The impulse response of the first arrival position (FAP) channel in molecular communication (MC) has been derived for spatial dimensions 2 and 3 in recent works, however, the Shannon capacity of FAP channels has yet to be determined. The fundamental obstacle to determining the capacity of FAP channels is rooted in the multi-dimensional Cauchy distribution nature of the FAP density, particularly when the drift velocity approaches zero. Consequently, conventional approaches for maximizing mutual information are inapplicable as the first and second moments of Cauchy distributions are non-existent. This paper presents a comprehensive characterization of the zero-drift FAP channel capacity for 2D and 3D spaces. The capacity formula for the FAP channel is found to have a form similar to the Gaussian channel case (under second-moment power constraint). Notably, the capacity of the 3D FAP channel is twice that of the 2D FAP channel, providing evidence that FAP channels have greater capacity as spatial dimensions increase. Our technical contributions include the application of a modified logarithmic constraint in lieu of the typical power constraint, and the selection of an output signal constraint as a replacement for the input signal constraint, resulting in a more concise formula.
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Deterministic identification over K-input multiple-access channels with average input cost constraints is considered. The capacity region for deterministic identification is determined for an average-error criterion, where arbitrarily large codes are achievable. For a maximal-error criterion, upper and lower bounds on the capacity region are derived. The bounds coincide if all average partial point-to-point channels are injective under the input constraint, i.e. all inputs at one terminal are mapped to distinct output distributions, if averaged over the inputs at all other terminals. The achievability is proved by treating the MAC as an arbitrarily varying channel with average state constraints. For injective average channels, the capacity region is a hyperrectangle. The modulo-2 and modulo-3 binary adder MAC are presented as examples of channels which are injective under suitable input constraints. The binary multiplier MAC is presented as an example of a non-injective channel, where the achievable identification rate region still includes the Shannon capacity region.
The increasingly crowded spectrum has spurred the design of joint radar-communications systems that share hardware resources and efficiently use the radio frequency spectrum. We study a general spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown at the receiver. In this dual-blind deconvolution (DBD) problem, a common receiver admits a multi-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets. The communications and radar channels are represented by continuous-valued range-time and Doppler velocities of multiple transmission paths and multiple targets. We exploit the sparsity of both channels to solve the highly ill-posed DBD problem by casting it into a sum of multivariate atomic norms (SoMAN) minimization. We devise a semidefinite program to estimate the unknown target and communications parameters using the theories of positive-hyperoctant trigonometric polynomials (PhTP). Our theoretical analyses show that the minimum number of samples required for near-perfect recovery is dependent on the logarithm of the maximum of number of radar targets and communications paths rather than their sum. We show that our SoMAN method and PhTP formulations are also applicable to more general scenarios such as unsynchronized transmission, the presence of noise, and multiple emitters. Numerical experiments demonstrate great performance enhancements during parameter recovery under different scenarios.
We establish sparsity and summability results for coefficient sequences of Wiener-Hermite polynomial chaos expansions of countably-parametric solutions of linear elliptic and parabolic divergence-form partial differential equations with Gaussian random field inputs. The novel proof technique developed here is based on analytic continuation of parametric solutions into the complex domain. It differs from previous works that used bootstrap arguments and induction on the differentiation order of solution derivatives with respect to the parameters. The present holomorphy-based argument allows a unified, ``differentiation-free'' proof of sparsity (expressed in terms of $\ell^p$-summability or weighted $\ell^2$-summability) of sequences of Wiener-Hermite coefficients in polynomial chaos expansions in various scales of function spaces. The analysis also implies corresponding analyticity and sparsity results for posterior densities in Bayesian inverse problems subject to Gaussian priors on uncertain inputs from function spaces. Our results furthermore yield dimension-independent convergence rates of various \emph{constructive} high-dimensional deterministic numerical approximation schemes such as single-level and multi-level versions of Hermite-Smolyak anisotropic sparse-grid interpolation and quadrature in both forward and inverse computational uncertainty quantification.
This paper addresses two fundamental problems in diffusive molecular communication: characterizing the first arrival position (FAP) density and bounding the information transmission capacity of FAP channels. Previous studies on FAP channel models, mostly captured by the density function of noise, have been limited to specific spatial dimensions, drift directions, and receiver geometries. In response, we propose a unified solution for identifying the FAP density in molecular communication systems with fully-absorbing receivers. Leveraging stochastic analysis tools, we derive a concise expression with universal applicability, covering any spatial dimension, drift direction, and receiver shape. We demonstrate that several existing FAP density formulas are special cases of this innovative expression. Concurrently, we establish explicit upper and lower bounds on the capacity of three-dimensional, vertically-drifted FAP channels, drawing inspiration from vector Gaussian interference channels. In the course of deriving these bounds, we unravel an explicit analytical expression for the characteristic function of vertically-drifted FAP noise distributions, providing a more compact characterization compared to the density function. Notably, this expression sheds light on a previously undiscovered weak stability property intrinsic to vertically-drifted FAP noise distributions.
Empirical evidence demonstrates that citations received by scholarly publications follow a pattern of preferential attachment, resulting in a power-law distribution. Such asymmetry has sparked significant debate regarding the use of citations for research evaluation. However, a consensus has yet to be established concerning the historical trends in citation concentration. Are citations becoming more concentrated in a small number of articles? Or have recent geopolitical and technical changes in science led to more decentralized distributions? This ongoing debate stems from a lack of technical clarity in measuring inequality. Given the variations in citation practices across disciplines and over time, it is crucial to account for multiple factors that can influence the findings. This article explores how reference-based and citation-based approaches, uncited articles, citation inflation, the expansion of bibliometric databases, disciplinary differences, and self-citations affect the evolution of citation concentration. Our results indicate a decreasing trend in citation concentration, primarily driven by a decline in uncited articles, which, in turn, can be attributed to the growing significance of Asia and Europe. On the whole, our findings clarify current debates on citation concentration and show that, contrary to a widely-held belief, citations are increasingly scattered.
