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AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication Networks

Yifei Zhang, Zhen Gao, Jingjing Zhao, Ziming He, Yunsheng Zhang, Chen Lu, Pei Xiao

TL;DR

This work addresses the challenge of achieving true synergy between communication and sensing in 6G ISAC systems, where conventional designs suffer from mutual tradeoffs and high signaling overhead. It proposes an AI-driven framework, JCASCasterNet, built around a two-stage frame structure that exploits the correlation between C&S channels and uses a single sensing antenna to extract and reconstruct channel semantics for both modalities. The approach combines Stage 1 basic pilots with Stage 2 enhanced distributed sensing, and introduces a knowledge-inspired reconstruction strategy that leverages an ASE-based loss for communications and a cosine-similarity loss for sensing, enabling end-to-end optimization. Case studies show significant ASE gains (around 1 bps/Hz) under limited pilot/signaling budgets and robustness to low pilot SNR, highlighting the potential of channel semantic acquisition to reduce hardware and signaling overhead while enabling scalable ISAC operations; the paper also outlines open issues in modeling, explainability, lightweight deployment, multi-BS sensing, and security for future 6G networks.

Abstract

Motivated by the need for increased spectral efficiency and the proliferation of intelligent applications, the sixth-generation (6G) mobile network is anticipated to integrate the dual-functions of communication and sensing (C&S). Although the millimeter wave (mmWave) communication and mmWave radar share similar multiple-input multiple-output (MIMO) architecture for integration, the full potential of dual-function synergy remains to be exploited. In this paper, we commence by overviewing state-of-the-art schemes from the aspects of waveform design and signal processing. Nevertheless, these approaches face the dilemma of mutual compromise between C&S performance. To this end, we reveal and exploit the synergy between C&S. In the proposed framework, we introduce a two-stage frame structure and resort artificial intelligence (AI) to achieve the synergistic gain by designing a joint C&S channel semantic extraction and reconstruction network (JCASCasterNet). With just a cost-effective and energy-efficient single sensing antenna, the proposed scheme achieves enhanced overall performance while requiring only limited pilot and feedback signaling overhead. In the end, we outline the challenges that lie ahead in the future development of integrated sensing and communication networks, along with promising directions for further research.

AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication Networks

TL;DR

This work addresses the challenge of achieving true synergy between communication and sensing in 6G ISAC systems, where conventional designs suffer from mutual tradeoffs and high signaling overhead. It proposes an AI-driven framework, JCASCasterNet, built around a two-stage frame structure that exploits the correlation between C&S channels and uses a single sensing antenna to extract and reconstruct channel semantics for both modalities. The approach combines Stage 1 basic pilots with Stage 2 enhanced distributed sensing, and introduces a knowledge-inspired reconstruction strategy that leverages an ASE-based loss for communications and a cosine-similarity loss for sensing, enabling end-to-end optimization. Case studies show significant ASE gains (around 1 bps/Hz) under limited pilot/signaling budgets and robustness to low pilot SNR, highlighting the potential of channel semantic acquisition to reduce hardware and signaling overhead while enabling scalable ISAC operations; the paper also outlines open issues in modeling, explainability, lightweight deployment, multi-BS sensing, and security for future 6G networks.

Abstract

Motivated by the need for increased spectral efficiency and the proliferation of intelligent applications, the sixth-generation (6G) mobile network is anticipated to integrate the dual-functions of communication and sensing (C&S). Although the millimeter wave (mmWave) communication and mmWave radar share similar multiple-input multiple-output (MIMO) architecture for integration, the full potential of dual-function synergy remains to be exploited. In this paper, we commence by overviewing state-of-the-art schemes from the aspects of waveform design and signal processing. Nevertheless, these approaches face the dilemma of mutual compromise between C&S performance. To this end, we reveal and exploit the synergy between C&S. In the proposed framework, we introduce a two-stage frame structure and resort artificial intelligence (AI) to achieve the synergistic gain by designing a joint C&S channel semantic extraction and reconstruction network (JCASCasterNet). With just a cost-effective and energy-efficient single sensing antenna, the proposed scheme achieves enhanced overall performance while requiring only limited pilot and feedback signaling overhead. In the end, we outline the challenges that lie ahead in the future development of integrated sensing and communication networks, along with promising directions for further research.
Paper Structure (15 sections, 5 figures, 1 table)

This paper contains 15 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Overview of the State-of-The-Art
  3. Waveform Design
  4. Signal Processing
  5. Proposed Joint Communication and Sensing Channel Semantic Acquisition Scheme
  6. Stage 1: Basic Pilot Based Channel Sounding
  7. Stage 2: Enhanced Pilot Based Distributed Active/Passive Sensing
  8. Channel Semantic Reconstruction
  9. Case Study
  10. Open Issues and Future Directions
  11. Channel Modeling of ISAC Networks
  12. Synergy Performance Analysis under Explainable AI
  13. Lightweight Deployment and Multi-BS Sensing
  14. Security Issues of ISAC Networks
  15. Conclusions

Figures (5)

  • Figure 1: The proposed ISAC system with a single sensing antenna: (a) the hardware architecture of the communication antenna array and the sensing antenna at the ISAC station, and (b) the proposed two-stage frame sutructure.
  • Figure 2: The block diagram of the proposed JCASCasterNet.
  • Figure 3: ASE performance comparison versus the data SNR: (a) $\textrm{Pilot SNR} = -10 \ \textrm{dB}$; (b) $\textrm{Pilot SNR} = 0 \ \textrm{dB}$; (c) $\textrm{Pilot SNR} = 10 \ \textrm{dB}$.
  • Figure 4: Sensing channel semantic reconstruction performance with different loss functions: (a) Cosine Similarity; (b) NMSE.
  • Figure 5: Angle estimation performance comparison of different loss functions: (a) Cosine Similarity; (b) NMSE.