AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication 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 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.
- X. Fang, W. Feng, Y. Chen, N. Ge and Y. Zhang, “Joint communication and sensing toward 6G: Models and potential of using MIMO,” IEEE Internet Things J., vol. 10, no. 5, pp. 4093-4116, March 2023.
- Z. Gao, S. Liu, Y. Su, Z. Li and D. Zheng, “Hybrid knowledge-data driven channel semantic acquisition and beamforming for cell-free massive MIMO,” IEEE J. Sel. Topics Signal Process., early access, doi: 10.1109/JSTSP.2023.3299175.
- R. C. Daniels, E. R. Yeh and R. W. Heath, “Forward collision vehicular radar with IEEE 802.11: Feasibility demonstration through measurements,” IEEE Trans. Veh. Technol., vol. 67, no. 2, pp. 1404-1416, Feb. 2018.
- F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths and L. Hanzo, “Joint radar and communication design: Applications, state-of-the-art, and the road ahead,” IEEE Trans. Commun., vol. 68, no. 6, pp. 3834-3862, June 2020.
- Y. Wu, F. Lemic, C. Han and Z. Chen, “Sensing integrated DFT-spread OFDM waveform and deep learning-powered receiver design for terahertz integrated sensing and communication systems,” IEEE Trans. Commun., vol. 71, no. 1, pp. 595-610, Jan. 2023.
- P. Kumari, S. A. Vorobyov and R. W. Heath, “Adaptive virtual waveform design for millimeter-wave joint communication-radar,” IEEE Trans. Signal Process, vol. 68, pp. 715-730, 2020.
- F. Liu, L. Zhou, C. Masouros, A. Li, W. Luo and A. Petropulu, “Toward dual-functional radar-communication systems: Optimal waveform design,” IEEE Trans. Signal Process, vol. 66, no. 16, pp. 4264-4279, Aug. 2018.
- Z. Cheng and B. Liao, “QoS-aware hybrid beamforming and DOA estimation in multi-carrier dual-function radar-communication systems,” IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1890-1905, June 2022.
- A. Ali, N. Gonzalez-Prelcic, R. W. Heath and A. Ghosh, “Leveraging sensing at the infrastructure for mmWave communication,” IEEE Commun. Mag., vol. 58, no. 7, pp. 84-89, July 2020.
- K. Dovelos, M. Matthaiou, H. Q. Ngo and B. Bellalta, “Channel estimation and hybrid combining for wideband terahertz massive MIMO systems,” IEEE J. Sel. Areas Commun., vol. 39, no. 6, pp. 1604-1620, June 2021.
- C. -K. Wen, W. -T. Shih and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 748-751, Oct. 2018.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.