Evolvability refers to the ability of an individual genotype (solution) to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent search methods, particularly novelty search, promote evolvability by implicitly creating selective pressure for it. The main objective of this paper is to provide a novel perspective on the relationship between neuroevolutionary divergent search and evolvability. In order to achieve this, several types of walks from the literature on fitness landscape analysis are first adapted to this context. Subsequently, the interplay between neuroevolutionary divergent search and evolvability under varying amounts of evolutionary pressure and under different diversity metrics is investigated. To this end, experiments are performed on Fetch Pick and Place, a robotic arm task. Moreover, the performed study in particular sheds light on the structure of the genotype-phenotype mapping (the behavior landscape). Finally, a novel definition of evolvability that takes into account the evolvability of offspring and is appropriate for use with discretized behavior spaces is proposed, together with a Markov-chain-based estimation method for it.
This article focuses on the near-field effect in terahertz (THz) communications and sensing systems. By equipping with extremely large-scale antenna arrays (ELAAs), the near-field region in THz systems can be possibly extended to hundreds of meters in proximity to THz transceivers, which necessitates the consideration of near-field effect in the THz band both for the communications and sensing. We first review the main characteristics of the near-field region in the THz bands. The signal propagation in the near-field region is characterized by spherical waves rather than planar waves in the far-field region. This distinction introduces a new distance dimension to the communication and sensing channels, which brings new opportunities and challenges for both THz communications and sensing. More particularly, 1) For THz communications, the near-field effect enables a new mechanism for beamforming, namely, beamfocusing, in the focusing region. Furthermore, in THz multiple-input and multiple-output (MIMO) systems, the near-field effect can be exploited to combat the multiplexing gain degradation caused by the sparse THz channels. To address the near-field beam split effect caused by the conventional frequency-independent hybrid beamforming architecture in THz wideband communications, we propose a pair of wideband beamforming optimization approaches by a new hybrid beamforming architecture based on true-time-delayers (TTDs). 2) For THz sensing, joint angle and distance sensing can be achieved in the near-field region. Additionally, the near-field beam split becomes a beneficial effect for enhancing the sensing performance by focusing on multiple possible target locations rather than a drawback encountered in communications. Finally, several topics for future research are discussed.
UAV (unmanned aerial vehicle) is rapidly gaining traction in various human activities and has become an integral component of the satellite-air-ground-sea (SAGS) integrated network. As high-speed moving objects, UAVs not only have extremely strict requirements for communication delay, but also cannot be maliciously controlled as a weapon by the attacker. Therefore, an efficient and secure communication method designed for UAV networks is necessary. We propose a communication mechanism ESCM. For high efficiency, ESCM provides a routing protocol based on the artificial bee colony (ABC) algorithm to accelerate communications between UAVs. Meanwhile, we use blockchain to guarantee the security of UAV networks. However, blockchain has unstable links in high-mobility networks resulting in low consensus efficiency and high communication overhead. Consequently, ESCM introduces digital twin (DT), which transforms the UAV network into a static network by mapping UAVs from the physical world into Cyberspace. This virtual UAV network is called CyberUAV. Then, in CyberUAV, we design a blockchain consensus based on network coding, named Proof of Network Coding (PoNC). Analysis and simulation show that the above modules in ESCM have advantages over existing schemes. Through ablation studies, we demonstrate that these modules are indispensable for efficient and secure communication of UAV networks.
Multivariate sequential data collected in practice often exhibit temporal irregularities, including nonuniform time intervals and component misalignment. However, if uneven spacing and asynchrony are endogenous characteristics of the data rather than a result of insufficient observation, the information content of these irregularities plays a defining role in characterizing the multivariate dependence structure. Existing approaches for probabilistic forecasting either overlook the resulting statistical heterogeneities, are susceptible to imputation biases, or impose parametric assumptions on the data distribution. This paper proposes an end-to-end solution that overcomes these limitations by allowing the observation arrival times to play the central role of model construction, which is at the core of temporal irregularities. To acknowledge temporal irregularities, we first enable unique hidden states for components so that the arrival times can dictate when, how, and which hidden states to update. We then develop a conditional flow representation to non-parametrically represent the data distribution, which is typically non-Gaussian, and supervise this representation by carefully factorizing the log-likelihood objective to select conditional information that facilitates capturing time variation and path dependency. The broad applicability and superiority of the proposed solution are confirmed by comparing it with existing approaches through ablation studies and testing on real-world datasets.
This paper deals with the problem of finding the preferred extensions of an argumentation framework by means of a bijection with the naive sets of another framework. First, we consider the case where an argumentation framework is naive-bijective: its naive sets and preferred extensions are equal. Recognizing naive-bijective argumentation frameworks is hard, but we show that it is tractable for frameworks with bounded in-degree. Next, we give a bijection between the preferred extensions of an argumentation framework being admissible-closed (the intersection of two admissible sets is admissible) and the naive sets of another framework on the same set of arguments. On the other hand, we prove that identifying admissible-closed argumentation frameworks is coNP-complete. At last, we introduce the notion of irreducible self-defending sets as those that are not the union of others. It turns out there exists a bijection between the preferred extensions of an argumentation framework and the naive sets of a framework on its irreducible self-defending sets. Consequently, the preferred extensions of argumentation frameworks with some lattice properties can be listed with polynomial delay and polynomial space.
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