A Technical Blueprint for AI-Driven Localization in 6G Mobile Networks

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Heena T. Shaikh,

  • IR. Kazi Kutubuddin Sayyad Liyakat,

  1. Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

The advent of sixth-generation (6G) wireless systems promises unprecedented spatial resolution, ultra- low latency, and pervasive connectivity, turning mobile localization from a peripheral service into a core enabler of immersive XR, autonomous logistics, and digital twins. Yet, the sheer scale of dense terahertz (THz) deployments, the stochastic nature of reconfigurable intelligent surfaces (RIS), and the dynamic interference landscape render traditional model-based positioning techniques inadequate. This work investigates how artificial intelligence (AI) can bridge the gap between raw radio-frequency (RF) observations and centimeter-level geolocation in real-time 6G networks. We propose a hierarchical AI stack that (i) fuses multi-modal sensor streams (channel state information, angle-of- arrival, time-of-flight, and sidelink RSSI) using a graph neural network (GNN) encoder, (ii) learns environment-aware propagation priors through a physics-informed transformer, and (iii) refines coarse estimates with a meta-reinforcement learner that continuously adapts to mobility patterns and RIS configurations. Extensive system-level simulations—covering urban micro-cells, vehicular corridors, and indoor factories—demonstrate that the proposed stack attains a median localization error of 7 cm under sub-millisecond latency, surpassing the best-in-class Kalman-filter and compressive-sensing baselines by 45%. Moreover, the framework exhibits graceful degradation in the presence of hardware impairments and partial RIS failures, highlighting AI’s robustness to the nonidealities endemic to 6G deployments. The results substantiate AI-driven localization as a decisive pillar for the next wave of context-aware mobile services.

Keywords: 6G, Artificial intelligence, communication, localization, mmwave, mobile networks

How to cite this article:
Heena T. Shaikh, IR. Kazi Kutubuddin Sayyad Liyakat. A Technical Blueprint for AI-Driven Localization in 6G Mobile Networks. Journal of Mobile Computing, Communications & Mobile Networks. 2026; 13(01):-.
How to cite this URL:
Heena T. Shaikh, IR. Kazi Kutubuddin Sayyad Liyakat. A Technical Blueprint for AI-Driven Localization in 6G Mobile Networks. Journal of Mobile Computing, Communications & Mobile Networks. 2026; 13(01):-. Available from: https://journals.stmjournals.com/jomccmn/article=2026/view=240862


References

  1. Wang X, Wang X, Mao S. Deep convolutional neural networks for indoor localization with CSI images. IEEE Transactions on Network Science and Engineering. 2018 Sep 19;7(1):316-27.
  2. Eskandari M, Savkin AV. SLAPS: Simultaneous localization and phase shift for a RIS-equipped UAV in 5G/6G wireless communication networks. IEEE Transactions on Intelligent Vehicles. 2023 Jul 25;8(12):4722-33.
  3. Zhou W, Zhang R, Chen G, Wu W. Integrated sensing and communication waveform design: A survey. IEEE Open Journal of the Communications Society. 2022 Oct 19;3:1930-49.
  4. Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Poor HV. Federated learning for internet of things: A comprehensive survey. IEEE communications surveys & tutorials. 2021 Apr 26;23(3):1622-58.
  5. Liu Y, Jiang Z, Zhang S, Xu S. Deep reinforcement learning-based beam tracking for low-latency services in vehicular networks. InICC 2020-2020 IEEE International Conference on Communications (ICC) 2020 Jun 7 (pp. 1-7). IEEE.
  6. Ruan Y, Chen L, Zhou X, Guo G, Chen R. Hi-Loc: Hybrid indoor localization via enhanced 5G NR CSI. IEEE Transactions on Instrumentation and Measurement. 2022 Aug 11;71:1-5.
  7. Hong JX, Zhang HB, Liu JH, Lei Q, Yang LJ, Du JX. A transformer-based multi-modal fusion network for 6D pose estimation. Information Fusion. 2024 May 1;105:102227.
  8. Hu J, Cao Y, Wu M, Yang F, Yu Z, Wang W, Plumbley MD, Yang J. Meta-SELD: Meta-learning for fast adaptation to the new environment in sound event localization and detection. arXiv preprint arXiv:2308.08847. 2023 Aug 17.
  9. Or B, Klein I. A hybrid model and learning-based adaptive navigation filter. IEEE Transactions on Instrumentation and Measurement. 2022 Aug 16;71:1-1.
  10. Sabovic A, Fontaine J, De Poorter E, Famaey J. Energy-aware tinyML model selection on zero energy devices. Internet of Things. 2025 Mar 1;30:101488.
  11. Alqasi MA, Alkelanie YA, Alnagrat AJ. Intelligent infrastructure for urban transportation: The role of artificial intelligence in predictive maintenance. Brilliance: research of artificial intelligence. 2024 Nov 11;4(2):625-37.
  12. Chabira C, Shayea I, Nurzhaubayeva G, Aldasheva L, Yedilkhan D, Amanzholova S. AI-Driven Handover Management and Load Balancing Optimization in Ultra-Dense 5G/6G Cellular Networks. Technologies. 2025 Jul 1;13(7):276. Available from: https://matjournals.net/engineering/index.php/JoRFMCT/article/view/2825
Ahead of Print Subscription Original Research
Volume 13
01
Received 15/02/2026
Accepted 17/02/2026
Published 24/04/2026
Publication Time 68 Days
1

Login


My IP

PlumX Metrics