Quantum-PROBE: Rydberg Atomic Receiver-Based Multi-AoA Estimation with RF Lens

TL;DR

Quantum-PROBE introduces a novel Rydberg atomic receiver framework with RF lens for multi-user AoA estimation, leveraging a physics-based model and sparse recovery algorithms to outperform benchmarks in accuracy and complexity.

Contribution

The paper develops a physics-consistent model and two AoA recovery strategies for Rydberg atomic receivers with RF lenses, enabling multi-user AoA estimation without phase information.

Findings

Quantum-PROBE outperforms benchmarks in accuracy.

The NN-LASSO method achieves higher accuracy.

The SIC algorithm offers lower complexity.

Abstract

This paper presents the Quantum-Power pROfile Based Estimation (PROBE) framework, a Rydberg Atomic Receiver (RARE)-based multi-user angle-of-arrival (AoA) estimation approach equipped with a radio-frequency (RF) lens front end. We establish a physics-consistent analytical model showing that magnitude-only RARE measurements, processed via the beam-propagation method (BPM) and snapshot-wise power accumulation, can be rigorously characterized as a nonnegative superposition of AoA-dependent, lens-induced spatial power profiles. This formulation reveals a structured and interpretable power-domain dictionary that enables multi-user AoA recovery without explicit phase reconstruction. Building on this foundation, we develop two complementary recovery strategies: (i) a principled non-negative least absolute shrinkage and selection operator (NN-LASSO)-based solver that estimates a sparse nonnegative angular representation via an accelerated proximal-gradient method followed by cluster-based AoA decoding, and (ii) a low-complexity successive interference cancellation (SIC) algorithm that iteratively identifies and removes dominant power-profile components through cosine-similarity matching. Simulation results demonstrate that the proposed Quantum-PROBE framework consistently outperforms representative RARE- and RF-based benchmarks across diverse system configurations, while offering a clear accuracy-complexity tradeoff between the NN-LASSO and SIC variants for practical quantum sensing deployments.