Communications
CAMARA is an open-source project which is part of Linux Foundation. It aims at defining service APIs by combining network APIs, over an operator domain. CAMARA works in close collaboration with the GSMA Operator Platform Group to align API requirements and definitions, and to publish APIs.
IEEE 5G testbed supports the CAMARA initiative and provides these APIs for data privacy and regulatory requirements and facilitate application to network integration.
CAMARA APIs allow to tackle several types of business use cases leveraging 4G/5G network capabilities.
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Internet telephony consists of a combination of hardware and software that enables you to
use the Internet as the transmission medium for telephone calls. For users who have free,
or fixed-price Internet access, Internet telephony software essentially provides free
telephone calls anywhere in the world. In its simplest form, PC-to-PC Internet telephony
can be as easy as hooking up a microphone to your computer and sending your voice
through a cable modem to a person who has Internet telephony software that is
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Modern, industrial automation is unthinkable without wireless communications. Thereby, wireless links provide the necessary flexibility for industrial real-time applications. On the other side, these applications need at the same time a wireless communications link that works ultra-reliably. In communications systems, unreliability can be traced back to the fading behavior of the wireless radio channel as the medium between the communicating entities.
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This dataset used in the research paper "JamShield: A Machine Learning Detection System for Over-the-Air Jamming Attacks." The research was conducted by Ioannis Panitsas, Yagmur Yigit, Leandros Tassiulas, Leandros Maglaras, and Berk Canberk from Yale University and Edinburgh Napier University.
For any inquiries, please contact Ioannis Panitsas at ioannis.panitsas@yale.edu.
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The design of this ASS structure is based on a dual-layer frequency-selective surface (FSS), where the unit cell consists of a single substrate and two metal layers. This structure achieves resonant transmission at specific oblique angles while blocking electromagnetic (EM) waves at other angles. In addition, the equivalent circuit model (ECM) is utilized to analyze the proposed structure, revealing the influence of oblique incidenc on circuit parameters.
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This dataset consists of network packet traces collected in 2023 on the 5G infrastructure deployed at Chalmers University of Technology.
The dataset includes 1,912 pcap files, distributed across 8 folders. Each pcap file captures 1 minute of encrypted network traffic generated by one of the following 8 popular mobile applications:
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The Multi-Server Multi-User computation offloading (MSCO) dataset is a dataset based on the scenario of multi-server multi-user binary computing offloading. It is characterized by the connection status between users and edge servers, user task information, and server computational resource information. The solution aims to minimize the total cost of power consumption and latency of all tasks. The labels are the offloading decisions of user tasks and the computational resource allocation of edge servers. The features and labels of this dataset are graph-structured.
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The TiHAN-V2X Dataset was collected in Hyderabad, India, across various Vehicle-to-Everything (V2X) communication types, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Vehicle (I2V), and Vehicle-to-Cloud (V2C). The dataset offers comprehensive data for evaluating communication performance under different environmental and road conditions, including urban, rural, and highway scenarios.
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The Unified Multimodal Network Intrusion Detection System (UM-NIDS) dataset is a comprehensive, standardized dataset that integrates network flow data, packet payload information, and contextual features, making it highly suitable for machine learning-based intrusion detection models. This dataset addresses key limitations in existing NIDS datasets, such as inconsistent feature sets and the lack of payload or time-window-based contextual features.
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The dataset consists of uplink channel gains, downlink channel gains and uplink to downlink channel gains along with corresponding power allocations for uplink users and downlink users across all subcarriers. Additionally, it consists of NOMA decoding order for successful implementation of SIC at NOMA receiver. The number of UL users and DL users are considered as N=M=6, and subcarriers are S=9. Each column in the dataset is a sample for fading channel realization and it should be converted back to the matrix to compute sumrate.
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