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Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission, where only information relevant to a specific task is communicated. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant information, leading to privacy concerns and subpar performance. To address this, we propose an information-bottleneck method, named CLAD (contrastive learning and adversarial disentanglement). CLAD leverages contrastive learning to effectively capture task-relevant features while employing adversarial disentanglement to discard task-irrelevant information. Additionally, due to the lack of reliable and reproducible methods to gain insight into the informativeness and minimality of the encoded feature vectors, we introduce a new technique to compute the information retention index (IRI), a comparative metric used as a proxy for the mutual information between the encoded features and the input, reflecting the minimality of the encoded features. The IRI quantifies the minimality and informativeness of the encoded feature vectors across different task-oriented communication techniques. Our extensive experiments demonstrate that CLAD outperforms state-of-the-art baselines in terms of task performance, privacy preservation, and IRI. CLAD achieves a predictive performance improvement of around 2.5-3%, along with a 77-90% reduction in IRI and a 57-76% decrease in adversarial accuracy., Comment: Submitted to EEE Journal on Selected Areas in Communications (JSAC): Intelligent Communications for Real-Time Computer Vision (Comm4CV)

In this paper, we study the system-level advantages provided by rateless coding, early termination and power allocation strategy for multiple users distributed across multiple cells. In a multi-cell scenario, the early termination of coded transmission not only reduces finite-length loss akin to the single-user scenario but also yields capacity enhancements due to the cancellation of interference across cells. We term this technique \emph{coded water-filling}, a concept that diverges from traditional water-filling by incorporating variable-length rateless coding and interference cancellation. We formulate a series of analytical models to quantify the gains associated with coded water-filling in multi-user scenarios. First, we analyze the capacity gains from interference cancellation in Additive White Gaussian Noise (AWGN) channels, which arises from the disparity in the number of bits transmitted by distinct users. Building upon this, we broaden our analysis to encompass fading channels to show the robustness of the interference cancellation algorithms. Finally, we address the power allocation problem analogous to the water-filling problem under a multi-user framework, proving that an elevation in the water-filling threshold facilitates overall system capacity enhancement. Our analysis reveals the capacity gains achievable through early termination and power allocation techniques in multi-user settings. These results show that coded water-filling is instrumental for further improving spectral efficiency in crowded spectrums.

As an emerging magnetic phase, altermagnets with compensated magnetic order and non-relativistic spin-splitting have attracted widespread attention. Currently, strain engineering is considered to be an effective method for inducing valley polarization in altermagnets, however, achieving controllable switching of valley polarization is extremely challenging. Herein, combined with tight-binding model and first-principles calculations, we propose that interlayer sliding can be used to successfully induce and effectively manipulate the large valley polarization in altermagnets. Using Fe2MX4 (M = Mo, W; X = S, Se or Te) family as examples, we predict that sliding induced ferrovalley states in such systems can exhibit many unique properties, including the linearly optical dichroism that is independent of spin-orbit coupling, and the anomalous valley Hall effect. These findings imply the correlation among spin, valley, layer and optical degrees of freedom that makes altermagnets attractive in spintronics, valleytronics and even their crossing areas.

The coupling of ferroelectricity (FE) and superconductivity (SC) becomes the frontier of condensed matter research recently especially in the realm of two-dimensional (2D) materials. Identifying a general strategy to realize coexistence of FE and SC in a single material is extremely important for this active field, but quite challenging thus far. We show in this work that coexistence of robust FE and metallicity/SC can be realized by hole-doping a ferroelectric insulator which hosts antibonding highest valence bands (HVB). Using typical 2D ferroelecrtic SnS monolayer as a concrete example, we demonstrate that 0.30 hole/cell doping leads to enhancement of total polarization mainly ascribed to the increasing of polar displacement and ionic polarization. In addition, due to the strong Fermi surface nesting and prominent softening of out-of-plane acoustic phonon upon hole-doping, SnS can be turned into a single gap superconductor with an unexpectedly high transition temperature (Tc) of ~7 K, whereas the polar phonon mode gives negligible contribution to electron-phonon couplings. Our work provides general principle and realistic material for realizing metallic FE and superconducting FE, which paves the way for reversible and nonvolatile superconducting devices., Comment: Comments are welcome

