6G and the Post‐Shannon Theory

Cabrera, Juan A.; Boche, Holger; Deppe, Christian; Schaefer, Rafael F.; Scheunert, Christian; Fitzek, Frank H. P.

doi:10.1002/9781119765554.ch16

Cited by 31 publications

(16 citation statements)

References 53 publications

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“…Various applications of MC within the framework of sixth generation wireless networks (6G) [16], [17] are associated with event-triggered systems, where Shannon's message transmission capacity, as considered in [7], [8], [12]- [15], may not be the appropriate performance metric. In particular, in eventdetection scenarios, where the receiver wishes to decides about the occurrence of a specific event in terms of a reliable Yes / No answer, the so-called identification capacity is the relevant performance measure [18].…”

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confidence: 99%

“…Specific examples of the identification problem in the context of MC can be found in targeted drug delivery [19], [20] and cancer treatment [21]- [23], where, e.g., a nano-device's objective may be to identify whether or not a specific cancer biomarker exists around the target tissue; in health monitoring [24], [25] where, e.g., one may be interested in whether or not the pH value of the blood exceeds a critical threshold; in natural pheromone communications [26], [27] where, e.g., a male insect searches for sex pheromones indicating the presence of a nearby female insect; etc. For these tasks, the identification capacity is deemed to be a key quantitative measure [16], [17]. Motivated by this discussion, this paper focuses on the problem of identification in the context of MC systems.…”

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confidence: 99%

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Deterministic Identification for Molecular Communications over the Poisson Channel

Javad¹,

Pereg²,

Boche³

et al. 2022

Preprint

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Various applications of molecular communications (MC) are event-triggered, and, as a consequence, the prevalent Shannon capacity may not be the right measure for performance assessment. Thus, in this paper, we motivate and establish the identification capacity as an alternative metric. In particular, we study deterministic identification (DI) for the discrete-time Poisson channel (DTPC), subject to an average and a peak power constraint, which serves as a model for MC systems employing molecule counting receivers. It is established that the codebook size for this channel scales as 2 (n log n)R , where n and R are the codeword length and coding rate, respectively. Lower and upper bounds on the DI capacity of the DTPC are developed. The obtained large capacity of the DI channel sheds light on the performance of natural DI systems such as natural olfaction, which are known for their extremely large chemical discriminatory power in biology. Furthermore, numerical simulations for the empirical miss-identification and false identification error rates are provided for finite length codes. This allows us to quantify the scale of error reduction in terms of the codeword length.

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confidence: 99%

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confidence: 99%

Deterministic Identification for Molecular Communications over the Poisson Channel

Javad¹,

Pereg²,

Boche³

et al. 2022

Preprint

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“…These applications include machine-to-machine and human-to-machine systems [6], digital watermarking [7], [8], [9], industry 4.0 [10] and 6G communication systems [11], Large 6G research projects [12] [13] are studying the problem of CR generation for future communication networks. This is because it is expected that CR will be a highly relevant resource for future communication systems [11] [14], on the basis of which, the resilience requirements [11] and security requirements [15] can be met. The aforementioned requirements are crucial for achieving trustworthiness.…”

Section: Introductionmentioning

confidence: 99%

Common Randomness Generation from Gaussian Sources

Labidi

Ezzine

Deppe

et al. 2022

2022 IEEE International Symposium on Information Theory (ISIT)

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We study a standard two-source model for common randomness (CR) generation in which Alice and Bob generate a common random variable with high probability of agreement by observing independent and identically distributed (i.i.d.) samples of correlated sources on countably infinite alphabet. The two parties are additionally allowed to communicate as little as possible over a noisy memoryless channel. In our work, we give a single-letter formula for the CR capacity for the proposed model and provide a rigorous proof of it. This is a challenging scenario because some of the finite alphabet properties, namely of the entropy can not be extended to the countably infinite case. Notably, it is known that the Shannon entropy is in fact discontinuous at all probability distributions with countably infinite support.

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“…Research on micro-scale molecular technology, such as intra-body networks, is still in its early stages and faces many challenges. Nonetheless, MC is a promising contender for future applications, such as the sixth generation of cellular communication (6G) [6], [7], and nanomedical applications including cancer treatment [8]- [10] and targeted drug delivery [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Deterministic Identification Over Poisson Channels

Salariseddigh¹,

Pereg²,

Boche³

et al. 2021

Preprint

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Deterministic identification (DI) for the discrete-time Poisson channel, subject to an average and a peak power constraint, is considered. It is established that the code size scales as 2 (n log n)R , where n and R are the block length and coding rate, respectively. The authors have recently shown a similar property for Gaussian channels [1]. Lower and upper bounds on the DI capacity of the Poisson channel are developed in this scale. Those imply that the DI capacity is infinite in the exponential scale, regardless of the dark current, i.e., the channel noise parameter.

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6G and the Post‐Shannon Theory

Cited by 31 publications

References 53 publications

Deterministic Identification for Molecular Communications over the Poisson Channel

Deterministic Identification for Molecular Communications over the Poisson Channel

Common Randomness Generation from Gaussian Sources

Deterministic Identification Over Poisson Channels

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