Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.

A Single-Board Integrated Millimeter-Wave Asymmetric Full-Digital Beamforming Array for B5G/6G Applications

In this article,a single-board integrated millimeter-wave(mm-Wave)asymmetric full-digital beamforming(AFDBF)array is developed for beyond-fifth-generation(B5G)and sixth-generation(6G)communications.The proposed integrated array effectively addresses the challenge of arranging a large number of ports in a full-digital array by designing vertical connections in a three-dimensional space and successfully integrating full-digital transmitting(Tx)and receiving(Rx)arrays independently in a single board.Unlike the traditional symmetric array,the proposed asymmetric array is composed of an 8×8 Tx array arranged in a square shape and an 8+8 Rx array arranged in an L shape.The center-to-center distance between two adjacent elements is 0.54k0 for both the Tx and Rx arrays,where k0 is the free-space wavelength at 27 GHz.The proposed AFDBF array possesses a more compact structure and lower system hardware cost and power consumption compared with conventional brick-type full-digital arrays.In addition,the energy efficiency of the proposed AFDBF array outperforms that of a hybrid beamforming array.The measurement results indicate that the operating frequency band of the proposed array is 24.25–29.50 GHz.An eight-element linear array within the Tx array can achieve a scanning angle ranging from-47°to+47°in both the azimuth and the elevation planes,and the measured scanning range of each eight-element Rx array is–45°to+45°.The measured maximum effective isotropic radiated power(EIRP)of the eight-element Tx array is 43.2 dBm at 28.0 GHz(considering the saturation point).Furthermore,the measured error vector magnitude(EVM)is less than 3%when 64-quadrature amplitude modulation(QAM)waveforms are used.

Vector Approximate Message Passing with Sparse Bayesian Learning for Gaussian Mixture Prior

Compressed sensing(CS)aims for seeking appropriate algorithms to recover a sparse vector from noisy linear observations.Currently,various Bayesian-based algorithms such as sparse Bayesian learning(SBL)and approximate message passing(AMP)based algorithms have been proposed.For SBL,it has accurate performance with robustness while its computational complexity is high due to matrix inversion.For AMP,its performance is guaranteed by the severe restriction of the measurement matrix,which limits its application in solving CS problem.To overcome the drawbacks of the above algorithms,in this paper,we present a low complexity algorithm for the single linear model that incorporates the vector AMP(VAMP)into the SBL structure with expectation maximization(EM).Specifically,we apply the variance auto-tuning into the VAMP to implement the E step in SBL,which decrease the iterations that require to converge compared with VAMP-EM algorithm when using a Gaussian mixture(GM)prior.Simulation results show that the proposed algorithm has better performance with high robustness under various cases of difficult measurement matrices.

Federated Edge Learning for the Wireless Physical Layer:Opportunities and Challenges

Deep learning(DL)has been applied to the physical layer of wireless communication systems,which directly extracts environment knowledge from data and outperforms conventional methods either in accuracy or computation complexity.However,most related research works employ centralized training that inevitably involves collecting training data from edge devices.The data uploading process usually results in excessive communication overhead and privacy disclosure.Alternatively,a distributed learning approach named federated edge learning(FEEL)is introduced to physical layer designs.In FEEL,all devices collaborate to train a global model only by exchanging parameters with a nearby access point.Because all datasets are kept local,data privacy is better protected and data transmission overhead can be reduced.This paper reviews the studies on applying FEEL to the wireless physical layer including channel state information acquisition,transmitter,and receiver design,which represent a paradigm shift of the DL-based physical layer design.In the meantime they also reveal several limitations inherent in FEEL,particularly when applied to the wireless physical layer,thus motivating further research efforts in the field.

Set Stabilization of Large-Scale Stochastic Boolean Networks:A Distributed Control Strategy

Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fraction of agents.

Simultaneous wireless information and power transfer transmitting architecture based on asynchronous space-time-coding digital metasurface[Invited]

Simultaneous wireless information and power transfer(SWIPT)architecture is commonly applied in wireless sensors or Internet of Things(IoT)devices,providing both wireless power sources and communication channels.However,the traditional SWIPT transmitter usually suffers from cross-talk distortion caused by the high peak-to-average power ratio of the input signal and the reduction of power amplifier efficiency.This paper proposes a SWIPT transmitting architecture based on an asynchronous space-time-coding digital metasurface(ASTCM).High-efficiency simultaneous transfer of information and power is achieved via energy distribution and information processing of the wireless monophonic signal reflected from the metasurface.We demonstrate the feasibility of the proposed method through theoretical derivations and experimental verification,which is therefore believed to have great potential in wireless communications and the IoT devices.