For polar codes, successive cancellation list (SCL) decoding algorithm significantly improves finite-length performance compared to SC decoding. SCL-flip decoding can further enhance the performance but the gain diminishes as code length increases, due to the difficulty in locating the first error bit position. In this work, we introduce an SCL-perturbation decoding algorithm to address this issue. A basic version of the algorithm introduces small random perturbations to the received symbols before each SCL decoding attempt, and exhibits non-diminishing gain at large block lengths. Its enhanced version adaptively performs random perturbations or directional perturbation on each received symbol according to previous decoding results, and managed to correct more errors with fewer decoding attempts. Extensive simulation results demonstrate stable gains across various code rates, lengths and list sizes. To the best of our knowledge, this is the first SCL enhancement with non-diminishing gains as code length increases, and achieves unprecedented efficiency. With only one additional SCL-$L$ decoding attempt (in total two), the proposed algorithm achieves SCL-$2L$-equivalent performance. Since the gain is obtained without increasing list size, the algorithm is best suited for hardware implementation.

This paper studies the rate-distortion-perception (RDP) tradeoff for a Gaussian vector source coding problem where the goal is to compress the multi-component source subject to distortion and perception constraints. The purpose of imposing a perception constraint is to ensure visually pleasing reconstructions. This paper studies this RDP setting with either the Kullback-Leibler (KL) divergence or Wasserstein-2 metric as the perception loss function, and shows that for Gaussian vector sources, jointly Gaussian reconstructions are optimal. We further demonstrate that the optimal tradeoff can be expressed as an optimization problem, which can be explicitly solved. An interesting property of the optimal solution is as follows. Without the perception constraint, the traditional reverse water-filling solution for characterizing the rate-distortion (RD) tradeoff of a Gaussian vector source states that the optimal rate allocated to each component depends on a constant, called the water-level. If the variance of a specific component is below the water-level, it is assigned a {zero} compression rate. However, with active distortion and perception constraints, we show that the optimal rates allocated to the different components are always {positive}. Moreover, the water-levels that determine the optimal rate allocation for different components are unequal. We further treat the special case of perceptually perfect reconstruction and study its RDP function in the high-distortion and low-distortion regimes to obtain insight to the structure of the optimal solution.

Twisted van der Waals bilayers offer ideal two-dimensional (2D) platforms for exploring the intricate interplay between the spin and charge degrees of freedom of electrons. By investigating twisted MoS2 bilayer, featuring two distinct stackings but with identical commensurate supercell sizes, we reveal an unusual dielectric response behavior inherent to this system. Our first-principles calculations demonstrate that the application of an out-of-plane electric field gives different responses in electronic polarization. Upon further analysis, it becomes apparent that this dielectric response comes from the planar charge redistribution associated with spin-orbit coupling (SOC) effect. The underlying mechanism lies in the fact that the external electric field tends to modify the internal pseudo-spin texture \sigma, subsequently generating an out-of-plane (pseudo-) spin current j_s \propto \sigma \times B_R as response to an in-plane pseudomagnetic field B_R through Rashba SOC. It is found that the generated j_s is opposite for the two distinct stackings, resulting in opposite in-plane electric susceptibility. As a consequence, through magnetoelectric coupling within such nonmagnetic system, there give rise to opposite tendency to redistribute charge, ultimately leading to an amplified or suppressed dielectric response.

With low-buckled structure for each layer in graphene bilayer system, there breaks inversion symmetry (P-symmetry) for one stacking when both A and B sublattices in top layer are aligned with those in bottom layer. In consideration of spin-orbit coupling (SOC), there opens nontrivial topological gap in each monolayer system to achieve quantum spin Hall effect (QSHE). As long as time-reversal symmetry (T-symmetry) is preserved the gapless edge states is robust in each individual layer even for the bilayer absent of PT symmetry. Based on this platform and through tight-binding (TB) model calculations we find it becomes a typical system that can exhibit quantum valley Hall effect (QVHE) when introduced a layer-resolved Rashba SOC that leads to band inversion at each K valley in the hexagonal Brillion zone (BZ). The topological transition comes from that the valley Chern number Cv = CK - CK' switches from 0 to 2, which characterizes the nontrivial QVHE phase transited from two coupled Z2 topological insulators. We also point that the layer-resolved Rashba SOC can be introduced equivalently by twisting two van der Waals touched layers. And through TB calculations, it is shown that the K bands inverts in its corresponding mini BZ when the two layers twisted by a small angle. Our findings advance potential applications for the devices design in topological valleytronics and twistronics.

