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arXiv:2411.11604 · IEEE World Forum on Internet of Things (WF-IoT)

Joint communication and sensing over the lossy bosonic quantum channel

Pere Munar-Vallespir, Janis Nötzel

Published Nov 10, 2024• 1 citation
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Abstract

We study the problem of joint communication and sensing for data transmission systems using optimal quantum instruments in order to transmit data and, at the same time, estimate environmental parameters. In particular we consider the specific but at the same time generic case of a noiseless bosonic classical-quantum channel where part of the transmitted light is reflected back to the transmitter. While sending messages to the receiver, the transmitter tries at the same time to estimate the reflectivity of the channel. Extending earlier results on similar but finite-dimensional systems, we are able to characterize optimal tradeoffs between communication and detection rates. We also compare quantum performance to analogous classical models, quantifying the quantum advantage.

DOI
10.1109/wf-iot62078.2024.10811180

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We study the problem of joint communication and sensing for data transmission systems using optimal quantum instruments in order to transmit data and, at the same time, estimate environmental parameters. In particular we consider the specific but at the same time generic case of a noiseless bosonic classical-quantum channel where part of the transmitted light is reflected back to the transmitter. While sending messages to the receiver, the transmitter tries at the same time to estimate the reflectivity of the channel. Extending earlier results on similar but finite-dimensional systems, we are able to characterize optimal tradeoffs between communication and detection rates. We also compare quantum performance to analogous classical models, quantifying the quantum advantage.
To capture the problem of joint communication and sensing in the quantum regime, we consider the problem of reliably communicating over a Classical-Quantum (c-q) channel that depends on a random parameter while simultaneously estimating the random parameter at the transmitter through a noisy feedback channel. Specifically, for non-adaptive estimation strategies, we obtain an exact characterization of the optimal tradeoffs between the rate of communication and the error exponent of parameter estimation. As in the classical setting, the tradeoff is governed by the empirical distribution of the codewords, which simultaneously controls the rate of reliable communication and the error exponent.
We consider a joint communication and sensing problem over an optical link in which a low-power transmitter simultaneously communicates with a receiver and identifies the range of a defect producing a backscattered signal. We model the system as a lossy thermal-noise bosonic channel, in which the target location, modeled as a beamsplitter, affects the timing of the backscattered signal. Motivated by the envisioned deployment of entanglement-enabled quantum networks, we allow the transmitter to exploit shared entanglement to assist both sensing and communication. Since entanglement is known to enhance sensing, as demonstrated in Quantum Illumination (QI), and to increase communication rates through entanglement-assisted communication, the transmitter faces a trade-off in allocating its entanglement resources between the two tasks. Our main result is a characterization of these trade-offs in the form of an achievable rate/error-exponent region, which can outperform time-sharing and demonstrates a quantum advantage.
Preventing signal detection in communication and active sensing requires careful control of transmission power. In fact, the square-root laws (SRL) for covert classical and quantum communication and sensing prescribe that the average output energy per channel use scales as 1/n1/\sqrt{n}1/n​ for nnn channel uses. \emph{Diffuse} and \emph{sparse} signaling achieve this. The former transmits signals whose energy decays as 1/n1/\sqrt{n}1/n​ over all nnn channel uses, which is convenient for mathematical analysis. The latter transmits constant-energy signals only approximately ∝n\propto\sqrt{n}∝n​ times out of nnn channel uses, remaining silent on the others. This offers significant practical advantages in compatibility with modern digital transmitters. Here, we study sparse signaling over the lossy thermal-noise bosonic channel, which is a quantum model of many practical channels (including optical, microwave, and radio-frequency). We characterize the input signal state that minimizes detectability. We find an unintuitive optimal quantum state structure: a mixture of just two consecutive photon-number states. In particular, in the low-brightness regime, the optimal signal state is a mixture of vacuum and a single photon. Since these states are generally suboptimal for both communication and active sensing, we explore the resulting trade-off and identify input-power thresholds for transitions between optimizing covertness and performance in communication and sensing tasks.
We study the problem of joint communication and detection of wiretapping on an optical fiber from a quantum perspective. Our system model describes a communication link that is capable of transmitting data under normal operating conditions and raising a warning at the transmitter side in case of eavesdropping. It contributes to a unified modelling approach, based on which established quantum communication paradigms like quantum key distribution can be compared to other approaches offering similar functionality.
This work considers mitigation of information leakage between communication and sensing operations in joint communication and sensing systems. Specifically, a discrete memoryless state-dependent broadcast channel model is studied in which (i) the presence of feedback enables a transmitter to simultaneously achieve reliable communication and channel state estimation; (ii) one of the receivers is treated as an eavesdropper whose state should be estimated but which should remain oblivious to a part of the transmitted information. The model abstracts the challenges behind security for joint communication and sensing if one views the channel state as a characteristic of the receiver, e.g., its location. For independent and identically distributed (i.i.d.) states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed. The characterization is exact when the broadcast channel is either physically-degraded or reversely-physically-degraded. The characterization is also extended to the situation in which the entire transmitted message should be kept secret. The benefits of a joint approach compared to separation-based secure communication and state-sensing methods are illustrated with a binary joint communication and sensing model.
This work considers the problem of mitigating information leakage between communication and sensing in systems jointly performing both operations. Specifically, a discrete memoryless state-dependent broadcast channel model is studied in which (i) the presence of feedback enables a transmitter to convey information, while simultaneously performing channel state estimation; one of the receivers is treated as an eavesdropper whose state should be estimated but which should remain oblivious to part of the transmitted information. The model abstracts the challenges behind security for joint communication and sensing if one views the channel state as a key attribute, e.g., location. For independent and identically distributed states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed. The characterization is exact when the broadcast channel is either physically-degraded or reversely-physically-degraded. The partial characterization is also extended to the situation in which the entire transmitted message should be kept secret. The benefits of a joint approach compared to separation-based secure communication and state-sensing methods are illustrated with binary joint communication and sensing models.