AI & ML
A Phase-Balanced Differential Rydberg-Atom Quantum Receiver for Weak GNSS Signal Detection
arXiv eess.SP0 views
arXiv:2611.09653v1 Announce Type: cross Abstract: This paper proposes a phase-balanced differential Rydberg atomic quantum receiver (DRAQR) for weak-signal recovery, using local-oscillator phase control to reinforce differential signal responses and a shared squeezed-reservoir model to correlate inter-cell quantum projection noise (QPN) and photon shot noise (PSN). An analytical model incorporating QPN, PSN, intrinsic thermal noise (ITN), and inter-cell correlations quantifies signal-to-noise ratio (SNR) gain, outage-constrained minimum detectable power, and bit-error rate (BER) for square $M$-QAM. For the evaluated settings, SNR gain reaches approximately $10$~dB as PSN correlation approaches unity, with BER decreasing as correlation increases. Under Rician fading, at a $0$-dB threshold SNR and maximum outage probability of $0.1$, the minimum detectable power decreases from $-146.95$~dBm at $K=1.155$ to $-159.98$~dBm at $K=2.4$. Analytical results are validated against Monte-Carlo simulations over $150{,}000$ data runs per point for receiver-performance plots.
Source:https://arxiv.org/abs/2611.09653PreviousA Tutorial on Practical QKD Post-Processing Algorithms Using a GUI-Based Simulation FrameworkNextLink-Level Performance Comparison of 5G Beamforming: Reciprocity-Based SVD vs. Codebook-Based Precoding
Related posts
What free AI training in Nepal can teach you before you pay for a class
0dev.to

Agents draft. People decide.
0dev.to UX

โค้ดเพิ่ม 400% แต่คุณไม่ได้เร็วขึ้น 400%: GitHub ชี้คอขวดใหม่อยู่ที่การรีวิว
0dev.to GitHub

Drago: Your GitHub Dragon
0dev.to GitHub
Case Study: Automating Tableau to Power BI Migration with Up to 90% Accuracy.
0dev.to Data
Vom Bluttest ins Molekül: Warum die Alamar/Nightingale-Studie räumliches Denken braucht...
0dev.to Data