In this paper we document for the first time some of the effects of self-healing, a property of orbital-angular-momentum (OAM) or vortex beams, as observed on a millimeter-wave experimental communications link in an outdoors line-of-sight (LOS) scenario. The OAM beams have a helical phase and polarization structure and have conical amplitude shape in the far field. The Poynting vectors of the OAM beams also possess helical structures, orthogonal to the corresponding helical phase-fronts. Due to such non-planar structure in the direction orthogonal to the beam axis, OAM beams are a subset of structured light beams. Such structured beams are known to possess self-healing properties when partially obstructed along their propagation axis, especially in their near fields, resulting in partial reconstruction of their structures at larger distances along their beam axis. Various theoretical rationales have been proposed to explain, model and experimentally verify the self-healing physical effects in structured optical beams, using various types of obstructions and experimental techniques. Based on these models, we hypothesize that any self-healing observed will be greater as the OAM order increases. Here we observe the self-healing effects for the first time in structured OAM radio beams, in terms of communication signals and channel parameters rather than beam structures. We capture the effects of partial near-field obstructions of OAM beams of different orders on the communications signals and provide a physical rationale to substantiate that the self-healing effect was observed to increase with the order of OAM, agreeing with our hypothesis., Comment: 9 pages, 10 figures, pending submission to IEEE Access journal

In this paper, we study the lossless analog compression for i.i.d. nonsingular signals via the polarization-based framework. We prove that for nonsingular source, the error probability of maximum a posteriori (MAP) estimation polarizes under the Hadamard transform, which extends the polarization phenomenon to analog domain. Building on this insight, we propose partial Hadamard compression and develop the corresponding analog successive cancellation (SC) decoder. The proposed scheme consists of deterministic measurement matrices and non-iterative reconstruction algorithm, providing benefits in both space and computational complexity. Using the polarization of error probability, we prove that our approach achieves the information-theoretical limit for lossless analog compression developed by Wu and Verdu., Comment: 48 pages, 5 figures. This work was presented in part at the 2023 IEEE Global Communications Conference

With the rapid development and application of foundation models (FMs), it is foreseeable that FMs will play an important role in future wireless communications. As current Artificial Intelligence (AI) algorithms applied in wireless networks are dedicated models that aim for different neural network architectures and objectives, drawbacks in aspects of generality, performance gain, management, collaboration, etc. need to be conquered. In this paper, we define NetGPT (Network Generative Pre-trained Transformer) -- the foundation models for wireless communications, and summarize ten issues regarding design and application of NetGPT.

In order to address the lack of applicable channel models for ISAC research and evaluation, we release Sensiverse, a dataset that can be used for ISAC research. In this paper, we present the method of generating Sensiverse, including the acquisition and formatting of the 3D scene models, the generation of the channel data and associations with Tx/Rx deployment. The file structure and usage of the dataset are also described, and finally the use of the dataset is illustrated with examples through the evaluation of use cases such as 3D environment reconstruction and moving targets.

Low-dimensional ferroelectric tunnel junctions are appealing for the realization of nanoscale nonvolatile memory devices due to their inherent advantage of device miniaturization. Those based on current mechanisms still have restrictions including low tunneling electroresistance (TER) effects and complex heterostructures. Here, we introduce an entirely new TER mechanism to construct the nanotube ferroelectric tunnel junction with ferroelectric nanotubes as the tunneling region. When rolling a ferroelectric monolayer into a nanotube, due to the coexistence of its intrinsic ferroelectric polarization with the flexoelectric polarization induced by bending, there occurs metal-insulator transition depending on radiative polarization states. For the pristine monolayer, its out-of-plane polarization is tunable by an in-plane electric field, the conducting states of the ferroelectric nanotube can thus be tuned between metallic and insulating via axial electric means. Using {\alpha}-In2Se3 as an example, our first-principles density functional theory calculations and nonequilibrium Green's function formalism confirm the feasibility of the TER mechanism and indicate an ultrahigh TER ratio exceeding 9.9*10^10% of the proposed nanotube ferroelectric tunnel junctions. Our findings provide a promising approach based on simple homogeneous structures for high density ferroelectric microelectronic devices with excellent ON/OFF performance., Comment: 15 pages, 5 figures

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed.

