Near-field Anchor-free Localization using Reconfigurable Intelligent Surfaces

Near-field localization is expected to play a crucial role in enabling a plethora of applications under the paradigm of 6G networks. The conventional localization methods rely on complex infrastructure for providing cooperative anchor nodes that often contribute to higher network overload and energy consumption. To address this, the passive reconfigurable intelligent surfaces (RISs) can be leveraged as perfectly synced reference nodes for developing anchor-free localization. This work proposes a two-stage framework for localizing user equipment (UE) equipped with multiple antennas. At first, we show that the optimal RIS phase shift matrix maximizing the received signal-to-noise ratio (SNR) for RIS-assisted anchor-free localization is independent of UE location, making the proposed framework scalable without increasing the overhead to control RIS. The proposed two-stage framework first obtains a coarse estimate of UE's location by correlating the received RIS-reflected signal with a dictionary constructed using line-of-sight (LoS) components at a few reference positions. Next, the coarse estimate is refined using Newton's refinement. The numerical results show that the small-sized dictionary, constructed using fewer reference positions, can be employed for accurate localization with a slight increase in the required number of refinement iterations.