Skip to content

Latest commit

 

History

31 Commits

Folders and files

NameName
Last commit message
Last commit date
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

VeISAC

VeISAC: An End-to-End MIMO-OFDM-FMCW Framework for Integrated Sensing and Communication in 6G Vehicular Networks


Environment Setup

Step 1 — Clone the Repository

git clone https://github.com/mohababsa/VeISAC.git
cd VeISAC

Step 2 — Create the Conda Environment

conda env create -f environment.yml
conda activate veisac-env

Step 3 — Install PyTorch with CUDA 13 Support

bash install_torch.sh

Dataset and Simulation Parameters

VeISAC operates on channel datasets generated using DeepVerse 6G. DeepVerse 6G produces synchronized multi-modal data (MIMO channel tensors, radar returns, LiDAR point clouds, camera frames, and vehicle trajectories) from ray-traced urban vehicular scenarios. Dataset generation scripts and scenario configurations are available on the DeepVerse 6G website under the Scenarios tab.

The dataset used in our evaluations was generated by our team using the DeepVerse 6G pipeline after configuring the Outdoor 1 (O1) scenario (https://deepverse6g.net/scenario/O1). The generated dataset is publicly available at: [Dataset link — coming soon]

Once downloaded, place the dataset under data/ and update the path in the relevant configuration file under configs/.

The simulation parameters used across all experiments are listed below.

ISAC Shared Parameters OFDM Communication Parameters FMCW Radar Parameters
Parameter Value Parameter Value Parameter Value
Frequency band n257 (26.5–29.5 GHz) Sampling rate F_s,c 245.76 MHz Sampling rate F_s,r 200 MHz
Carrier frequency f_c 28 GHz Subcarrier spacing Δf 120 kHz Chirp slope μ 2.4×10¹³ Hz/s
System bandwidth B 200 MHz FFT size N_FFT 2048 Samples/chirp N_samp 1664
Wavelength λ ≈ 10.71 mm Active subcarriers N_sc 1633 Number of chirps N_c 128
Transmit power P_TX 43 dBm (20 W) CP length N_CP 1024 samples Chirp duration T_chirp 8.32 μs
ISAC-TX antennas M_t 4 (2×2 UPA) OFDM symbols/slot N_sym 14 RX FoV (az×el) ±90°×±90°
UE Com-RX M_c 2 (2×1 ULA) Pilot spacing (time) D_t 3 symbols Sensing noise figure ℱ_s 5 dB
Mono. BS Sen-RX M_r 4 (2×2 UPA) Pilot spacing (freq.) D_f 3 subcarriers Sensing noise power σ²_n,s −86.0 dBm
Bist. BS Sen-RX M_r 4 (2×2 UPA) Total pilots/slot 2725
Bist. UE Sen-RX M_r 2 (2×1 ULA) Default modulation QPSK
Antenna spacing d λ/2 = 5.35 mm RX FoV (az×el) 360°×180°
Power allocation angle θ = π/4 Comm. noise figure ℱ_c 7 dB
Power constraint cos²θ + sin²θ = 1 Comm. noise power σ²_n,c −84.0 dBm
TX FoV (az×el) 360°×180°

VeISAC Modules

ISAC-TX (veisac/tx/)

Implements the full transmitter chain: QAM modulation, pilot insertion, IFFT-based OFDM generation, linear FMCW chirp synthesis, and additive time-domain superposition ith trigonometric power allocation enforcing cos²θ + sin²θ = 1.

Com-RX (veisac/rx/)

Implements the OFDM communication receiver: CP removal, FFT demodulation, pilot-based LS/MMSE channel estimation with FMCW interference-aware regularization, MMSE equalization, power recovery, and bit demapping.

Sen-RX (veisac/rx/)

Implements the FMCW sensing receiver: NLMS-based OFDM interference mitigation, de-chirping, windowed range/Doppler FFT, zero-Doppler clutter removal, 2D OS-CFAR detection, MUSIC angle estimation, and CRB computation.

End-to-End Chain (veisac/isac_chain.py)

Top-level orchestrator coordinating TX, channel application, Com-RX, Sen-RX, and joint performance evaluation (PAPR, BER, throughput, range/velocity/angle errors, EIR, Equiv-MSE, Capacity-Distortion) across all three baseline topologies.


Quick Start

Run the full end-to-end signal processing simulation across all baselines and calculate the communication, sensing, and unified ISAC performance metrics:

conda activate veisac-env
python scripts/run_veisac.py 

Results and figures are saved under results/.


References

[1] U. Demirhan, A. Taha, S. Jiang, and A. Alkhateeb, "DeepVerse 6G: A Dataset Generation Framework for Multi-Modal Sensing and Communication Digital Twins," preprint, Feb. 2025.

[2] Remcom, "Wireless InSite," [Online]. Available: https://www.remcom.com/wireless-insite


License & Citation

1. VeISAC Paper: M. Ababsa, S. Ribouh, Y. El Hillali, and A. Rivenq, "VeISAC: An End-to-End MIMO-OFDM-FMCW Framework for ISAC in 6G Vehicular Networks," submitted to IEEE Transactions on Vehicular Technology, 2026.

About

VeISAC: An End-to-End MIMO-OFDM-FMCW Framework for Integrated Sensing and Communication in 6G Vehicular Networks

Resources

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages