Information-Theoretic Limits on Compression of Semantic Information

As conventional communication systems based on classic information theory have closely approached Shannon capacity,semantic communication is emerging as a key enabling technology for the further improvement of communication performance.However,it is still unsettled on how to represent semantic information and characterise the theoretical limits of semantic-oriented compression and transmission.In this paper,we consider a semantic source which is characterised by a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network.We give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence.We further characterise the limits on lossy compression of the semantic source and the upper and lower bounds of the rate-distortion function.We also investigate the lossy compression of the semantic source with two-sided information at the encoder and decoder,and obtain the corresponding rate distortion function.We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information.

An inversely designed integrated spectrometer with reconfigurable performance and ultra-low power consumption

Despite the pressing demand for integrated spectrometers,a solution that deliver high-performance while being practically operated is still missing.Furthermore,current integrated spectrometers lack reconfigurability in their performance,which is highly desirable for dynamic working scenarios.This study presents a viable solution by demonstrating a userfriendly,reconfigurable spectrometer on silicon.At the core of this innovative spectrometer is a programmable photonic circuit capable of exhibiting diverse spectral responses,which can be significantly adjusted using on-chip phase shifters.The distinguishing feature of our spectrometer lies in its inverse design approach,facilitating effortless control and efficient manipulation of the programmable circuit.By eliminating the need for intricate configuration,our design reduces power consumption and mitigates control complexity.Additionally,our reconfigurable spectrometer offers two distinct operating conditions.In the Ultra-High-Performance mode,it is activated by multiple phase-shifters and achieves exceptional spectral resolution in the picometer scale while maintaining broad bandwidth.On the other hand,the Ease-of-Use mode further simplifies the control logic and reduces power consumption by actuating a single-phase shifter.Although this mode provides a slightly degraded spectral resolution of approximately 0.3 nm,it prioritizes ease of use and is wellsuited for applications where ultra-fine spectral reconstruction is not a primary requirement.

Electronic Structure and Optical Properties of LiBiO3 Doped with V,Nb,and Ta

The crystal structure, band structure, density of states, Mulliken charge, bond population and optical properties for LiBi_（1-x）M_xO_3（M=V, Nb, and Ta） were investigated using hybrid density functional theory. It was found that LiBiO_3 doped with V, Nb, and Ta presented distinctly stronger covalent interactions in M-O（M=V, Nb, and Ta） than Bi-O, thus resulting in mild distortion of the structure and facilitating the separation of photogenerated carriers. Furthermore, the hybridizations of Bi-6s, M-d（M=V, Nb, and Ta） and O-2p widened the valence and conduction bands, which promoted transmission of photogenerated carriers in the band edge and thus caused better photocatalytic performance.

Deep Learning Aided SCL Decoding of Polar Codes with Shifted-Pruning

Recently,a generalized successive cancellation list(SCL)decoder implemented with shiftedpruning(SP)scheme,namely the SCL-SP-ωdecoder,is presented for polar codes,which is able to shift the pruning window at mostωtimes during each SCL re-decoding attempt to prevent the correct path from being eliminated.The candidate positions for applying the SP scheme are selected by a shifting metric based on the probability that the elimination occurs.However,the number of exponential/logarithm operations involved in the SCL-SP-ωdecoder grows linearly with the number of information bits and list size,which leads to high computational complexity.In this paper,we present a detailed analysis of the SCL-SP-ωdecoder in terms of the decoding performance and complexity,which unveils that the choice of the shifting metric is essential for improving the decoding performance and reducing the re-decoding attempts simultaneously.Then,we introduce a simplified metric derived from the path metric(PM)domain,and a custom-tailored deep learning(DL)network is further designed to enhance the efficiency of the proposed simplified metric.The proposed metrics are both free of transcendental functions and hence,are more hardware-friendly than the existing metrics.Simulation results show that the proposed DL-aided metric provides the best error correction performance as comparison with the state of the art.

Advances in Software Defined Networking and Network Functions Virtualization

Software defined networking (SDN) and network functions virtualization (NFV) are being heralded as a fundamental leap forward, which will substantively transform the landscape of the whole telecommunications and networks area. A number of key technical advancements for network softwarization and virtualization, commoditizing networking hardware, simplifying operations, and reducing capital and operational expenditures have been quickly emerging on the fronts. Basically, decoupling the control plane from the data plane as well as decoupling the network functions from the hardware implementation using virtualization and service abstraction possess distinctive networking flexibility and capability, but also point to a number of functional and operational challenges.

High-Pressure Oxidation on Ge:Improvement of Ge/GeO2 Interface and GeO2 Bulk Properties

On the basis of thermodynamic and kinetic consideration of Ge-O system,high-pressure oxidation(HPO)on Ge was proposed to suppress the GeO desorption during the thermal oxidation and significant improvements of Ge/GeO2-based gate stacks have been achieved.It is found that the post oxidation annealing at lower temperatures is helpful to passivate the interface defects at the Ge/GeO2 stack generated by the conventional thermal oxidation,while the high-quality GeO2 bulk properties can only be achieved by HPO that grows GeO2 film at high temperatures without the GeO desorption.This paper reviews the advantage of HPO on the formation of Ge/GeO2 stacks in terms of Ge/GeO2 interface and GeO2 bulk properties.

Task Offloading Optimization for AGVs with Fixed Routes in Industrial IoT Environment

In order to solve the delay requirements of computing intensive tasks in industrial Internet of things,edge computing is moving from theoretical research to practical applications.Edge servers(ESs)have been deployed in factories,and on-site auto guided vehicles(AGVs),besides doing their regular transportation tasks,can partly act as mobile collectors and distributors of computing data and tasks.Since AGVs may offload tasks to the same ES if they have overlapping path segments,resource allocation conflicts are inevitable.In this paper,we study the problem of efficient task offloading from AGVs to ESs,along their fixed trajectories.We propose a multi-AGV task offloading optimization algorithm(MATO),which first uses the weighted polling algorithm to preliminarily allocate tasks for individual AGVs based on load balancing,and then uses the Deep Q-Network(DQN)model to obtain the updated offloading strategy for the AGV group.The simulation results show that,compared with the existing methods,the proposed MATO algorithm can significantly reduce the maximum completion time of tasks and be stable under various parameter settings.

Performance Analysis of RIS Assisted NOMA Networks over Rician Fading Channels

In this paper,we consider a downlink non-orthogonal multiple access(NOMA)network assisted by two reconfigurable intelligent surfaces(RISs)over Rician fading channels,in which each user communicates with the base station by the virtue of a RIS to enhance the reliability of the received signal.To evaluate the system performance of our proposed RIS-NOMA network,we first derive the exact and asymptotic expressions for the outage probability and ergodic rate of two users.Then,we derive the exact and asymptotic upper bound expressions for the ergodic rate of the nearby user.Based on asymptotic analytical results,the diversity orders for the outage probability and the high signal-to-noise ratio(SNR)slopes for the ergodic rate of the two users are obtained in the high SNR regime.Moreover,we derive the system throughputs of the proposed RIS-NOMA network in delay-limited and delay-tolerant transmission modes.Numerical results confirm our analysis and demonstrate that:1)The outage probability and ergodic rate of RIS-NOMA networks are superior to that of RIS-assisted orthogonalmultiple access(OMA)networks;2)The RIS-NOMA networks have ability to achieve a larger system throughput compared to RIS-OMA networks;and 3)The system performance of RIS-NOMA networks can be significantly improved as the number of reflecting elements and Rician factor increases.

Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

Artificial Intelligence in Healthcare:Review and Prediction Case Studies

Artificial intelligence(AI)has been developing rapidly in recent years in terms of software algorithms,hardware implementation,and applications in a vast number of areas.In this review,we summarize the latest developments of applications of AI in biomedicine,including disease diagnostics,living assistance,biomedical information processing,and biomedical research.The aim of this review is to keep track of new scientific accomplishments,to understand the availability of technologies,to appreciate the tremendous potential of AI in biomedicine,and to provide researchers in related fields with inspiration.It can be asserted that,just like AI itself,the application of AI in biomedicine is still in its early stage.New progress and breakthroughs will continue to push the frontier and widen the scope of AI application,and fast developments are envisioned in the near future.Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.

Terahertz Band: Lighting up Next-Generation Wireless Communications

With the explosion of wireless data rates,the terahertz(THz)band(0.1–10 THz)is envisioned as a promising candidate to break the existing bandwidth bottleneck and satisfy the ever-increasing capacity demand.The THz wireless communications feature a number of attractive properties,such as potential terabit-per-second capacity and high energy efficiency.In this paper,an overview on the state-of-the-art THz communications is studied,with a special focus on key technologies of THz transceivers and THz communication systems.The recent progress on both electronic and photonic THz transmitters are presented,and then the THz receivers operating in direct-and heterodyne reception modes are individually surveyed.Based on the THz transceiver schemes,three kinds of THz wireless communication systems are reviewed,including solid-state electronic systems,photonics-assisted systems and all-photonics systems.The prospective key enabling technologies,corresponding challenges and research directions for lighting up high-speed THz communication systems are discussed as well.

Terahertz Orbital Angular Momentum: Generation, Detection and Communication

To accommodate the ever-increasing wireless capacity,the terahertz(THz)orbital angular momentum(OAM)beam that combines THz radiation and OAM technologies has attracted much attention recently,with contributing efforts to explore new dimensions in the THz region.In this paper,we provide an overview of the generation and detection techniques of THz-OAM beams,as well as their applications in communications.The principle and research status of typical generation and detection methods are surveyed,and the advantages and disadvantages of each method are summarized from a viewpoint of wireless communication.It is shown that developing novel THz components in generating,detecting and(de)multiplexing THz-OAM beams has become the key engine to drive this direction forward.Anyway,beneficial from the combination of infinite orthogonal modes and large bandwidth,THz-OAM beams will have great potential in delivering very large capacity in next generation wireless communications.

Delay-Optimal Random Access in Large-Scale Energy Harvesting IoT Networks Based on Mean Field Game

With energy harvesting capability, the Internet of things(IoT) devices transmit data depending on their available energy, which leads to a more complicated coupling and brings new technical challenges to delay optimization. In this paper,we study the delay-optimal random access(RA) in large-scale energy harvesting IoT networks. We model a two-dimensional Markov decision process(MDP)to address the coupling between the data and energy queues, and adopt the mean field game(MFG) theory to reveal the coupling among the devices by utilizing the large-scale property. Specifically, to obtain the optimal access strategy for each device, we derive the Hamilton-Jacobi-Bellman(HJB) equation which requires the statistical information of other devices.Moreover, to model the evolution of the states distribution in the system, we derive the Fokker-PlanckKolmogorov(FPK) equation based on the access strategy of devices. By solving the two coupled equations,we obtain the delay-optimal random access solution in an iterative manner with Lax-Friedrichs method. Finally, the simulation results show that the proposed scheme achieves significant performance gain compared with the conventional schemes.

Towards Converged Millimeter-Wave/Terahertz Wireless Communication and Radar Sensing

Converged communication and radar sensing systems have attained increasing attention in recent years.The development of converged radar-data systems is reviewed,with a special focus on millimeter/terahertz systems as a promising trend.Firstly,we present historical development and convergence technology concept for communication-radar systems,and highlight some emerging technologies in this area.We then provide an updated and comprehensive survey of several converged systems operating in different microwave and millimeter frequency bands,by providing some selective typical communication and radar sensing systems.In this part,we also summarize and compare the system performance in terms of maximum range/range resolution for radar mode and Bit Error Rate(BER)/wireless distance for communication mode.In the last section,the convergence of millimeter/terahertz communication-radar system is concluded by analyzing the prospect of millimeter-wave/terahertz technologies in providing ultrafast data rates and high resolution for our smart future.

Ultrasensitive skin‐like wearable optical sensors based on glass micro/nanofibers

Electronic skin,a class of wearable electronic sensors that mimic the functionalities of human skin,has made remarkable success in applications including health monitoring,human-machine interaction and electronic-biological interfaces.While electronic skin continues to achieve higher sensitivity and faster response,its ultimate performance is fundamentally limited by the nature of low-frequency AC currents.Herein,highly sensitive skin-like wearable optical sensors are demonstrated by embedding glass micro/nanofibers(MNFs)in thin layers of polydimethylsiloxane(PDMS).Enabled by the transition from guided modes into radiation modes of the waveguiding MNFs upon external stimuli,the skin-like optical sensors show ultrahigh sensitivity(1870 k·Pa^-1),low detection limit(7 mPa)and fast response(10μs)for pressure sensing,significantly exceeding the performance metrics of state-of-the-art electronic skins.Electromagnetic interference(EMI)-free detection of high-frequency vibrations,wrist pulse and human voice are realized.Moreover,a five-sensor optical data glove and a 2×2-MNF tactile sensor are demonstrated.These initial results pave the way toward a new category of optical devices ranging from ultrasensitive wearable sensors to optical skins.

Thermal conductivity characterization of ultra-thin silicon film using the ultra-fast transient hot strip method

Thermal conductivity is an important material parameter of silicon when studying the performance and reliability of devices or for guiding circuit design when considering heat dissipation, especially when the self-heating effect becomes prominent in ultra-scaled MOSFETs.The cross-plane thermal conductivity of a thin silicon film is lacking due to the difficulty in sensing high thermal conductivity in the vertical direction.In this paper, a feasible method that utilizes an ultra-fast electrical pulse within 20 μs combined with the hot strip technique is adopted.To the best of our knowledge, this is the first work that shows how to extract the cross-plane thermal conductivity of sub-50 nm(30 nm, 17 nm, and 10 nm)silicon films on buried oxide.The ratio of the extracted cross-plane thermal conductivity of the silicon films over the bulk value is only about 6.9%, 4.3%, and 3.8% at 300 K, respectively.As the thickness of the films is smaller than the phonon mean free path, the classical heat transport theory fails to predict the heat dissipation in nanoscale transistors.Thus, in this study, a ballistic model, derived from the heat transport equation based on extended-irreversible-hydrodynamics(EIT), is used for further investigation, and the simulation results exhibit good consistence with the experimental data.The extracted effective thermal data could provide a good reference for precise device simulations and thermoelectric applications.

