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In the identification (ID) scheme proposed by Ahlswede and Dueck, the receiver’s goal is simply to verify whether a specific message of interest was sent. Unlike Shannon’s transmission codes, which aim for message decoding, ID codes for a discrete memoryless channel (DMC) are far more efficient; their size grows doubly exponentially with the blocklength when randomized encoding is used. This indicates that when the receiver’s objective does not require decoding, the ID paradigm is significantly more efficient than traditional Shannon transmission in terms of both energy consumption and hardware complexity. Further benefits of ID schemes can be realized by leveraging additional resources such as feedback. In this work, we address the problem of joint ID and channel state estimation over a DMC with independent and identically distributed (i.i.d.) state sequences. State estimation functions as the sensing mechanism of the model. Specifically, the sender transmits an ID message over the DMC while simultaneously estimating the channel state through strictly causal observations of the channel output. Importantly, the random channel state is unknown to both the sender and the receiver. For this system model, we present a complete characterization of the ID capacity–distortion function.

Physical unclonable functions (PUFs) leverage inherent, nonclonable physical randomness to generate unique input-output pairs, serving as secure fingerprints for cryptographic protocols like authentication. Quantum PUFs (QPUFs) extend this concept by using quantum states as input-output pairs, offering advantages over classical PUFs, such as challenge reusability via public channels and eliminating the need for trusted parties due to the no-cloning theorem. Recent work introduced a generalized mathematical framework for QPUFs. It was shown that random unitary QPUFs cannot achieve existential unforgeability against quantum polynomial time (QPT) adversaries. Security was possible only with additional uniform randomness. To avoid the cost of external randomness, we propose a novel measurement-based scheme. Here, the randomness naturally arises from quantum measurements. Additionally, we introduce a second model where the QPUF functions as a nonunitary quantum channel, which guarantees existential unforgeability. These are the first models in the literature to demonstrate a high level of provable security. Finally, we show that the quantum phase estimation (QPE) protocol, applied to a Haar random unitary, serves as an approximate implementation of the second type of QPUF by approximating a von Neumann measurement on the unitary's eigenbasis.

This article discusses a formalization of aspects of Cyber-Sovereignty (CyS) for information and communication technology (ICT), linking them to technological trustworthiness and deriving an associated paradigm for hard- and software design. The upcoming 6G ICT standard is considered a keystone within modern society’s increasing interconnectedness and automatization, as it provides the necessary technological infrastructure for applications such as the Metaverse or large-scale digital twinning. Since emerging technological systems increasingly affect sensitive human goods, hard- and software manufacturers must consider a new dimension of societal and judicial constraints in the context of technological trustworthiness. This article aims to establish a formalized theory of specific aspects of CyS, providing a paradigm for hard- and software engineering in ICT. This paradigm is directly applicable in formal technology assessment and ensures that the relevant facets of CyS – specifically, the principle of Algorithmic Transparency (AgT) – are satisfied. The framework follows an axiomatic approach. Particularly, the formal basis of our theory consists of four fundamental assumptions about the general nature of physical problems and algorithmic implementations. This formal basis allows for drawing general conclusions on the relation between CyS and technological trustworthiness and entails a formal meta-thesis on AgT in digital computing.

Various applications of molecular communications (MCs) feature an alarm-prompt behavior for which the prevalent Shannon capacity may not be the appropriate performance metric. The identification capacity as an alternative measure for such systems has been motivated and established in the literature. In this paper, we study deterministic K-identification (DKI) for the discrete-time Poisson channel (DTPC) with inter-symbol interference (ISI), where the transmitter is restricted to an average and a peak molecule release rate constraint. Such a channel serves as a model for diffusive MC systems featuring long channel impulse responses and employing molecule-counting receivers. We derive lower and upper bounds on the DKI capacity of the DTPC with ISI when the size of the target message set $K$ and the number of ISI channel taps $L$ may grow with the codeword length $n$ . As a key finding, we establish that for deterministic encoding, assuming that $K$ and $L$ both grow sub-linearly in $n$ , i.e., $K = 2^{\kappa \log n}$ and $L = 2^{l \log n} $ with $\kappa + 4l \in [0,1)$ , where $\kappa \in [0,1)$ is the identification target rate and $l \in [0,1/4) $ is the ISI rate, then the number of different messages that can be reliably identified scales super-exponentially in $n$ , i.e., $\sim 2^{(n\log n)R}$ , where $R$ is the DKI coding rate. Moreover, since $l$ and $\kappa $ must fulfill $\kappa + 4l \in [0,1)$ , we show that optimizing $l$ (or equivalently the symbol rate) leads to an effective identification rate [bits/s] that scales sub-linearly with $n $ . This result is in contrast to the typical transmission rate [bits/s] which is independent of $n$ .

