The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to evaluate the feasibility of in-band full-duplex acoustic communications in the underwater environment. The experiment was conducted in Lake Tuscaloosa in June 2021. Two boats, each equipped with an instrument line, were deployed for the experiment. For the local instrument line, the transducer was mounted 6 meters below the water surface. The receiving array was placed 7 meters below the transducer.
The dataset is oriented on encrypted traffic classification problems. The dataset contains three classes of flows: web flows, YouTube flows, and Netflixflows. These classes are chosen because web and video traffic account for 90% of global traffic, while YouTube and Netflix are the largest video services. The structure of the dataset is as follows. It includes 100 download traces of the most popular web pages according to https://httparchive.org, 100 the most popular YouTube videos, and 50 Netflix series and movies.
This dataset includes the measured Downlink (DL) signal-to-noise ratios (SNRs) at the User Equipments (UEs), adopting one of the beams of the beamforming codebook employed at the Base Stations (BSs). First, we configured a system-level simulator that implements the most recent Third Generation Partnership Project (3GPP) 3D Indoor channel models and the geometric blockage Model-B to simulate an indoor network deployment of BSs and UEs adopting Uniform Planar Arrays (UPAs) and a codebook based transmission.
The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.
This code and related data is related to research work on quantified benchmarking of Reconfigurable Intelligent Surface (RIS). Related research article has been submitted to VTM titled "Reconfigurable Intelligent Surfaces: Tradeoff between Unit-Cell- and Surface-Level Design under Quantifiable Benchmarks". 'ReadMe.text' file in 'RIS_restricted_02.zip' explains how to use the code to generate RIS configurations for RIS of arbitrary size and unit cell design, which can accomodate restricting to certain size grouped control.
This is a CSI dataset towards 5G NR high-precision positioning,
which is fine-grained, general-purpose and 3GPP R16 standards-complied.
5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).
Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.
The dataset_[SNR]_[date]_[time].mat contains:
1) a 4-D matrix, features, representing the feature data, and
2) a structure array, labels, labeling the ground truth of UE positions.
[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.
The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).
The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.
The value of Ng corresponds to the number of UEs in labels.
Colsed beta test is running.
In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.
In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.
Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.
To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.
1) Recruit participants for colsed beta test.
1)Expend our dataset with more CSI data with low SNR levels noise.
2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.
Closed beta test will start after suggestion collection.
1)Expend our dataset with more CSI data with different SNR levels noise.
2)Publish map files for Scenario 1 indoor office.
The OFMC back-end in AVISPA is used to carry out security verification experiments in our scheme for the login and authentication phase, Case1 in the password and biometric renewal phase, and Case2 in the password and biometric renewal phase, respectively.
Here's the experimental result for the login and authentication phase as a show.
This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples.
This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples. That is, 6×16×4096 = 393216 pieces in total.