We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specifically, we focus on a seeded modular code design for the block-fading Gaussian wiretap channel with channel-output feedback, combining universal hash functions for security and learned feedback-based codes for reliability. The trade-off between communication reliability and information leakage is studied, illustrating that feedback enables agreeing on a secret key shared between legitimate parties, overcoming the security advantage of the eavesdropper. Our findings motivate code designs for sensing-assisted secure communications in the context of integrated sensing and communication (ISAC).

This paper focuses on enhancing the energy efficiency (EE) of a cooperative network that features a miniature unmanned aerial vehicle (UAV) operating at terahertz (THz) frequencies and equipped with holographic surfaces to improve network performance. Unlike traditional reconfigurable intelligent surfaces (RIS), which serve as passive relays for signal reflection, this work introduces a novel concept: energy harvesting (EH) using reconfigurable holographic surfaces (RHS). These surfaces provide more powerful and focused energy delivery during wireless power transfer than RIS and are mounted on the miniature UAV. In this system, a source node enables the UAV to simultaneously receive both information and energy signals, with the harvested energy powering data transmission to a specific destination. The EE optimization problem involves adjusting non-orthogonal multiple access (NOMA) power coefficients and the UAV's flight path while accounting for the unique characteristics of the THz channel. The problem is solved in two stages to maximize EE and meet a target transmission rate. The UAV trajectory is optimized using a successive convex approximation (SCA) method, followed by the adjustment of NOMA power coefficients through a quadratic transform technique. Simulation results demonstrate the effectiveness of the proposed algorithm, showing significant improvements over baseline methods.

The KO (Kronecker Operation) code is a recent deep-learned error-correcting code using a neural network architecture to generalize a Reed-Muller code with Dumer decoding. Analyzing the encoder modules and using ablation techniques, we give interpretations of the KO encoder which significantly reduce the number of parameters. We also discuss interpretability aspects of the KO decoder. The interpretation opens up possibilities to give an explicit representation of KO codes, which could be useful for more efficient learning of KO codes and explaining the learning mechanism underlying the empirical observations made about its performance in previous work.

Arbiter-based Physically Unclonable Functions (APUFs) utilize the variability in manufacturing to create distinct digital identifiers for integrated circuits (ICs). Essentially, the input-output functions / truth-tables / full set of "responses" to "challenges", serve as potential hardware security primitives. To fulfill this role, every APUF batch from the same design should exhibit specific features; two of the most important are the response bias and uniqueness. A faulty APUF batch with a mu-fault from the design phase fails to achieve desired uniqueness levels and sometimes exhibits undesired response bias as well, hence is unqualified for security purposes. Instead of discarding such faulty APUFs and re-designing, we present a novel method to salvage a faulty APUF batch with the presence of multiple mu-faults, so that the desired uniqueness and bias are restored. This is done by carefully selecting challenges that can mitigate the impact of the faults. Such a salvaging strategy via challenge selection is intrinsically difficult, due to the enormous size of the challenge set, the black-box nature of APUFs, and the need to perform such tasks efficiently. To overcome these problems, we propose a simple yet effective way to estimate the intensity of the multiple faults and use them to guide the challenge selection process. The proposed method can efficiently find large challenge sets that achieve the desired response bias and uniqueness, thus salvaging a faulty APUF batch in the post-production phase.

Several researchers have used deep learning to obtain novel feedback codes. Two such codes for AWGN channels with passive (possibly noisy) output feedback are Deepcode which employs a bit-by-bit rate 1/3 encoder, and Lightcode, which is a symbol-by-symbol code inspired by the Schalkwijk-Kailath (SK) scheme. Here, we build on prior work to interpret these codes by 1) providing the optimal maximum a posteriori (MAP) decoder for our simple non-linear interpretable encoder of a single-bit, two-round code that accurately approximates both single-bit Deepcode and Lightcode. This non-linear interpretable coding scheme, which mimics these codes, turns out to resemble both the functional form and performance of the Polyanskiy-Poor-Verdu (PPV) single bit feedback scheme that minimizes energy transmission asymptotically. 2) We extend our non-linear interpretable code to support more than one bit and two rounds, again providing an optimal MAP decoder. This remarkably simple and power-efficient nonlinear scheme provides insight into Lightcode.

