Zobrazeno 1 - 10
of 139
pro vyhledávání: '"SAÏD, Karim"'
Deep learning is making a profound impact in the physical layer of wireless communications. Despite exhibiting outstanding empirical performance in tasks such as MIMO receive processing, the reasons behind the demonstrated superior performance improv
Externí odkaz:
http://arxiv.org/abs/2410.07072
In the physical layer (PHY) of modern cellular systems, information is transmitted as a sequence of resource blocks (RBs) across various domains with each resource block limited to a certain time and frequency duration. In the PHY of 4G/5G systems, d
Externí odkaz:
http://arxiv.org/abs/2409.02785
The paper proposes a new architecture for Distributed MIMO (D-MIMO) in which the base station (BS) jointly transmits with wireless mobile nodes to serve users (UEs) within a cell for 6G communication systems. The novelty of the architecture lies in t
Externí odkaz:
http://arxiv.org/abs/2409.02055
Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the O
Externí odkaz:
http://arxiv.org/abs/2406.16868
Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the O
Externí odkaz:
http://arxiv.org/abs/2311.08543
Deep learning has seen a rapid adoption in a variety of wireless communications applications, including at the physical layer. While it has delivered impressive performance in tasks such as channel equalization and receive processing/symbol detection
Externí odkaz:
http://arxiv.org/abs/2310.04956
Recurrent neural networks (RNNs) are known to be universal approximators of dynamic systems under fairly mild and general assumptions. However, RNNs usually suffer from the issues of vanishing and exploding gradients in standard RNN training. Reservo
Externí odkaz:
http://arxiv.org/abs/2308.02464
Publikováno v:
In Journal of Energy Storage 15 November 2024 102 Part A
Publikováno v:
In Materials Chemistry and Physics 15 February 2025 332
Publikováno v:
In Biochemical and Biophysical Research Communications January 2025 742