Numerous applications of the Internet of Things (IoT) feature an event recognition behavior where the established Shannon capacity is not authorized to be the central performance measure. Instead, the identification capacity for such systems is considered to be an alternative metric, and has been developed in the literature. In this paper, we develop deterministic K-identification (DKI) for the binary symmetric channel (BSC) with and without a Hamming weight constraint imposed on the codewords. This channel may be of use for IoT in the context of smart system technologies, where sophisticated communication models can be reduced to a BSC for the aim of studying basic information theoretical properties. We derive inner and outer bounds on the DKI capacity of the BSC when the size of the goal message set K may grow in the codeword length n. As a major observation, we find that, for deterministic encoding, assuming that K grows exponentially in n, i.e., K=2nκ, where κ is the identification goal rate, then the number of messages that can be accurately identified grows exponentially in n, i.e., 2nR, where R is the DKI coding rate. Furthermore, the established inner and outer bound regions reflects impact of the input constraint (Hamming weight) and the channel statistics, i.e., the cross-over probability.

This article compares two methods of algorithmically processing bandlimited time-continuous signals in light of the general problem of finding “suitable” representations of analog information on digital hardware. Albeit abstract, we argue that this problem is fundamental in digital twinning, a signal-processing paradigm the upcoming 6G communication-technology standard relies on heavily. Using computable analysis, we formalize a general framework of machine-readable descriptions for representing analytic objects on Turing machines. Subsequently, we apply this framework to sampling and interpolation theory, providing a thoroughly formalized method for digitally processing the information carried by bandlimited analog signals. We investigate discrete-time descriptions, which form the implicit quasi-standard in digital signal processing, and establish continuous-time descriptions that take the signal’s continuous-time behavior into account. Motivated by an exemplary application of digital twinning, we analyze a textbook model of digital communication systems accordingly. We show that technologically fundamental properties, such as a signal’s (Banach-space) norm, can be computed from continuous-time, but not from discrete-time descriptions of the signal. Given the high trustworthiness requirements within 6G, e.g., employed software must satisfy assessment criteria in a provable manner, we conclude that the problem of “trustworthy” digital representations of analog information is indeed essential to near-future information technology.

It is anticipated that future communication systems will involve the use of new technologies, requiring high-speed computations using large amounts of data, in order to take advantage of data-driven methods for improving services and providing reliability and other benefits [...]

We extend the problem of secure source coding by considering a remote source whose noisy measurements are correlated random variables used for secure source reconstruction. The main additions to the problem are as follows: (1) all terminals noncausally observe a noisy measurement of the remote source; (2) a private key is available to all legitimate terminals; (3) the public communication link between the encoder and decoder is rate-limited; and (4) the secrecy leakage to the eavesdropper is measured with respect to the encoder input, whereas the privacy leakage is measured with respect to the remote source. Exact rate regions are characterized for a lossy source coding problem with a private key, remote source, and decoder side information under security, privacy, communication, and distortion constraints. By replacing the distortion constraint with a reliability constraint, we obtain the exact rate region for the lossless case as well. Furthermore, the lossy rate region for scalar discrete-time Gaussian sources and measurement channels is established. An achievable lossy rate region that can be numerically computed is also provided for binary-input multiple additive discrete-time Gaussian noise measurement channels.

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges, and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper, we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will also describe applications of this sparse signal processing paradigm in Multiple Input Multiple Output random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize an important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.