Recent advances in deep learning for wireless communications have renewed interest in channel output feedback codes. In the additive white Gaussian broadcast channel with feedback (AWGN-BC-F), feedback can expand the channel capacity region beyond that of the no-feedback case, but linear analytical codes perform poorly with even small amounts of feedback noise. Deep learning enables the design of nonlinear feedback codes that are more resilient to feedback noise. We extend single-user learned feedback codes for the AWGN channel to the broadcast setting, and compare their performance with existing analytical codes, as well as a newly proposed analytical scheme inspired by the learned schemes. Our results show that, for a fixed code rate, learned codes outperform analytical codes at the same blocklength by using power-efficient nonlinear structures and are more robust to feedback noise. Analytical codes scale more easily to larger blocklengths with perfect feedback and surpass learned codes at higher SNRs.

We focus on designing error-correcting codes for the symmetric Gaussian broadcast channel with feedback. Feedback not only expands the capacity region of the broadcast channel but also enhances transmission reliability. In this work, we study the construction of learned finite blocklength codes for broadcast channels with feedback. Learned error-correcting codes, in which both the encoder and decoder are jointly trained, have shown impressive performance in point-to-point channels, particularly with noisy feedback. However, few learned schemes exist for multi-user channels. Here, we develop a lightweight code for the broadcast channel with feedback that performs well and operates effectively at short blocklengths.

We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specifically, we focus on a seeded modular code design for the block-fading Gaussian wiretap channel with channel-output feedback, combining universal hash functions for security and learned feedback-based codes for reliability. The trade-off between communication reliability and information leakage is studied, illustrating that feedback enables agreeing on a secret key shared between legitimate parties, overcoming the security advantage of the eavesdropper. Our findings motivate code designs for sensing-assisted secure communications in the context of integrated sensing and communication (ISAC).

This paper explores the fundamental limits of Integrated Sensing and Communication (ISAC) in a more realistic setting compared to previous literature when the Base Staion (BS) has only statistical CSI of the communication user rather than full CSI. We analyze a monostatic setting where the BS performs multi-target Angle of Arrival (AoA) estimation while simultaneously communicating with one of the targets. We assume that the BS has statistical CSI about all AoAs, with less uncertainty in the AoA of the communication receiver. The communication receiver is assumed to have perfect CSI. Utilizing a Bayesian Cramér-Rao Bound (BCRB) framework to characterize the fundamental limits of sensing under minimum mean square error (MMSE) criteria, we derive achievable BCRB-rate trade-off regions. Our approach introduces a number of transmission strategies that share power across sensing and communication beams over a coherence time. Our analysis reveals that beam allocation strategies leveraging the principal eigenvectors of the target-specific sensing matrices minimize individual AoA estimation errors, while strategies balancing sensing and communication directions optimize joint estimation performance at the cost of individual accuracy. We demonstrate that leveraging updated BCRB-based sensing information for the communication receiver, due to its lower channel uncertainty, enables significantly improved communication rates.

The sustainable development of the Internet of Things hinges on communication solutions that are battery-less, minimal in hardware complexity, and capable of operating near the optimal trade-off between data and power reception. Traditionally, the balance between downlink data and power transfer has been explored through various forms of splitting and conventional communication methods. In this article, we introduce two novel approaches that enable simultaneous and unified receivers, capable of rectifying power and demodulating information from the same received signal using identical analog low-power hardware. We demonstrate how information can be encoded in both amplitude and frequency, and how hardware-induced non-idealities can be mitigated or even leveraged. Our goal is to bring these unified receivers to the forefront of the IoT community's attention, and outline several open problems that warrant further investigation.

Deep learning methods have recently been used to construct non-linear codes for the additive white Gaussian noise (AWGN) channel with feedback. However, there is limited understanding of how these black-box-like codes with many learned parameters use feedback. This study aims to uncover the fundamental principles underlying the first deep-learned feedback code, known as Deepcode, which is based on an RNN architecture. Our interpretable model based on Deepcode is built by analyzing the influence length of inputs and approximating the non-linear dynamics of the original black-box RNN encoder. Numerical experiments demonstrate that our interpretable model -- which includes both an encoder and a decoder -- achieves comparable performance to Deepcode while offering an interpretation of how it employs feedback for error correction.

