论文简报
cs.IT 2605.26549v1 值得读

Joint Localization and Orientation with Triple-Beam Fingerprints in Massive MIMO-OFDM

Yu Zhao, Zhenzhou Jin, Jinke Tang, Li You, Chen Sun, Xiang-Gen Xia, Xiqi Gao

发布日期：2026-05-26 05:00 相关性：1.0000 价值：0.7400 分类：cs.IT eess.SP
arXiv 摘要 PDF 原文 网页正文

摘要

With the widespread application of location-based services, fingerprint-based localization has demonstrated advantages in environments with complex signal propagation. Deep learning has significantly improved the efficiency of both offline training and online matching in localization processes. However, existing fingerprints only contain terminal position information without capturing motion states, and neural network designs have not fully incorporated structural features such as fingerprint sparsity. In this paper, we propose a triple-beam fingerprint (TBF) incorporating Doppler information and design a Transformer-based localization and orientation awareness network (LOA-Net) to simultaneously estimate user position and motion direction in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first show the correlation between TBF and multipath information, and investigate the collinearity of different TBFs, demonstrating that TBF is an effective small-size sparse fingerprint. Then, we propose LOA-Net containing a mask-augmented detection Transformer for regression (MaskDETR-Reg) module and a fusion-enhanced Transformer for direction classification (Fusion-TDC) module to process angle-delay domain information and Doppler domain information, respectively. Finally, in the simulation of indoor scenarios defined in 3GPP 38.901, the proposed method achieves significantly better localization accuracy than weighted $K$-nearest neighbors (WKNN), 2D and 3D convolutional neural networks (CNNs), and achieves satisfactory motion direction estimation accuracy.

相关性判断

high
相关方向
wireless_communications massive_mimo localization channel_fingerprinting ofdm
判断依据

Massive MIMO-OFDM localization/orientation using channel fingerprints and Doppler is squarely in wireless communications and adjacent information theory topics.

价值判断

High relevance to cs.IT wireless localization, massive MIMO, OFDM, and channel fingerprinting. Clear technical contribution: Doppler-augmented triple-beam fingerprints plus Transformer-based joint position and orientation estimation. Structure analysis indicates theorem/discriminability and collinearity analysis, not just empirical model fitting. Evaluation shows gains over WKNN and CNN baselines in a standard 3GPP-style indoor simulation setting.

核心问题与主要方法

核心问题

joint user localization and motion-direction estimation from wireless fingerprints in massive MIMO-OFDM

场景：single-cell TDD massive MIMO-OFDM with UPA BS, indoor NLOS fingerprinting, and triple-beam angle-delay-Doppler tensor features

主要方法

Transforms the space-frequency-time channel representation into a triple-beam angle-delay-Doppler domain to obtain a compact sparse channel power tensor fingerprint. Uses asymptotic energy-concentration analysis to connect nonzero TBF entries with multipath DOA, TOA, Doppler frequency, and path power. Uses collinearity analysis to argue that reduced-size TBFs preserve localization discriminability comparable to SFTFs. Aggregates the Doppler dimension to form angle-delay input for MaskDETR-Reg and constructs a threshold mask to focus attention on informative sparse regions. Uses estimated coordinates from MaskDETR-Reg as prior information for Fusion-TDC, which classifies Doppler-domain signatures into 16 motion-direction bins.

关键贡献与后续阅读

关键贡献

Introduces a Doppler-augmented triple-beam fingerprint for massive MIMO-OFDM that encodes angle, delay, and Doppler-domain multipath structure in a compact sparse tensor. Provides theorem-backed analysis that TBF path energy concentrates in physically meaningful TB-domain bins and that TBF has asymptotically equivalent collinearity to SFTF. Designs LOA-Net as a two-part Transformer pipeline: MaskDETR-Reg for 3D coordinate regression from angle-delay features and Fusion-TDC for motion-direction classification from Doppler features plus coordinate prior. Demonstrates in 3GPP 38.901-style indoor NLOS simulation that the DETR-based localization modules outperform WKNN and CNN baselines, with DE-MaskDETR achieving the best reported mean localization error.

研究启发

How sensitive is LOA-Net to realistic channel-estimation errors beyond the added AWGN/noise model? Does TBF remain discriminative under hardware impairments, synchronization offsets, or changing indoor layouts? How does orientation accuracy change when user speed is unknown rather than fixed? Are the gains over CNNs due mainly to the Transformer architecture, the mask construction, or the TBF representation itself?

限制与不确定性

Evidence is simulation-only, with no field measurements reported. Main formulation assumes perfect CSI, which may overstate practical performance. Orientation task appears simplified to fixed speed and 16 direction classes. Deep review urgency is moderated because the contribution seems application-specific rather than broadly foundational.

