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Quantum Communication and Phase Sensing over a Dephasing Channel with Thermal Noise
arXiv math.IT0 views
arXiv:2611.07855v1 Announce Type: new Abstract: Dephasing is a prominent source of decoherence in quantum communication, degrading phase coherence and transmission reliability. Therefore, knowledge of the physical channel parameters for reliable transmission is imperative, motivating the integration of quantum communication and sensing at the physical layer. Consequently, we study the transmission of coherent states over a von Mises dephasing channel affected by thermal noise. We consider a heterodyne receiver, for which, we first derive the symbol-error probability (SEP) and an accurate closed-form approximation, through which an irreducible asymptotic error floor due to the dephasing channel is identified. We note that the derived SEP is affected by the mismatch between the receiver's mean phase estimation and the true mean phase, thereby formalizing the interplay between sensing accuracy and communication reliability. In addition, we derive the Fisher information (FI) for the mean phase acquisition of the channel and provide closed-form lower and upper FI bounds. Moreover, the quantum FI (QFI) of the channel is studied, and lower and upper bounds on the QFI are given, which become asymptotically tight as the photon number increases. Despite the initial complexity of the FI and the QFI, the bounds given are relatively simple and effective. In addition, by comparing the FI with the QFI, we show that heterodyne detection approaches the quantum sensing limit as the number of photons increase, while an unavoidable sensing floor exists due to dephasing. Then, using the SEP and the FI analysis, we discuss the conditions under which communication and phase estimation requirements are jointly feasible. Finally, numerical results validate the derived SEP, estimation bounds, and the sensing-communication feasibility tradeoff.
Source:https://arxiv.org/abs/2611.07855PreviousOver-the-Scene Computation: Illumination-Programmed Functional Sensing of Unresolvable PopulationsNextOperational Indirect Rate-Distortion Functions for Classification Tasks
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