Advection and Thermal Diode

We prove that under the condition of closed boundary to mass flux, pure advection is not a valid mechanism to make a practical thermal diode. Among the various designs of thermal diodes, many of them involve circulating fluid flow, such as in thermosyphons. However, those designs often employ natural convection, which is basically a nonlinear process. It thus remains unclear how the pure advection of temperature field induced by a decoupled velocity field influences the symmetry of heat transfer. Here we study three typical models with pure advection:one with open boundary, one with closed boundary at unsteady state, and one with closed boundary at steady state. It is shown that only the last model is practical, while it cannot become a thermal diode. Finally, a general proof is given for our claim by analyzing the diffusive reciprocity.

Joint User Selection and Resource Allocation for Fast Federated Edge Learning

By periodically aggregating local learning updates from edge users, federated edge learning (FEEL) is envisioned as a promising means to reap the benefit of local rich da?ta and protect users'privacy. However, the scarce wireless communication resource greatly limits the number of participated users and is regarded as the main bottleneck which hin?ders the development of FEEL. To tackle this issue, we propose a user selection policy based on data importance for FEEL system. In order to quantify the data importance of each user, we first analyze the relationship between the loss decay and the squared norm of gradi?ent. Then, we formulate a combinatorial optimization problem to maximize the learning effi?ciency by jointly considering user selection and communication resource allocation. By problem transformation and relaxation, the optimal user selection policy and resource alloca?tion are derived, and a polynomial-time optimal algorithm is developed. Finally, we deploy two commonly used deep neural network (DNN) models for simulation. The results validate that our proposed algorithm has strong generalization ability and can attain higher learning efficiency compared with other traditional algorithms.

用于5G多波束MIMO系统的先进低温共烧陶瓷封装的39 GHz双信道收发器芯片组

本文介绍了一种用于5G多输入多输出(MIMO)应用的39 GHz收发器前端芯片组。每个芯片包括两个可变增益的频率转换通道,可以同时支持两个独立波束,芯片还集成了一个本地振荡器链和数字模块,用于多芯片扩展和增益状态控制。为了提高射频性能,对前端系统的关键模块提出了几种电路级改进技术。此外,开发了一种先进的低温共烧陶瓷工艺来封装39 GHz双通道收发器芯片组,实现了低封装损耗和两个发射(TX)/接收(RX)通道之间的高隔离。进行了芯片级和系统级封装(SIP)测量,以演示收发器芯片组的性能。测量结果表明,TX SIP的最大增益为11 dB,饱和输出功率为10 dBm;RX SIP的最大增益为52 dB,噪声系数为5.4 dB,输出压缩点为7.2 dBm。该收发器的单通道通信链路测试表明,64正交调幅(QAM)调制的误差矢量幅度(EVM)为3.72%,频谱效率为3.25 bit·s^(−1)·Hz^(−1);256-QAM调制在1 m距离上的误差矢量幅度(EVM)为3.76%,频谱效率为3.9 bit·s^(−1)·Hz^(−1)。基于该芯片组,还开发了39 GHz多波束原型,用于执行5G毫米波应用的MIMO操作。单流和双流传输的空中通信链路表明,多波束原型机可以覆盖5~150 m的距离,吞吐量相当。

Satellite Internet of Things:challenges,solutions,and development trends

Satellite Internet of Things(IoT)is a promising way to provide seamless coverage to a massive number of devices all over the world,especially in remote areas not covered by cellular networks,e.g.,forests,oceans,mountains,and deserts.In general,satellite IoT networks take low Earth orbit(LEO)satellites as access points,which solves the problem of wide coverage,but leads to many challenging issues.We first give an overview of satellite IoT,with an emphasis on revealing the characteristics of IoT services.Then,the challenging issues of satellite IoT,i.e.,massive connectivity,wide coverage,high mobility,low power,and stringent delay,are analyzed in detail.Furthermore,the possible solutions to these challenges are provided.In particular,new massive access protocols and techniques are designed according to the characteristics and requirements of satellite IoT.Finally,we discuss several development trends of satellite IoT to stimulate and encourage further research in such a broad area.