参考文献

51 条
  1. [1] Y. Zhao, Z. Jin, J. Tang, L. You, C. Sun, X.-G. Xia, and X. Gao, “Joint localization and orientation assisted by massive MIMO-OFDM triple-beam fingerprints,” in Proc. IEEE Wireless Commun. Netw. Conf. Workshops (WCNC Workshops) , Kuala Lumpur, Malaysia, Apr. 2026, pp. 1–6.
  2. [2] L. You, X. Qiang, Y. Zhu, F. Jiang, C. G. Tsinos, W. Wang, H. Wymeersch, X. Gao, and B. Ottersten, “Integrated communications and localization for massive MIMO LEO satellite systems,” IEEE Trans. Wireless Commun. , vol. 23, no. 9, pp. 11 061–11 075, Sep. 2024.
  3. [3] F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and technologies,” IEEE Commun. Surv. Tutorials , vol. 21, no. 3, pp. 2568–2599, 3rd Quart. 2019.
  4. [4] Y. Zhu, L. You, H. Zhou, Z. Jin, Q. Kong, and X. Gao, “Robust precoding for massive MIMO LEO satellite integrated communication and localization systems,” IEEE Commun. Lett. , vol. 29, no. 1, pp. 21–25, Jan. 2025.
  5. [5] J. Singh, A. Gupta, A. K. Jagannatham, and L. Hanzo, “Multi-beam object-localization for millimeter-wave ISAC-aided connected autonomous vehicles,” IEEE Trans. Veh. Technol. , vol. 74, no. 1, pp. 1725–1729, Jan. 2025.
  6. [6] Z. Jin, L. You, H. Zhou, Y. Wang, X. Liu, X. Gong, X. Gao, D. W. K. Ng, and X.-G. Xia, “GDM4MMIMO: Generative diffusion models for massive MIMO communications,” IEEE Commun. Mag. , vol. 64, no. 4, pp. 50–56, Apr. 2026.
  7. [7] Z. Jin, L. You, D. Wing Kwan Ng, X.-G. Xia, and X. Gao, “Near-field channel estimation for XL-MIMO: A deep generative model guided by side information,” IEEE Trans. Cognit. Commun. Networking , vol. 12, pp. 628–643, Dec. 2025.
  8. [8] C. De Lima, D. Belot, R. Berkvens, A. Bourdoux, D. Dardari, M. Guillaud, M. Isomursu, E.-S. Lohan, Y. Miao, A. N. Barreto, M. R. K. Aziz, J. Saloranta, T. Sanguanpuak, H. Sarieddeen, G. Seco-Granados, J. Suutala, T. Svensson, M. Valkama, B. Van Liempd, and H. Wymeersch, “Convergent communication, sensing and localization in 6G systems: An overview of technologies, opportunities and challenges,” IEEE Access , vol. 9, pp. 26 902–26 925, Feb. 2021.
  9. [9] Y. Ye, L. You, H. Xu, A. Elzanaty, K.-K. Wong, and X. Gao, “SCNR maximization for MIMO ISAC assisted by fluid antenna system,” IEEE Trans. Veh. Technol. , vol. 74, no. 8, pp. 13 272–13 277, Aug. 2025.
  10. [10] F. Guidi and D. Dardari, “Radio positioning with EM processing of the spherical wavefront,” IEEE Trans. Wireless Commun. , vol. 20, no. 6, pp. 3571–3586, Jun. 2021.
  11. [11] Y. Ye, L. You, J. Wang, H. Xu, K.-K. Wong, and X. Gao, “Fluid antenna-assisted MIMO transmission exploiting statistical CSI,” IEEE Commun. Lett. , vol. 28, no. 1, pp. 223–227, Jan. 2024.
  12. [12] J. Talvitie, M. Renfors, and E. S. Lohan, “Distance-based interpolation and extrapolation methods for RSS-based localization with indoor wireless signals,” IEEE Trans. Veh. Technol. , vol. 64, no. 4, pp. 1340–1353, Apr. 2015.
  13. [13] M. Zhou, Y. Li, M. J. Tahir, X. Geng, Y. Wang, and W. He, “Integrated statistical test of signal distributions and access point contributions for Wi-Fi indoor localization,” IEEE Trans. Veh. Technol. , vol. 70, no. 5, pp. 5057–5070, May 2021.
  14. [14] S. H. Fang, T. N. Lin, and K. C. Lee, “A novel algorithm for multipath fingerprinting in indoor WLAN environments,” IEEE Trans. Wireless Commun. , vol. 7, no. 9, pp. 3579–3588, Sep. 2008.
