These channel data were created, and are made available, by the WiDeS group at USC; specifically they are based on ray-tracing simulations conducted by Dr. Zheda using the ray-tracing tool Wireless Insite and a database of the geographical and morphological features of the propagation environment, i.e., the University Park Campus of USC. The dataset was then prepared through pre-processing methods such as interpolation and data augmentation by Dr. Juhyung Lee. The details of the dataset can be found in [Lee 2022a]. If you have any questions regarding the data, please email Juhyung Lee at juhyung.lee@usc.edu.
Copyright Juhyung Lee, University of Southern California. The code may be used for non-commercial purposes only. Redistribution prohibited. If you use this code for results presented in research papers, please cite as follows: Data were obtained from [Lee2022], whose data are available at [WiDeS_Lee2022]
[Lee2022] J.H. Lee et al., "PMNet: Robust Pathloss Map Prediction via Supervised Learning," arXiv preprint arXiv:2106.15276, 2022.
[WiDeS_Lee2022] J.H. Lee et al. "Open-Source Pathloss Map (Large-Scale Channel) Prediction Dataset. URL: https://wides.usc.edu/research_matlab.html
This code is used to generate individual preferences of users. We provide two functions that can generate individual preferences of users based on two real-world dataset. The details of the modeling and parameterization, and the recipe of the generator can be found in [Lee, 2019]. The use of the generator should be as simple as using a Matlab function. Please find the functions and see the Readme file in the download.
Copyright Ming-Chun Lee, University of Southern California. The code may be used for non-commercial purposes only.
Redistribution prohibited. If you use this code for results presented in research papers, please cite as follows:
Files were generated according to the model of [Lee 2019], whose MATLAB code is available at [WiDeS]
[Lee 2019] M.-C. Lee, A. F. Molisch, N. Sastry, and A. Raman, “Individual preference probability modeling and parameterization for video content in wireless caching networks,” IEEE/ACM Trans. Netw., 2019
[WiDeS] M.-C. Lee, A. F. Molisch, N. Sastry, and A. Raman, “Code for generating files with realistic popularity distribution,”. URL: https://wides.usc.edu/research_matlab.html
These channel data are obtained from channel measurement campaigns between the drone transmitter and a 128-element cylindrical antenna array receiver. The drone flew across a trajectory on the University Park campus of USC acting as an access point and the receiver was put into four different locations. The measurement was conducted at 3.5 GHz, with 46 MHz bandwidth and 2301 subcarriers. The details of the measurement campaign can be found from [Choi2021Using]. If you have any questions regarding the data, please email Thomas Choi at choit@usc.edu.
Copyright Thomas Choi, University of Southern California. The code may be used for non-commercial purposes only. Redistribution prohibited. If you use this code for results presented in research papers, please cite as follows: Data were obtained from [Choi2021Using], whose data are available at [WiDeS_Choi2021Using]
[Choi2021Using] T. Choi et al., "Using a drone sounder to measure channels for cell-free massive MIMO systems," arXiv preprint arXiv:2106.15276, 2021.
[WiDeS_Choi2021Using] T. Choi et al., “Open-Source Cell-Free Massive MIMO Channel Data 2020”. URL: https://wides.usc.edu/research_matlab.html