Zobrazeno 1 - 10
of 166
pro vyhledávání: '"AKROUT, Mohamed"'
This paper introduces an algorithmic solution to a broader class of unlabeled sensing problems with multiple measurement vectors (MMV). The goal is to recover an unknown structured signal matrix, $\mathbf{X}$, from its noisy linear observation matrix
Externí odkaz:
http://arxiv.org/abs/2406.08290
The ongoing fifth-generation (5G) standardization is exploring the use of deep learning (DL) methods to enhance the new radio (NR) interface. Both in academia and industry, researchers are investigating the performance and complexity of multiple DL a
Externí odkaz:
http://arxiv.org/abs/2405.11072
Approximate message passing (AMP) algorithms are devised under the Gaussianity assumption of the measurement noise vector. In this work, we relax this assumption within the vector AMP (VAMP) framework to arbitrary independent and identically distribu
Externí odkaz:
http://arxiv.org/abs/2402.04111
Autor:
Akrout, Mohamed
Existing large language models (LLMs) are known for generating "hallucinated" content, namely a fabricated text of plausibly looking, yet unfounded, facts. To identify when these hallucination scenarios occur, we examine the properties of the generat
Externí odkaz:
http://arxiv.org/abs/2309.01245
Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disa
Externí odkaz:
http://arxiv.org/abs/2307.07359
Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless tran
Externí odkaz:
http://arxiv.org/abs/2303.08106
We investigate the benefits of mutual coupling effects between the passive elements of intelligent reconfigurable surfaces (IRSs) on maximizing the achievable rate of downlink Internet-of-Things (IoT) networks. In this paper, we present an electromag
Externí odkaz:
http://arxiv.org/abs/2302.11130
Autor:
Akrout, Mohamed, Gyepesi, Bálint, Holló, Péter, Poór, Adrienn, Kincső, Blága, Solis, Stephen, Cirone, Katrina, Kawahara, Jeremy, Slade, Dekker, Abid, Latif, Kovács, Máté, Fazekas, István
Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting. However, labeled training data for real-world applications such as healthcare is limited and difficult to access gi
Externí odkaz:
http://arxiv.org/abs/2301.04802
With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency a
Externí odkaz:
http://arxiv.org/abs/2211.10753
The evolution of behavior of dermatology patients has seen significantly accelerated change over the past decade, driven by surging availability and adoption of digital tools and platforms. Through our longitudinal analysis of this behavior within Tu
Externí odkaz:
http://arxiv.org/abs/2208.02852