A physically unclonable function (PUF) is a hardware security primitive used for authentication and key generation. It takes an input bit-vector challenge and produces a single-bit response, resulting in a challenge-response pair (CRP). The truth table of all challenge-response pairs of each manufactured PUF should look different due to inherent manufacturing randomness, forming a digital fingerprint. A PUF’s entropy (the entropy of all the responses, taken over the manufacturing randomness and uniformly selected challenges) has been studied before and is a challenging problem. Here, we explore a related notion—the response entropy, which is the entropy of an arbitrary response given knowledge of one (and later two) other responses. This allows us to explore how knowledge of some CRP(s) impacts the ability to guess another response. The arbiter PUF (APUF) is a well-known PUF architecture based on accumulated delay differences between two paths. In this paper, we obtain in closed form the probability mass function of any arbitrary response given knowledge of one or two other arbitrary CRPs for the APUF architecture. This allows us to obtain the conditional response entropy and then to define and obtain the size of the entropy bins (challenge sets with the same conditional response entropy) given knowledge of one or two CRPs. All of these results depend on the probability that two different challenge vectors yield the same response, termed the response similarity of those challenges. We obtain an explicit closed-form expression for this. This probability depends on the statistical correlations induced by the PUF architecture together with the specific known and to-be-guessed challenges. As a by-product, we also obtain the optimal (minimizing probability of error) predictor of an unknown challenge given access to one (or two) challenges and the associated predictability.

We present an interpretation of Deepcode, a learned feedback code that showcases higher-order error correction relative to an earlier interpretable model. By interpretation, we mean succinct analytical encoder and decoder expressions (albeit with learned parameters) in which the role of feedback in achieving error correction is easy to understand. By higher-order, we mean that longer sequences of large noise values are acted upon by the encoder (which has access to these through the feedback) and used in error correction at the decoder in a two-stage decoding process.

In classical capacity analysis for point-to-point (P2P) channels, all transmitted data is equally protected. Transmitted data will be recovered if and only if transmitted at a rate below the channel's capacity. When a P2P channel is studied in the Finite Blocklength (FBL) regime the inclusion of a reliability term suggests the possibility of constructing codes that protect portions of the data differently. In this paper, we present an FBL achievable bound for the utilization of superposition (SUP) coding with successive interference cancellation (SIC) in the realization of Unequal Bit Protection (UBP) in transmission over a static, scalar additive white Gaussian noise (AWGN) channel. We also present a converse bound for the problem. Through our numerical analysis we show that in cases where data to be transmitted has known and differing reliability requirements, the use of superposition coding can increase the achievable sum rate of transmission of all classes of data protection over a uniform protection coding scheme and orthogonalization over time. However, the (SUP-SIC) achievable region and converse are not tight, and the FBL UBP capacity remains an open problem.

Most deep-learned error-correcting codes (DL-ECCs) use binary cross-entropy (BCE) between the true input bits and the soft decoded outputs of the learned encoder/decoder pair as a loss function during training. It is known that this decomposes into two terms which in the case of a DL-ECC correspond to: a Kullback-Leibler (KL) divergence between the true and estimated posteriors on the input bits given the noisy channel outputs, and the conditional entropy (CE) of the input bits given the channel outputs. We use this decomposition to explore the training process of one particular DLECC termed TurboAE, which replaces constituent codes in the encoders/decoders of a Turbo code with learned convolutional neural networks. Evaluating each term in the BCE decomposition of TurboAE is facilitated through the junction tree algorithm for exact inference on graphs, yielding a MAP decoding of TurboAE-like codes of up to 40 input, 120 output bits (vs. the original 100-bit inputs). Plots of this decomposition over training offer insight into the alternating training process used in TurboAE and other DL-ECCs. We add to the growing body of work on interpreting DL-ECCs by providing a new lens through which to view any DL-ECC training process that uses BCE as a loss function.