  15. [15] H. Wu, L. Liang, X. Mei, and Y. Zhang, “A convex optimization approach for NLOS error mitigation in TOA-based localization,” IEEE Signal Process Lett. , vol. 29, pp. 677–681, Jan. 2022.
  16. [16] K. Tong, X. Wang, A. Khabbazibasmenj, and A. Dounavis, “Optimum reference node deployment for TOA-based localization,” in Proc. IEEE Int. Conf. Commun. (ICC) , London, United kingdom, 2015, pp. 3252–3256.
  17. [17] C. Xu, J. He, X. Zhang, P.-H. Tseng, and S. Duan, “Toward near-ground localization: Modeling and applications for TOA ranging error,” IEEE Trans. Antennas Propag. , vol. 65, no. 10, pp. 5658–5662, Oct. 2017.
  18. [18] S. Li, M. Hedley, I. B. Collings, and D. Humphrey, “TDOA-based localization for semi-static targets in NLOS environments,” IEEE Wireless Commun. Lett. , vol. 4, no. 5, pp. 513–516, Oct. 2015.
  19. [19] L. Qiao, A. Liao, Z. Li, H. Wang, Z. Gao, X. Gao, Y. Su, P. Xiao, L. You, and D. W. K. Ng, “Sensing user’s activity, channel, and location with near-field extra-large-scale MIMO,” IEEE Trans. Commun. , vol. 72, no. 2, pp. 890–906, Feb. 2024.
  20. [20] Y. Wang and K. C. Ho, “Unified near-field and far-field localization for AOA and hybrid AOA-TDOA positionings,” IEEE Trans. Wireless Commun. , vol. 17, no. 2, pp. 1242–1254, Feb. 2018.
  21. [21] Y. Zheng, M. Sheng, J. Liu, and J. Li, “Exploiting AoA estimation accuracy for indoor localization: A weighted AoA-based approach,” IEEE Wireless Commun. Lett. , vol. 8, no. 1, pp. 65–68, Feb. 2019.
  22. [22] X. Gong, X. Yu, X. Liu, and X. Gao, “Machine learning-based fingerprint positioning for massive MIMO systems,” IEEE Access , vol. 10, pp. 89 320–89 330, Aug. 2022.
  23. [23] M. Luckner, S. Sowik, and P. Brida, “Selection of signal sources influence at indoor positioning system,” IEEE Trans. Wireless Commun. , vol. 23, no. 1, pp. 45–57, Jan. 2024.
  24. [24] X. Gong, A. Lu, X. Liu, X. Fu, X. Gao, and X.-G. Xia, “Deep learning based fingerprint positioning for multi-cell massive MIMO-OFDM systems,” IEEE Trans. Veh. Technol. , vol. 73, no. 3, pp. 3832–3849, Mar. 2024.
  25. [25] Z. Jin, L. You, X.-G. Xia, and X. Gao, “EnvCDiff: Joint refinement of environmental information and channel fingerprints via conditional generative diffusion model,” IEEE Trans. Veh. Technol. , vol. 75, no. 4, pp. 6846–6851, Apr. 2025.
  26. [26] L. You, X. Gao, A. L. Swindlehurst, and W. Zhong, “Channel acquisition for massive MIMO-OFDM with adjustable phase shift pilots,” IEEE Trans. Signal Process. , vol. 64, no. 6, pp. 1461–1476, Mar. 2016.
  27. [27] J. Tang, L. You, X. Gong, C. Xie, X. Gao, X.-G. Xia, and X. Shi, “Statistical CSI acquisition for multi-frequency massive MIMO systems,” IEEE Trans. Commun. , vol. 73, no. 11, pp. 11 798–11 813, Nov. 2025.
  28. [28] T. Chen, W. Wang, R. Ding, G. Seco-Granados, L. You, and X. Gao, “Precoding design for joint synchronization and positioning in 5G integrated satellite communications,” in Proc. IEEE Global Commun. Conf. (GLOBECOM) , Madrid, Spain, 2021, pp. 01–06.
  29. [29] L. You, K.-X. Li, J. Wang, X. Gao, X.-G. Xia, and B. Ottersten, “Massive MIMO transmission for LEO satellite communications,” IEEE J. Sel. Areas Commun. , vol. 38, no. 8, pp. 1851–1865, Aug. 2020.
  30. [30] Z. Jin, L. You, X. Li, Z. Gao, Y. Liu, X.-G. Xia, and X. Gao, “Channel fingerprint construction for massive MIMO: A deep conditional generative approach,” IEEE Trans. Wireless Commun. , vol. 25, pp. 6096–6113, Dec. 2025.

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