Near-field Enhanced Accuracy Tracking for 6G Networks (NEAT-6G)

Research Project, 2024 – 2026

As the technological world marches ahead, the advent of the sixth generation (6G) networks promises unprecedented advancements in communication capabilities. One of the core components of 6G systems will be the inclusion of various large array structures such as the extremely large antenna array, reconfigurable intelligent surface, and cell-free multiple-input multiple-output. Ttraditional positioning and tracking methodologies (which are based on far-field signal models) are ill-suited for these innovations. This is mainly caused by curved wavefront in the near-field models. Consequently, the conventional methods for positioning both passive objects (e.g. walls) and active users (e.g. phones) will be severely degraded when the far-field assumption does not hold. On the other hand, the radio signals optimized for positioning and sensing in the far-field are sub-optimal in the near-field regime. To address this gap, this research aims to develop novel positioning and tracking methods tailored explicitly for the near-field environment of these large array structures. To this end, three work packages (WP) are conducted. WP1 is to design optimal signal waveforms for positioning and sensing with large and/or sparse antenna arrays that can make full use of the near-field features. WP2 is to develop phase-coherent sensing and tracking methods for non-isotropic and extended targets. WP3 is to develop algorithms for carrier-phase based positioning and tracking of active targets, by using the near-field signals transmitted by them. This research intends to solve these challenges through the development and implementation of innovative algorithms and methodologies, such as compressive sensing, convex or non-convex optimization, and machine learning, establishing a dedicated framework for near-field positioning and tracking in 6G networks.