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

Hong-Bae Jeon, Kaibin Huang, Chan-Byoung Chae

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.

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

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.
Paper Structure (21 sections, 66 equations, 12 figures, 2 tables, 2 algorithms)

This paper contains 21 sections, 66 equations, 12 figures, 2 tables, 2 algorithms.

Figures (12)

  • Figure 1: Illustration of the signal processing mechanism in an RARE. The impinging electromagnetic wave induces coupling between two highly excited Rydberg states (e.g., $53D_{3/2}$ and $54P_{3/2}$), leading to AT-splitting. The observed spectral separation $\Delta f$ is converted to the Rabi frequency $\Omega$ via \ref{['rabidef']}.
  • Figure 2: Illustration of the RARE-aided wireless system with an RF lens front-end.
  • Figure 3: Normalized field distribution of the incident electromagnetic wave with (a)-(b) $\theta=10^\circ$ and (c)-(d) $\theta=-8^\circ, 3^\circ$, and $10^\circ$ at the RF lens, computed via BPM. The dashed line indicates $f$ from the lens along the propagation axis, where the RARE array is deployed.
  • Figure 4: Normalized (a) $\mathbf p_{\rm MU}(\boldsymbol\theta)$ for $\boldsymbol\theta=[-8^\circ, 3^\circ, 10^\circ]^{\mathrm{T}}$, and (b) the corresponding $\mathbf p_{\rm Q}(\theta_i)\in\mathcal{D}_{\mathrm{Q}}$ for $i=1,2,3$, normalized by the maximum value over the dictionary, which together constitute the profile shown in (a). The profiles in Fig. \ref{['fig_aoa1']} are obtained by extracting the cross-section along the dashed line at the focal plane $f$ in Fig. \ref{['fig_bpm1']}.
  • Figure 5: Illustration of $\{\hat{w}_i\}$ which generates $\hat{\mathcal{S}}$ and $\{\hat{\mathcal{C}}_c\}_{c=1}^{N_c}$ when $\boldsymbol\theta=[-8^\circ,3^\circ,10^\circ]^{\mathrm{T}}$.
  • ...and 7 more figures