Uncertainty in quality and the inspectors' imperfect testing capability leave raw agricultural products (e.g., fresh produce, seafood, livestock and poultry products, etc.) wide open to economically motivated adulteration (EMA), and the strong demand for online shopping of these products in China makes this situation even worse. In this paper, we develop a game-theoretic framework to investigate online raw agricultural product sellers' preemptive EMA behavior on an ecommerce platform (EP). Particularly, the sellers differ from each other in the original quality of their products. We characterize the sellers' equilibrium pricing and adulteration decisions and the EP's optimal take rate decision, and analyze how the sampling inspections and adulteration penalty jointly impact these decisions. Moreover, we investigate three managerial levers, such as claiming a higher-than-law-requires penalty, that the administrative departments or the EP can use to deter EMA. Finally, we use the real-word data from Taobao.com to calibrate our model and derive more managerial insights from the analytical findings. We find that the heterogenous sellers' adulteration decisions are symmetric and their ex-post pricing decisions lead them to evenly share the market on the EP. Interestingly, we show that the EP's higher take rate will inhibit the sellers' adulteration behavior. However, the profit-maximizing EP may indulge the sellers' adulteration behavior by intentionally decreasing this rate. Our results highlight a penalty-inspection-centered approach as essential to combat EMA, and the three levels can play a role as supplements to this approach under certain conditions.

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.

This study focuses on the efficiency of message-passing-based decoding algorithms for polar and low-density parity-check (LDPC) codes. Both successive cancellation (SC) and belief propagation (BP) decoding algorithms are studied {in} the message-passing framework. Counter-intuitively, SC decoding demonstrates the highest decoding efficiency, although it was considered a weak decoder {in terms of} error-correction performance. We analyze the complexity-performance tradeoff to dynamically track the decoding efficiency, where the complexity is measured by the number of messages passed (NMP), and the performance is measured by the statistical distance to the maximum a posteriori (MAP) estimate. This study offers a new insight into the contribution of each message passed in decoding, and compares various decoding algorithms on a message-by-message level. The analysis corroborates recent results on terabits-per-second polar SC decoders, and might shed light on better scheduling strategies.

Identification capacities of randomized and deterministic identification were proved to exceed channel capacity for Gaussian channels \emph{with} channel side information (CSI). In this work, we extend deterministic identification to the block fading channels without CSI by applying identification codes for both channel estimation and user identification. We prove that identification capacity is asymptotically higher than transmission capacity even in the absence of CSI. And we also analyze the finite-length performance theoretically and numerically. The simulation results verify the feasibility of the proposed blind deterministic identification in finite blocklength regime.

Rate-distortion-perception theory generalizes Shannon's rate-distortion theory by introducing a constraint on the perceptual quality of the output. The perception constraint complements the conventional distortion constraint and aims to enforce distribution-level consistencies. In this new theory, the information-theoretic limit is characterized by the rate-distortion-perception function. Although a coding theorem for the rate-distortion-perception function has recently been established, the fundamental nature of the optimal coding schemes remains unclear, especially regarding the role of randomness in encoding and decoding. It is shown in the present work that except for certain extreme cases, the rate-distortion-perception function is achievable by deterministic codes. This paper also clarifies the subtle differences between two notions of perfect perceptual quality and explores some alternative formulations of the perception constraint.

Methylammonium lead iodide, as related organometal halide perovskites, emerged recently as a particularly attractive material for photovoltaic applications. The origin of its appealing properties is sometimes assigned to its potential ferroelectric character, which remains however a topic of intense debate. Here, we rationalize from first-principles calculations how the spatial arrangement of methylammonium polar molecules is progressively constrained by the subtle interplay between their tendency to bond with the inorganic framework and the appearance of iodine octahedra rotations inherent to the perovskite structure. The disordered tetragonal phase observed at room temperature is paraelectric. We show that it should a priori become ferroelectric but that iodine octahedra rotations drive the system toward an antipolar orthorhombic ground state, making it a missed ferroelectric.

