Common Randomness Generation and Identification over Gaussian Channels

Ezzine, Rami; Labidi, Wafa; Boche, Holger; Deppe, Christian

doi:10.1109/globecom42002.2020.9322460

Cited by 16 publications

(13 citation statements)

References 16 publications

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“…In contrast to the discrete case in [2], [14], note that the CR capacity can reach infinity [24]. If the I(X, Y ) = +∞, then the single-letter characterization in ( 13) can be reduced to the following form:…”

Section: B Definitions and Main Resultsmentioning

confidence: 99%

“…CR capacity refers to the maximum rate of CR that Alice and Bob can generate using the resources available in the model. Later, the results on CR capacity have been extended in [14] to point-to-point single-input single-output (SISO) and Multiple-Input Multiple-Output (MIMO) Gaussian channels for their practical relevance in many communication situations such as wired and wireless communications, satellite and deep space communication links, etc. The results on CR capacity over Gaussian channels have been used to establish a lower-bound on their corresponding correlation-assisted secure identification capacity in the log-log scale [14].…”

Section: Introductionmentioning

confidence: 99%

“…Later, the results on CR capacity have been extended in [14] to point-to-point single-input single-output (SISO) and Multiple-Input Multiple-Output (MIMO) Gaussian channels for their practical relevance in many communication situations such as wired and wireless communications, satellite and deep space communication links, etc. The results on CR capacity over Gaussian channels have been used to establish a lower-bound on their corresponding correlation-assisted secure identification capacity in the log-log scale [14]. This lower bound can already exceed the secure identification capacity over Gaussian channels with randomized encoding elaborated in [15].…”

Section: Introductionmentioning

confidence: 99%

“…Applications of our work include the problem of correlationassisted identification, where the sender and the receiver have access to a correlated Gaussian source. Indeed, analogously to the discrete case [14] and based on an early work in [19], one can construct identification codes for noisy memoryless channels based on the concatenation of two transmission codes using CR as a resource.…”

Section: Introductionmentioning

confidence: 99%

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Common Randomness Generation from Gaussian Sources

Labidi¹,

Ezzine²,

Deppe³

et al. 2022

Preprint

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We study the problem of common randomness (CR) generation in the basic two-party communication setting in which the sender and the receiver aim to agree on a common random variable with high probability by observing independent and identically distributed (i.i.d.) samples of correlated Gaussian sources and while communicating as little as possible over a noisy memoryless channel. We completely solve the problem by giving a single-letter characterization of the CR capacity for the proposed model and by providing a rigorous proof of it. Interestingly, we prove that the CR capacity is infinite when the Gaussian sources are perfectly correlated.

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Section: B Definitions and Main Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Common Randomness Generation from Gaussian Sources

Labidi¹,

Ezzine²,

Deppe³

et al. 2022

Preprint

Self Cite

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“…In this setting, Eve is not required to obtain a public key, and she is viewed as a malicious party that tries to get a hold of Bob's secret key. On the other hand, taking R 1 = 0, we distill a public key without eavesdropping, which can be viewed as randomness concentration [4,93]. Nonetheless, we require the public communication to be independent of the distilled key.…”

Section: Conference Key Agreementmentioning

confidence: 99%

Key Assistance, Key Agreement, and Layered Secrecy for Bosonic Broadcast Channels

Pereg¹,

Ferrara²,

Bloch³

2021

Preprint

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Secret-sharing building blocks based on quantum broadcast communication are studied. The confidential capacity region of the pure-loss bosonic broadcast channel is determined, both with and without key assistance, and an achievable region is established for the lossy bosonic broadcast channel. If the main receiver has a transmissivity of η < 1 2 , then confidentiality solely relies on the key-assisted encryption of the one-time pad. We also address conference key agreement for the distillation of two keys, a public key and a secret key. A regularized formula is derived for the key-agreement capacity region in finite dimensions. In the bosonic case, the key-agreement region is included within the capacity region of the corresponding broadcast channel with confidential messages. We then consider a network with layered secrecy, where three users with different security ranks communicate over the same broadcast network. We derive an achievable layered-secrecy region for a pure-loss bosonic channel that is formed by the concatenation of two beam splitters.

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