RIS-Enabled Self-Localization: Leveraging Controllable Reflections With Zero Access Points

Abstract

Reconfigurable intelligent surfaces (RISs) are one of the most promising technological enablers of the next (6th) generation of wireless systems. In this paper, we introduce a novel use-case of the RIS technology in radio localization, which is enabling the user to estimate its own position via transmitting orthogonal frequency-division multiplexing (OFDM) pilots and processing the signal reflected from the RIS. We demonstrate that user localization in this scenario is possible by deriving Cramér-Rao lower bounds on the positioning error and devising a lowcomplexity position estimation algorithm. We consider random and directional RIS phase profiles and apply a specific temporal coding to them, such that the reflected signal from the RIS can be separated from the uncontrolled multipath. Finally, we assess the performance of our position estimator for an example system, and show that the proposed algorithm can attain the derived bound at high signal-to-noise ratio values.

Leveraging RIS-Enabled Smart Signal Propagation for Solving Infeasible Localization Problems: Scenarios, Key Research Directions, and Open Challenges

“…SISO with 1 RIS in the absence of a BS (Fig. 3 (d)): A single-antenna full-duplex UE can estimate its own location by transmitting WB pilots to the RIS and processing the reflected signals, thus (strictly) not requiring any BS (see [14]). In this scenario, we can measure the RTT and the AoD from the RIS to the UE.…”

Leveraging RIS-Enabled Smart Signal Propagation for Solving Infeasible Localization Problems

“…SISO with 1 RIS in the absence of a BS. A single-antenna full-duplex UE can estimate its own location by transmitting WB pilots to the RIS and processing the reflected signals, thus (strictly) not requiring any BS (see [13]). Indeed, in this scenario, we can measure the RTT and the AoD from the RIS to the UE.…”

Joint Localization and Beamforming for Reconfigurable Intelligent Surface Aided 5G mmWave Communication Systems

“…To sum up, each sub-algorithm of the proposed JLBO algorithm is convergent, and thus the overall JLBO algorithm is convergent according to [41]- [43].…”

“…In [42], power and bandwidth optimization for localization (by optimizing the CRLB) was studied, where the UE location is assumed to be known. To overcome the first challenge, a solution from a robust optimization method was used to optimize the radio resource for localization [43], [44]. Specifically, a worst-case CRLB with respect to (w.r.t.)…”