Epitaxial SrTiO$_3$ (STO) on Si is nowadays the benchmark initial platform for the further addition of functional oxides on Si. Starting the growth of STO on a Sr-passivated Si substrate with 1/2 monolayer (ML) Sr coverage and a (1 $\times$ 2) reconstructed Si surface with rows of Si dimers, the final STO/Sr/Si stack exhibits 1 ML Sr coverage and a (1 $\times$ 1) Si surface without dimer. Using first-principles density functional theory calculations, we investigate how the interface evolves from 1/2 ML to 1 ML Sr coverage, concluding that the latter is indeed most stable and that the reconstruction of the interface takes place during the early stage of the layer-by-layer deposition. Going further, we determine the band alignment of the final stable interface and assess its potential interest as photocathode for water reduction., Comment: 9 pages, 6 figures

A distributed lossy compression network with $L$ encoders and a decoder is considered. Each encoder observes a source and sends a compressed version to the decoder. The decoder produces a joint reconstruction of target signals with the mean squared error distortion below a given threshold. It is assumed that the observed sources can be expressed as the sum of target signals and corruptive noises which are independently generated from two symmetric multivariate Gaussian distributions. The minimum compression rate of this network versus the distortion threshold is referred to as the rate-distortion function, for which an explicit lower bound is established by solving a minimization problem. Our lower bound matches the well-known Berger-Tung upper bound for some values of the distortion threshold. The asymptotic gap between the upper and lower bounds is characterized in the large $L$ limit.

Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning. This article provides an overview on semantic communications. After a brief review of Shannon information theory, we discuss semantic communications with theory, framework, and system design enabled by deep learning. Different from the symbol/bit error rate used for measuring conventional communication systems, performance metrics for semantic communications are also discussed. The article concludes with several open questions in semantic communications.

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays with various beamforming technologies in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.

In recent years, techniques developed in artificial intelligence (AI), especially those in machine learning (ML), have been successfully applied in various areas, leading to a widespread belief that AI will collectively play an important role in future wireless communications. To accomplish the aspiration, we present nine challenges to be addressed by the interdisciplinary areas of AI/ML and wireless communications, with particular focus towards the sixth generation (6G) wireless networks. Specifically, this article classifies the nine challenges into computation in AI, distributed neural networks and learning, and ML enabled semantic communications., Comment: 6 pages

Targeting high-throughput and low-power communications, we implement two successive cancellation (SC) decoders for polar codes. With $16nm$ ASIC technology, the area efficiency and energy efficiency are $4Tbps/mm^2$ and $0.63pJ/bit$, respectively, for the unrolled decoder, and $561Gbps/mm^2$ and $1.21pJ/bit$, respectively, for the recursive decoder. To achieve such a high throughput, a novel code construction, coined as fast polar codes, is proposed and jointly optimized with a highly-parallel SC decoding architecture. First, we reuse existing modules to fast decode more outer code blocks, and then modify code construction to facilitate faster decoding for all outer code blocks up to a degree of parallelism of $16$. Furthermore, parallel comparison circuits and bit quantization schemes are customized for hardware implementation. Collectively, they contribute to an $2.66\times$ area efficiency improvement and $33\%$ energy saving over the state of the art., Comment: 8 pages, 5 figures. Part of this paper was presented in an invited talk at the 2021 International Symposium on Information Theory (ISIT)

In this paper, we design a polar decoding platform for diverse application scenarios that require low-cost and low-power communications. Specifically, prevalent polar decoders such as successive cancellation (SC), SC-list (SCL) and Fano decoders are all supported under the same architecture. Unlike high-throughput or low-latency decoders that promote parallelism, this architecture promotes serialization by repeatedly calling a ``sub-process'' that is executed by a core module. The resulting serial SCL-8 decoder is only 3 times as big as an SC decoder. Cost and power are minimized through resource sharing and adaptive decoding techniques, etc. We carried out performance simulation and hardware implementation to evaluate the actual chip area and energy consumption., Comment: 6 pages, 8 figures. Part of this paper was presented in an invited talk at the 2021 International Symposium on Information Theory (ISIT)