Distributed online bandit tracking for Nash equilibrium under partial-decision information setting

This paper is concerned with a Nash equilibrium(NE)tracking issue in online games with bandit feedback,where cost functions vary with time and agents only have access to the values of these functions at two points during each round.A partial-decision information setting is considered,in which agents have only access to the decisions of their neighbors.The primary objective of this paper is to develop a distributed online NE tracking algorithm that ensures sublinear growth of regret with respect to the total round T,under both the bandit feedback and partial-decision information setting.By utilizing a two-point estimator together with the leader-following consensus method,a new distributed online NE tracking algorithm is established with the estimated gradient and local estimated decisions based on the projection gradient-descent method.Moreover,sufficient conditions are derived to guarantee an improved upper bound of dynamic regret compared to existing bandit algorithms.Finally,a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

Exponential Convergence of Primal-Dual Dynamical System for Linear Constrained Optimization

Dear editor,Primal-dual dynamics(PDD)and its variants are prominent first-order continuous-time algorithms to determine the primal and dual solutions of a constrained optimization problem(COP).Due to the simple structure,they have received widespread attention in various fields,such as distributed optimization[1],power systems[2],and wireless communication[3].In view of their wide applications,there are numerous theoretic studies on the convergence properties of PDD and its variants,including the exponential stability analysis[4]-[9].

A chattering free consensus controller for multiple Lur'e systems with a non-autonomous leader and directed switching topology

Achieving asymptotical cooperative goal for multi-agent systems(MASs)with non-autonomous leaders(i.e.,leaders with nonzero inputs)is a critical but challenging issue.Traditional approach is to use discontinuous controllers which may cause chattering phenomenon in practical applications.How to achieve the asymptotical goal via a chattering free cooperative controller remains to be open so far.In this paper,an adaptive continuous controller is designed to achieve zero error consensus tracking in multiple Lur’e systems with a non-autonomous leader under directed switching topology.Firstly,an unknown input observer(UIO)based on relative outputs is given to estimate the relative full states.Then an adaptive continuous controller is designed by introducing a decay function which remains positive into the term that plays the role of eliminating the impacts of leader’s nonzero inputs.Secondly,by using multiple Lyapunov functions(MLFs)technique,it is proven that zero error consensus tracking can be achieved if the average dwell time(ADT)is greater than a positive threshold.Finally,theoretical result is verified by performing simulations on Chua’s circuits.Compared with existing work,the proposed controller can not only achieve asymptotical consensus,but also is chattering free.

Six-channel programmable coding metasurface simultaneously for orthogonal circular and linear polarizations

Metasurfaces have intrigued long-standing research interests and developed multitudinous compelling applications owing to their unprecedented capability for manipulating electromagnetic waves,and the emerging programmable coding metasurfaces(PCMs)provide a real-time reconfigurable platform to dynamically implement customized functions.Nevertheless,most existing PCMs can only act on the single polarization state or perform in the limited polarization channel,which immensely restricts their practical application in multitask intelligent metadevices.Herein,an appealing strategy of the PCM is proposed to realize tunable functions in co-polarized reflection channels of orthogonal circularly polarized waves and in co-polarized and cross-polarized reflection channels of orthogonal linearly polarized waves from 9.0 to 10.5 GHz.In the above six channels,the spindecoupled programmable meta-atom can achieve high-efficiency reflection and 1-bit digital phase modulation by selecting the specific ON/OFF states of two diodes,and the phase coding sequence of the PCM is dynamically regulated by the field-programmable gate array to generate the desired function.A proof-of-concept prototype is constructed to verify the feasibility of our methodology,and numerous simulation and experimental results are in excellent agreement with the theoretical predictions.This inspiring design opens a new avenue for constructing intelligent metasurfaces with higher serviceability and flexibility,and has tremendous application potential in communication,sensing,and other multifunctional smart metadevices.

Stretchable and self-healable spoof plasmonic meta-waveguide for wearable wireless communication system

Microwave transmission lines in wearable systems are easily damaged after frequent mechanical deformation,posing a severe threat to wireless communication.Here,we report a new strategy to achieve stretchable microwave transmission lines with superior reliability and durability by integrating a self-healable elastomer with serpentine-geometry plasmonic meta-waveguide to support the spoof surface plasmon polariton(SSPP).After mechanical damage,the self-healable elastomer can autonomously repair itself to maintain the electromagnetic performance and mechanical strength.Meanwhile,the specially designed SSPP structure exhibits excellent stability and damage resistance.Even if the self-healing process has not been completed or the eventual repair effect is not ideal,the spoof plasmonic meta-waveguide Can still maintain reliable performance.Self-healing material enhances strength and durability,while the SSPP improves stability and gives more tolerance to the self-healing process.Our design coordinates the structural design with material synthesis to maximize the advantages of the SSPP and self-healing material,signifcantly improving the relability and durability of stretchable microwave transmission lines.We also perform communication quality experiments to demonstrate the potential of the proposed meta-waveguide as interconnects in future body area network systems.

