David Gesbert
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View article: Spatial domain prediction of optimal MIMO beam alignment pairs in D2D networks
Spatial domain prediction of optimal MIMO beam alignment pairs in D2D networks Open
International audience
View article: Optimal SSB Beam Planning and UAV Cell Selection for 5G Connectivity on Aerial Highways
Optimal SSB Beam Planning and UAV Cell Selection for 5G Connectivity on Aerial Highways Open
In this article, we introduce a method to optimize 5G massive multiple-input multiple-output (mMIMO) connectivity for unmanned aerial vehicles (UAVs) on aerial highways through strategic cell association. UAVs operating in 3D space encount…
View article: Revisiting matching pursuit: Beyond approximate submodularity
Revisiting matching pursuit: Beyond approximate submodularity Open
We study the problem of selecting a subset of vectors from a large set to obtain the best signal representation over a family of functions. Although greedy methods have been widely used to tackle this problem and many of those have been an…
View article: Global Scale Self-Supervised Channel Charting with Sensor Fusion
Global Scale Self-Supervised Channel Charting with Sensor Fusion Open
The sensing and positioning capabilities foreseen in 6G have great potential for technology advancements in various domains, such as future smart cities and industrial use cases. Channel charting has emerged as a promising technology in re…
View article: Coordinated Machine Learning for Energy Efficient D2D Communication
Coordinated Machine Learning for Energy Efficient D2D Communication Open
International audience
View article: Massive MIMO for Aerial Highways: Enhancing Cell Selection via SSB Beams Optimization
Massive MIMO for Aerial Highways: Enhancing Cell Selection via SSB Beams Optimization Open
[EN] In this article, we introduce a novel approach for enhancing cellular connectivity for unmanned aerial vehicles (UAVs) on aerial highways via terrestrial 5G networks. Owing to their ability to navigate 3D space, UAVs may experience fa…
View article: Communication-Efficient Federated Learning via Regularized Sparse Random Networks
Communication-Efficient Federated Learning via Regularized Sparse Random Networks Open
This work presents a new method for enhancing communication efficiency in stochastic Federated Learning that trains over-parameterized random networks. In this setting, a binary mask is optimized instead of the model weights, which are kep…
View article: A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G Open
Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both…
View article: Robust PAC<i> <sub>m</sub> </i>: Training Ensemble Models Under Misspecification and Outliers
Robust PAC<i> <sub>m</sub> </i>: Training Ensemble Models Under Misspecification and Outliers Open
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers. Probably approximately correct (PAC)-Bayes theory demonstrates that the free energy criterion minimi…
View article: Machine Learning-Based Channel Quality Prediction in 6G Mobile Networks
Machine Learning-Based Channel Quality Prediction in 6G Mobile Networks Open
International audience
View article: Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks Open
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms. Multi-agent reinforcement learning (MARL) has emerged …
View article: UAV Trajectory Optimization and Tracking for User Localization in Wireless Networks
UAV Trajectory Optimization and Tracking for User Localization in Wireless Networks Open
In this paper, we investigate the problem of UAV-aided user localization in wireless networks. Unlike the existing works, we do not assume perfect knowledge of the UAV location, hence we not only need to localize the users but also to trac…
View article: Revisiting Matching Pursuit: Beyond Approximate Submodularity
Revisiting Matching Pursuit: Beyond Approximate Submodularity Open
We study the problem of selecting a subset of vectors from a large set, to obtain the best signal representation over a family of functions. Although greedy methods have been widely used for tackling this problem and many of those have bee…
View article: Integrated Access and Backhaul in 5G with Aerial Distributed Unit using OpenAirInterface
Integrated Access and Backhaul in 5G with Aerial Distributed Unit using OpenAirInterface Open
In this work, we propose an UAV-aided Integrated Access and Backhaul (IAB) system design offering 5G connectivity to ground users. UAV is integrated with a distributed unit (DU) acting as an aerial DU, which can provide 5G wireless backhau…
View article: User-Centric Federated Learning: Trading off Wireless Resources for Personalization
User-Centric Federated Learning: Trading off Wireless Resources for Personalization Open
Statistical heterogeneity across clients in a Federated Learning (FL) system increases the algorithm convergence time and reduces the generalization performance, resulting in a large communication overhead in return for a poor model. To ta…
View article: Some power allocation algorithms for cognitive uplink satellite systems
Some power allocation algorithms for cognitive uplink satellite systems Open
Cognitive satellite communication (SatCom) is rapidly emerging as a promising technology to overcome the scarcity of the exclusive licensed band model in order to fulfill the increasing demand for high data rate services. The paper address…
View article: A Novel Metric for mMIMO Base Station Association for Aerial Highway Systems
A Novel Metric for mMIMO Base Station Association for Aerial Highway Systems Open
In this article, we introduce a new metric for driving the serving cell selection process of a swarm of cellular connected unmanned aerial vehicles (CCUAVs) located on aerial highways when served by a massive multiple input multiple output…
View article: Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications Open
This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep learning techniques adopt a frequentist framework, and are …
View article: Channel Reuse for Backhaul in UAV Mobile Networks with User QoS Guarantee
Channel Reuse for Backhaul in UAV Mobile Networks with User QoS Guarantee Open
In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we stu…
View article: User-Centric Federated Learning: Trading off Wireless Resources for Personalization
User-Centric Federated Learning: Trading off Wireless Resources for Personalization Open
Statistical heterogeneity across clients in a Federated Learning (FL) system increases the algorithm convergence time and reduces the generalization performance, resulting in a large communication overhead in return for a poor model. To ta…
View article: Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems
Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems Open
International audience
View article: QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs
QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs Open
The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, such potential improvement requires an efficient positioning…
View article: Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations
Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations Open
Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of wireless networks. Such potential enhancement, however, re…
View article: Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications Open
This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep learning techniques adopt a frequentist framework, and are …
View article: A Partial Reciprocity-Based Channel Prediction Framework for FDD Massive MIMO With High Mobility
A Partial Reciprocity-Based Channel Prediction Framework for FDD Massive MIMO With High Mobility Open
Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the ``curse of mobili…
View article: Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance Open
Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where decisions of one agent directly affect the other agent's optimal…
View article: UAV-Aided Multi-Community Federated Learning
UAV-Aided Multi-Community Federated Learning Open
In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In th…
View article: Communication-Efficient Distributionally Robust Decentralized Learning
Communication-Efficient Distributionally Robust Decentralized Learning Open
Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator. In the case of heterogeneous data distri…
View article: A Channel Estimation Framework for High-mobility FDD Massive MIMO using Partial Reciprocity
A Channel Estimation Framework for High-mobility FDD Massive MIMO using Partial Reciprocity Open
The estimation of Channel State Information (CSI) is one of the most difficult tasks for massive multiple-input multiple-output (MIMO) in frequency division duplex (FDD) mode. It is even more challenging in high-mobility scenarios. In this…