The paradigm shift from an exclusive allocation of frequency bands, one for each system, to a shared use of frequencies comes along with the need of new concepts since interference will be an ubiquitous phenomenon. In this paper, we use the concept of arbitrarily varying channels to model the impact of unknown interference caused by coexisting wireless systems which operate on the same frequencies. Within this framework, capacity can be zero if pre-specified encoders and decoders are used. This necessitates the use of more sophisticated coordination schemes where the choice of encoders and decoders is additionally coordinated based on common randomness. As an application we study the arbitrarily varying bidirectional broadcast channel and derive the capacity regions for different coordination strategies. This problem is motivated by decode-and-forward bidirectional or two-way relaying, where a relay establishes a bidirectional communication between two other nodes while sharing the resources with other coexisting wireless networks.

We consider an authentication process that makes use of biometric data or the output of a physical unclonable function (PUF), respectively, from an information theoretical point of view. We analyse different definitions of achievability for the authentication model. For the secrecy of the key generated for authentication, these definitions differ in their requirements. In the first work on PUF based authentication, weak secrecy has been used and the corresponding capacity regions have been characterized. The disadvantages of weak secrecy are well known. The ultimate performance criteria for the key are perfect secrecy together with uniform distribution of the key. We derive the corresponding capacity region. We show that, for perfect secrecy and uniform distribution of the key, we can achieve the same rates as for weak secrecy together with a weaker requirement on the distribution of the key. In the classical works on PUF based authentication, it is assumed that the source statistics are known perfectly. This requirement is rarely met in applications. That is why the model is generalized to a compound model, taking into account source uncertainty. We also derive the capacity region for the compound model requiring perfect secrecy. Additionally, we consider results for secure storage using a biometric or PUF source that follow directly from the results for authentication. We also generalize known results for this problem by weakening the assumption concerning the distribution of the data that shall be stored. This allows us to combine source compression and secure storage.

Robust biometric authentication is studied from an information theoretic perspective. Compound sources are used to account for uncertainty in the knowledge of the source statistics and are further used to model certain attack classes. It is shown that authentication is robust against source uncertainty and a special class of attacks under the strong secrecy condition. A single-letter characterization of the privacy secrecy capacity region is derived for the generated and chosen secret key model. Furthermore, the question is studied whether small variations of the compound source lead to large losses of the privacy secrecy capacity region. It is shown that biometric authentication is robust in the sense that its privacy secrecy capacity region depends continuously on the compound source.

The wiretap channel models secure communication between two users in the presence of an eavesdropper who must be kept ignorant of transmitted messages. This communication scenario is studied for arbitrarily varying channels (AVCs), in which the legitimate users know only that the true channel realization comes from a pre-specified uncertainty set and that it varies from channel use to channel use in an arbitrary and unknown manner. This concept not only captures the case of channel uncertainty, but also models scenarios in which malevolent adversaries influence or jam the transmission of the legitimate users. For secure communication over orthogonal arbitrarily varying wiretap channels (AVWCs) it has been shown that the phenomenon of super-activation occurs; that is, there are orthogonal AVWCs, each having zero secrecy capacity, which allow for transmission with positive rate if they are used together. It is shown that for such orthogonal AVWCs super-activation is generic in the sense that whenever super-activation is possible, it is possible for all AVWCs in a certain neighborhood as well. As a consequence, a super-activated AVWC is robust and continuous in the uncertainty set, although a single AVWC might not be. Moreover, it is shown that the question of super-activation and the continuity of the secrecy capacity solely depends on the legitimate link. Accordingly, the single-user AVC is subsequently studied and it is shown that in this case, super-activation for non-secure message transmission is not possible making it a unique feature of secure communication over AVWCs. However, the capacity for message transmission of the single-user AVC is shown to be super-additive including a complete characterization. Such knowledge is important for medium access control and in particular resource allocation as it determines the overall performance of a system.

We are concerned with the control of quality of service (QoS) in wireless cellular networks utilizing linear receivers. We investigate the issues of fairness and total performance, which are measured by a utility function in the form of a weighted sum of link QoS. We disprove the common conjecture on incompatibility of min-max fairness and utility optimality by characterizing network classes in which both goals can be accomplished concurrently. We characterize power and weight allocations achieving min-max fairness and utility optimality and show that they correspond to saddle points of the utility function. Next, we address the problem of the difference between min-max fairness and max-min fairness. We show that in general there is a (fairness) gap between the performance achieved under min-max fairness and under max-min fairness. We characterize the network class for which both performance values coincide. Finally, we characterize the corresponding network subclass, in which both min-max fairness and max-min fairness are achievable by the same power allocation.