IM-OCDM for ISAC: Joint Communication and Sensing Using Index Modulation and LFM Radar

Jose Miracy de Souza Fo, George Lucas Lima Takemiya, José Carlos Marinello

DOI: 10.14209/sbrt.2025.1571157297

Evento: XLIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2025)

Keywords: ISAC Transformers GRU IM_OCDM

Abstract

This paper presents an Index Modulation-enabled Orthogonal Chirp Division Multiplexing (IM-OCDM) waveform tailored for integrated sensing and communication (ISAC) in future 6G networks. The proposed approach leverages inactive subchirps to transmit Linear Frequency Modulated (LFM) radar signals, enabling dual functionality without requiring additional bandwidth. A Convolutional Neural Network combined with a Transformer-based attention mechanism is employed to accurately separate radar and communication components. This architecture effectively captures both local spatial features and global dependencies within the signal. Compared to conventional recurrent models such as \textbf{CNN-GRU-Attention}, the Transformer-based model demonstrates superior robustness under low-SNR conditions, achieving up to 100\% accuracy and F1-score at SNR levels of -5~dB and 0~dB. Meanwhile, the CNN-GRU-Attention baseline achieves peak F1-scores of 98.2\% under favorable SNR conditions but exhibits greater sensitivity in noisy environments. Simulation results confirm significant improvements in classification accuracy, spectrum efficiency, and radar sensing quality. This unified waveform design addresses key challenges in 6G ISAC systems and offers a scalable, efficient solution for complex signal processing tasks.

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