposed algorithm compared to a system not using beamforming at the CE.
Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access
In particular, we investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD, considering the broadcast nature of wireless channels and the link dynamics in the communication topology.
Using Mobile Phones for Participatory Detection and Localization of a GNSS Jammer
Evaluations are performed using a Samsung S20+ mobile phone as participatory sensor and a Spirent GSS9000 GNSS simulator to generate GNSS and jamming signals.
Dynamic Scheduling for Federated Edge Learning with Streaming Data
In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints.
Phase Calibration of Distributed Antenna Arrays
Antenna arrays can be either reciprocity calibrated (R-calibrated), which facilitates reciprocity-based beamforming, or fully calibrated (F-calibrated), which additionally facilitates transmission and reception in specific physical directions.
25 Years of Signal Processing Advances for Multiantenna Communications
In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries.
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc.
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents.
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations
In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server at the same time.
Detecting Abrupt Changes in Channel Covariance Matrix for MIMO Communication
Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time.
Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training
The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges.
Participatory Sensing for Localization of a GNSS Jammer
The notion of participatory sensing, or crowdsensing, is that a large ensemble of voluntary contributors provides the measurements, rather than relying on dedicated sensing infrastructure.
Location-based Initial Access for Wireless Power Transfer with Physically Large Arrays
Specular components can be predicted by means of a geometric model.
Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System
Deep learning (DL) architectures have been successfully used in many applications including wireless systems.
Detection of Abrupt Change in Channel Covariance Matrix for Multi-Antenna Communication
This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.
Learning to Perform Downlink Channel Estimation in Massive MIMO Systems
The second one is a deep-learning-based approach that uses a neural network to identify a mapping between the available information and the effective channel gain.
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning
Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes.
Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data
Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home.
Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network.
Consensus-Based Distributed Computation of Link-Based Network Metrics
Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors.
Distributed Computing
Distributed, Parallel, and Cluster Computing
Social and Information Networks
Signal Processing
Massive Machine Type Communication Pilot-Hopping Sequence Detection Architectures Based on Non-Negative Least Squares for Grant-Free Random Access
The energy consumption is about 300 nJ/detection for the fast projected gradient algorithm using 256 iterations, leading to a convergence close to the theoretical.
Massively Distributed Antenna Systems with Non-Ideal Optical Fiber Front-hauls: A Promising Technology for 6G Wireless Communication Systems
Employing massively distributed antennas brings radio access points (RAPs) closer to users, thus enables aggressive spectrum reuse that can bridge gaps between the scarce spectrum resource and extremely high connection densities in future wireless systems.
Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks
To exploit the full potential of SR, in this paper, we address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx's RF signals using reconfigurable intelligent surfaces.
Max-Min Optimal Beamforming for Cell-Free Massive MIMO
This letter develops an optimum beamforming method for downlink transmissions in cell-free massive multiple-input multiple-output (MIMO) systems, which employ a massive number of distributed access points to provide concurrent services to multiple users.
Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks
Our objective is to maximize the weighted sum-rate (WSR) of all users by joint designing the beamforming at the access point (AP) and the phase vector of the RIS elements, while both the perfect channel state information (CSI) setup and the imperfect CSI setup are investigated.
Signal Processing
Optimizing Information Freshness in a Multiple Access Channel with Heterogeneous Devices
We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node.
Information Theory Networking and Internet Architecture Information Theory
Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?
The rate and energy efficiency of wireless channels can be improved by deploying software-controlled metasurfaces to reflect signals from the source to the destination, especially when the direct path is weak.
Information Theory Information Theory
Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks
In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels.
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks.
Power Control in Cellular Massive MIMO with Varying User Activity: A Deep Learning Solution
We first consider the sum spectral efficiency (SE) optimization problem for systems with a dynamically varying number of active users.
Information Theory Information Theory
How Energy-Efficient Can a Wireless Communication System Become?
Is this roughly as energy-efficient future systems (5G and beyond) can become, or are we still far from the physical limits?
Information Theory Information Theory
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks.
Massive MIMO in Sub-6 GHz and mmWave: Physical, Practical, and Use-Case Differences
The use of base stations (BSs) and access points (APs) with a large number of antennas, called Massive MIMO (multiple-input multiple-output), is a key technology for increasing the capacity of 5G networks and beyond.
Information Theory Information Theory
A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems
The Massive MIMO (multiple-input multiple-output) technology has great potential to manage the rapid growth of wireless data traffic.
Information Theory Networking and Internet Architecture Information Theory
Kernel Methods for Accurate UWB-Based Ranging with Reduced Complexity
For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning.
Fingerprinting-Based Positioning in Distributed Massive MIMO Systems
Location awareness in wireless networks may enable many applications such as emergency services, autonomous driving and geographic routing.
