Wireless Networking

Dataset: IQ samples of LTE, 5G NR, WiFi, ITS-G5, and C-V2X PC5

Thes dataset comprises IQ samples captured from ITSG-5, C-V2X PC5, WiFi, LTE, 5G NR and Noise. Six different dataset bunches are collected at sampling rates of 1, 5, 10, 15 , 20, and 25 Msps. In each dataset cluster, 7500 examples are collected from each considered technology. The dataset size at each considered sampling rate is 7500 X M, where M can be 44, 220, 440, 660, 880, and 1100 for a sampling rate of 1, 5, 10, 15 , 20, and 25 Msps,respectively.

 

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Dataset for Identification of Saturated and Unsaturated WiFi Networks

The Dataset comprises the histogram of Inter-frame spacing for saturated and unsaturated WiFi networks.

In order to develop a CNN model that can classify saturated and unsaturated traffic in WiFi network, we prepared a large dataset that represents the traffic characteristics of both cases. 

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Dataset for Identification of Saturated and Unsaturated WiFi Networks

The Dataset comprises the histogram of Inter-frame spacing for saturated and unsaturated WiFi networks.

In order to develop a CNN model that can classify saturated and unsaturated traffic in WiFi network, we prepared a large dataset that represents the traffic characteristics of both cases. 

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335 Views

A distinctive low-profile 2x2 MIMO antenna system for Wi-Fi 7 applications is presented in this paper that is compact, easily manufactured and with excellent performance. Due to its physical properties and RF performance, the design can be placed in hidden locations for various applications such as the automotive field in the front side mirrors or front dashboard, and consumer products in laptops and internet wireless routers.

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Physical layer security (PLS) is seen as the means to enhance physical layer trustworthiness in 6G. This work provides a proof-of-concept for one of the most mature PLS technologies, i.e., secret key generation (SKG) from wireless fading coefficients during the channel’s coherence time. As opposed to other works, where only specific parts of the protocol are typically investigated, here, we implement the full SKG chain in four indoor experimental campaigns.

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The advancements in the field of telecommunications have resulted in an increasing demand for robust, high-speed, and secure connections between User Equipment (UE) instances and the Data Network (DN). The implementation of the newly defined 3rd Generation Partnership Project 3GPP (3GPP) network architecture in the 5G Core (5GC) represents a significant leap towards fulfilling these demands. This architecture promises faster connectivity, low latency, higher data transfer rates, and improved network reliability.

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A new approach addressing the spectrum scarcity challenge in 6G networks by implementing an enhanced licensed shared access (LSA) framework is considered. The proposed mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism in which the determination of weights is based on the results of the previous auctions.

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Smart home automation is part of the Internet of Things that enables house remote control via the use of smart devices, sensors, and actuators. Despite its convenience, vulnerabilities in smart home devices provide attackers with an opportunity to break into the smart home infrastructure without permission. In fact, millions of Z-Wave smart home legacy devices are vulnerable to wireless injection attacks due to the lack of encryption support and the lack of firmware updates.

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The DeepVerse 6G Machine Learning Challenge is a student competition organized by the Student and Outreach Subcommittee (SOSC) of the IEEE Information Theory Society, in collaboration with the Wireless Intelligence Lab at Arizona State University (ASU) and the Information Theory Labs at National Yang Ming Chiao Tung University.

Last Updated On: 
Sun, 06/18/2023 - 23:51

The dataset includes the coordinates of a 2980 antenna array. 

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