Zobrazeno 1 - 10
of 2 039
pro vyhledávání: '"A. Gesbert"'
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 encounter distinct
Externí odkaz:
http://arxiv.org/abs/2409.01812
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 recent years
Externí odkaz:
http://arxiv.org/abs/2405.04357
Recursive graph queries are increasingly popular for extracting information from interconnected data found in various domains such as social networks, life sciences, and business analytics. Graph data often come with schema information that describe
Externí odkaz:
http://arxiv.org/abs/2403.01863
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 kept fixed. Th
Externí odkaz:
http://arxiv.org/abs/2309.10834
Autor:
Zeng, Yong, Chen, Junting, Xu, Jie, Wu, Di, Xu, Xiaoli, Jin, Shi, Gao, Xiqi, Gesbert, David, Cui, Shuguang, Zhang, Rui
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 new challe
Externí odkaz:
http://arxiv.org/abs/2309.07460
Big data programming frameworks have become increasingly important for the development of applications for which performance and scalability are critical. In those complex frameworks, optimizing code by hand is hard and time-consuming, making automat
Externí odkaz:
http://arxiv.org/abs/2306.07690
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 as a soluti
Externí odkaz:
http://arxiv.org/abs/2306.02029
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 track the UAV l
Externí odkaz:
http://arxiv.org/abs/2305.14959
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 been analyzed
Externí odkaz:
http://arxiv.org/abs/2305.07782
Autor:
Mundlamuri, Rakesh, Esrafilian, Omid, Gangula, Rajeev, Kharade, Rohan, Roux, Cedric, Kaltenberger, Florian, Knopp, Raymond, Gesbert, David
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 backhaul access to
Externí odkaz:
http://arxiv.org/abs/2305.05983