Zobrazeno 1 - 10
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pro vyhledávání: '"LOVE, DAVID"'
Deep learning aided codes have been shown to improve code performance in feedback codes in high noise regimes due to the ability to leverage non-linearity in code design. In the additive white Gaussian broadcast channel (AWGN-BC), the addition of fee
Externí odkaz:
http://arxiv.org/abs/2410.17404
As the number of low Earth orbit (LEO) satellites rapidly increases, the consideration of frequency sharing or cooperation between geosynchronous Earth orbit (GEO) and LEO satellites is gaining attention. In this paper, we consider a hybrid GEO-LEO s
Externí odkaz:
http://arxiv.org/abs/2410.14902
Autor:
Arun, Ashwin Natraj, Lee, Byunghyun, Castiblanco, Fabio A., Buckmaster, Dennis R., Wang, Chih-Chun, Love, David J., Krogmeier, James V., Butt, M. Majid, Ghosh, Amitava
One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a n
Externí odkaz:
http://arxiv.org/abs/2409.12281
Matrix computations are a fundamental building-block of edge computing systems, with a major recent uptick in demand due to their use in AI/ML training and inference procedures. Existing approaches for distributing matrix computations involve allocat
Externí odkaz:
http://arxiv.org/abs/2408.05152
Dual-function radar-communication (DFRC) is a key enabler of location-based services for next-generation communication systems. In this paper, we investigate the problem of designing constant modulus waveforms for DFRC systems. For high-precision rad
Externí odkaz:
http://arxiv.org/abs/2406.18951
Opportunistic spectrum access has the potential to increase the efficiency of spectrum utilization in cognitive radio networks (CRNs). In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput while mi
Externí odkaz:
http://arxiv.org/abs/2404.14319
Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational com
Externí odkaz:
http://arxiv.org/abs/2404.15374
A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust performance against diverse channel conditions, they also have a major dra
Externí odkaz:
http://arxiv.org/abs/2402.09580
Autor:
Mohamed, Ahmed P., Lee, Byunghyun, Zhang, Yaguang, Hollingsworth, Max, Anderson, C. Robert, Krogmeier, James V., Love, David J.
Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challenges, this paper introduces a novel simulation-enhanced data augmentati
Externí odkaz:
http://arxiv.org/abs/2402.01969