Communications
In-vehicle networks are responsible for safety-critical control applications, depending on data communication between electronic control units, and most are based on the CAN protocol. A huge amount of data is necessary for reliability, safety, and cybersecurity analysis in today's automotive solutions, especially to feed machine learning models. It is relevant to provide comprehensive datasets about CAN communication and different driving situations, which represents a lack in recent research because most public datasets are very limited.
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This dataset contains signals collected on 7 different dates from 13 wired Ethernet network cards transmitted using the 100BASE-TX protocol. The signal is collected at the access point (switch side) using an oscilloscope with a sampling rate of 625Mbps and a sampling accuracy of 8 bits.
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This MATLAB script implements a simulation framework for an IRS-assisted IoT network with multiple nodes in a 3D environment. The code integrates a number of advanced wireless features, including elevation-aware IRS phase alignment, dynamic spectrum sensing for channel allocation, inter-node interference modeling, and Doppler effects from node mobility. Operating at microwaves with configurable elements, the system achieves realistic performance metrics through iterative optimization.
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This MATLAB script presents an innovative approach to 5G beamforming prediction using a sequence-based LSTM neural network. Unlike conventional methods that predict only final vectors, this solution provides time-stepped predictions across entire sequences, enabling real-time tracking of dynamic channel conditions. The framework achieves stable training convergence while maintaining physically meaningful performance metrics, including realistic path loss and SNR values.
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71 Views
The data was collected by a tester holding a Xiaomi 13 smartphone while walking and collecting data in an underground parking lot covering a 16x70m area. The data includes 5G radio features and geomagnetic field information.
Collection Time: From 09:58 AM to 10:34 AM on July 13, 2024.
Total Samples: 12,800
Training Set (including validation set): 10,240
Test Set: 2,560
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42 Views
This MATLAB script implements a reinforcement learning (RL) approach to optimize IRS phase configurations in a MIMO wireless system. The implementation features a basic MIMO setup with a 16-element IRS operating at 12 GHz (mid-band frequency). Using the policy gradient method with a two-layer neural network, it learns optimal phase shifts while considering user mobility and Rician fading channels. The system models both direct and IRS-reflected paths, incorporating realistic path loss and channel conditions.
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81 Views
This MATLAB script demonstrates an approach to beamforming and interference suppression in scenarios with multiple users and multiple interferers. It constructs an N-element linear array, computes beamformer weights through a generalized eigen-decomposition of summed desired and interference correlation matrices, and then runs a Monte Carlo simulation to estimate the Signal-to-Interference-plus-Noise Ratio (SINR) for one of the users under random channel conditions.
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133 Views
Nonlinear distortion is critical for optical communication systems with a high baud rate and a high-order modulation format. Thus, a simple and accurate method to measure the nonlinear distortion is highly desired. Although simple notch, which directly removes the certain frequency components of nonlinear system input and then measures the re-growth components of nonlinear system output, is straightforward to measure nonlinear distortion, it is only applicable to the Gaussian signal.
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89 Views