This paper considers the single antenna, static, scalar Gaussian broadcast channel in the finite blocklength regime. Second order achievable and converse rate regions are presented. Both a global reliability and per-user reliability requirements are considered. The two-user case is analyzed in detail, and generalizations to the $K$ -user case are also discussed. The largest second order achievable regions presented here require both superposition and rate splitting in the code construction, as opposed to the (infinite blocklength, first order) capacity region which does not require rate splitting. Indeed, the finite blocklength penalty causes superposition alone to under-perform other coding techniques in some parts of the region. In addition, the proposed scheme uses joint simultaneous decoding as opposed to successive interference cancellation. Interestingly, in the two-user case with per-user reliability requirements, the capacity achieving superposition encoding order (with the codeword intended for the user with the smallest received SNR as cloud center) does not necessarily give the largest second order region. Instead, the message of the user with the smallest point-to-point second order capacity should be encoded in the cloud center in order to obtain the largest second order region for the proposed scheme.

Modeling attacks, in which an adversary uses machine learning techniques to model a hardware-based Physically Unclonable Function (PUF) pose a great threat to the viability of these hardware security primitives. In most modeling attacks, a random subset of challenge-response-pairs (CRPs) are used as the labeled data for the machine learning algorithm. Here, for the arbiter-PUF, a delay based PUF which may be viewed as a linear threshold function with random weights (due to manufacturing imperfections), we investigate the role of active learning in Support Vector Machine (SVM) learning. We focus on challenge selection to help SVM algorithm learn ``fast'' and learn ``slow''. Our methods construct challenges rather than relying on a sample pool of challenges as in prior work. Using active learning to learn ``fast'' (less CRPs revealed, higher accuracies) may help manufacturers learn the manufactured PUFs more efficiently, or may form a more powerful attack when the attacker may query the PUF for CRPs at will. Using active learning to select challenges from which learning is ``slow'' (low accuracy despite a large number of revealed CRPs) may provide a basis for slowing down attackers who are limited to overhearing CRPs.

As new deep-learned error-correcting codes continue to be introduced, it is important to develop tools to interpret the designed codes and understand the training process. Prior work focusing on the deep-learned TurboAE has both interpreted the learned encoders post-hoc by mapping these onto nearby "interpretable" encoders, and experimentally evaluated the performance of these interpretable encoders with various decoders. Here we look at developing tools for interpreting the training process for deep-learned error-correcting codes, focusing on: 1) using the Goldreich-Levin algorithm to quickly interpret the learned encoder; 2) using Fourier coefficients as a tool for understanding the training dynamics and the loss landscape; 3) reformulating the training loss, the binary cross entropy, by relating it to encoder and decoder parameters, and the bit error rate (BER); 4) using these insights to formulate and study a new training procedure. All tools are demonstrated on TurboAE, but are applicable to other deep-learned forward error correcting codes (without feedback).

Arbiter Physically Unclonable Functions (APUFs) are low-cost hardware security primitives that may serve as unique digital fingerprints for ICs. To fulfill this role, it is critical for manufacturers to ensure that a batch of PUFs coming off the same design and production line have different truth tables, and uniqueness / inter-PUF-distance metrics have been defined to measure this. This paper points out that a widely-used uniqueness metric fails to capture some special cases, which we remedy by proposing a modified uniqueness metric. We then look at two fundamental APUF-native production line fault models that severely affect uniqueness: the $\mu$ (abnormal mean of a delay difference element) and (abnormal variance of a delay difference element) faults. We propose test and diagnosis methods aimed at these two APUF production line faults, and show that these low-cost techniques can efficiently and effectively detect such faults, and pinpoint the element of abnormality, without the (costly) need to directly measure the uniqueness metric of a PUF batch.

Deep learning has been used recently to learn error-correcting encoders and decoders which may improve upon previously known codes in certain regimes. The encoders and decoders are learned "black-boxes", and interpreting their behavior is of interest both for further applications and for incorporating this work into coding theory. Understanding these codes provides a compelling case study for Explainable Artificial Intelligence (XAI): since coding theory is a well-developed and quantitative field, the interpretability problems that arise differ from those traditionally considered. We develop post-hoc interpretability techniques to analyze the deep-learned, autoencoder-based encoders of TurboAE-binary codes, using influence heatmaps, mixed integer linear programming (MILP), Fourier analysis, and property testing. We compare the learned, interpretable encoders combined with BCJR decoders to the original black-box code.