This is the code for the paper "RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction", IEEE TCCN.
Simulation of Digital Communication (physical layer) in Python.
Passively scan for Bluetooth Low Energy devices and attempt to fingerprint them
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks
GNU-radio wireless communication system lab
OpenDPD is an end-to-end learning framework built in PyTorch for power amplifier (PA) modeling and digital pre-distortion (DPD). You are cordially invited to contribute to this project by providing your own backbone neural networks, pretrained models or measured PA datasets.
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DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks
The Heterogeneous Radio Mobile Simulator
A PyTorch-based toolkit for simulating communication systems
DeepMIMOv4: A Toolchain and Database for Ray-tracing Datasets.
Simulation code for "A Novel SCA-Based Method for Beamforming Optimization in IRS/RIS-Assisted MU-MISO Downlink," by V. Kumar, R. Zhang, M. D. Renzo and L. -N. Tran, IEEE Wireless Communications Letters, doi: 10.1109/LWC.2022.3224316.
Plug-and-play tensor equivariant neural network toolbox for AI-assisted MU-MIMO transmission tasks.
Simulation code for "A max-min task offloading algorithm for mobile edge computing using non-orthogonal multiple access," by V. Kumar, M. F. Hanif, M. Juntti and L. -N. Tran, published in IEEE Transactions on Vehicular Technology, vol. 72, no. 9, pp. 12332-12337, Sept. 2023, doi: 10.1109/TVT.2023.3263791.
Simulates pruned DFT spread FBMC and compares the performance to OFDM, SC-FDMA and conventional FBMC. The included classes (QAM, DoublySelectiveChannel, OFDM, FBMC) can be reused in other projects.
A PyTorch implementation of the IEEE WCNC 2025 paper "Worst-Case MSE Minimization for RIS-Assisted mmWave MU-MISO Systems With Hardware Impairments and Imperfect CSI"
Machine learning accelerated Branch and Bound for Joint beamforming and antenna selection
Simulation code for "Achievable Rate Maximization for Underlay Spectrum Sharing MIMO System with Intelligent Reflecting Surface," by V. Kumar, M. F. Flanagan, R. Zhang, and L. -N. Tran, IEEE Wireless Communications Letters, 2022, doi: 10.1109/LWC.2022.3180988.
Source code of IEEE TCOM paper: Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks With Double Dynamics.
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