STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Mobile Computing, Communications & Mobile Networks Original Research

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

  1. Heena T. Shaikh Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur
  2. IR. Kazi Kutubuddin Sayyad Liyakat Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur

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 extended reality (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 radiofrequency (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 received signal strength indicator (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

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