Arbitrarily rotating polarization direction and manipulating phases in linear and nonlinear ways using programmable metasurface

Independent controls of various properties of electromagnetic(EM)waves are crucially required in a wide range of applications.Programmable metasurface is a promising candidate to provide an advanced platform for manipulating EM waves.Here,we propose an approach that can arbitrarily control the polarization direction and phases of reflected waves in linear and nonlinear ways using a stacked programmable metasurface.Further,we extend the space-timecoding theory to incorporate the dimension of polarization,which provides an extra degree of freedom for manipulating EM waves.As proof-of-principle application examples,we consider polarization rotation,phase manipulation,and beam steering at linear and nonlinear frequencies.For validation,we design,fabricate,and measure a metasurface sample.The experimental results show good agreement with theoretical predictions and simulations.The proposed approach has a wide range of applications in various areas,such as imaging,data storage,and wireless communication.

A metasurface-based light-to-microwave transmitter for hybrid wireless communications

Signal conversion plays an important role in many applications such as communication,sensing,and imaging.Realizing signal conversion between optical and microwave frequencies is a crucial step to construct hybrid communication systems that combine both optical and microwave wireless technologies to achieve better features,which are highly desirable in the future wireless communications.However,such a signal conversion process typically requires a complicated relay to perform multiple operations,which will consume additional hardware/time/energy resources.Here,we report a light-to-microwave transmitter based on the time-varying and programmable metasurface integrated with a high-speed photoelectric detection circuit into a hybrid.Such a transmitter can convert a light intensity signal to two microwave binary frequency shift keying signals by using the dispersion characteristics of the metasurface to implement the frequency division multiplexing.To illustrate the metasurface-based transmitter,a hybrid wireless communication system that allows dual-channel data transmissions in a light-to-microwave link is demonstrated,and the experimental results show that two different videos can be transmitted and received simultaneously and independently.Our metasurface-enabled signal conversion solution may enrich the functionalities of metasurfaces,and could also stimulate new information-oriented applications.

Optically transparent graphene-based cognitive metasurface for adaptive frequency manipulation

Optically transparent microwave absorbing metasurfaces have shown great potential and are needed in multiple applications environments containing optical windows owing to their ability to reduce backscattering electromagnetic(EM)signals while keeping continuous optical observation.Meanwhile,they are also required to have adaptive EM manipulation capability to cope with complex and capricious EM environments.As a general approach,distributed circuit components,including positive-intrinsic-negative diodes and varactors and sensing components,are integrated with passive absorbing metasurfaces to realize adaptive control of microwave absorption.However,these circuit elements generally require bulky electrical wires and complex control circuits to regulate the operating state,resulting in the absorbing structures being optically opaque.Hence,it is a great challenge to realize self-operating absorbers while maintaining optical transparency.Here,we report an optically transparent cognitive metasurface made of patterned graphene sandwich structures and a radio frequency detector,which can achieve adaptive frequency manipulation to match incident EM waves.As a proof-of-principle application example,we realize a closed-loop automatic absorber system prototype of the proposed graphene metasurface with self-adaptive frequency variation,without any human intervention.The approach may facilitate other adaptive metadevices in microwave regime with high-level recognition and manipulation and,more generally,promote the development of intelligent stealth technologies.

Robust Fingerprint Localization Based on Massive MIMO Systems

Location-based services have become an important part of the daily life.Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment.In this paper,a single-site multiple-input multiple-output(MIMO)orthogonal frequency division multiplexing(OFDM)system is modeled,from which an angle delay channel power matrix(ADCPM)is extracted.Considering the changing environment,auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints.When the scattering environment has changed beyond a certain extent,the robustness will not be able to make up for the positioning error.Under this circumstance,an updating of the fingerprint database is imperative.A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed.Simulation results show the desirable performance of the proposed methods.

Channel Knowledge Map(CKM)-Assisted Multi-UAV Wireless Network:CKM Construction and UAV Placement

Channel knowledge map(CKM)has recently emerged as a viable new solution to facilitate the placement and trajectory optimization for unmanned aerial vehicle(UAV)communications,by exploiting the siteand location-specific radio propagation information.This paper investigates a CKM-assisted multi-UAV wireless network,by focusing on the construction and utilization of CKMs for multi-UAV placement optimization.First,we consider the CKM construction problem when data measurements for only a limited number of points are available.Towards this end,we exploit a data-driven interpolation technique,namely the Kriging method,to construct CKMs to characterize the signal propagation environments.Next,we study the multi-UAV placement optimization problem by utilizing the constructed CKMs,in which the multiple UAVs aim to optimize their placement locations to maximize the weighted sum rate with their respectively associated ground base stations(GBSs).However,the weighted sum rate function based on the CKMs is generally non-differentiable,which renders the conventional optimization techniques relying on function derivatives inapplicable.To tackle this issue,we propose a novel iterative algorithm based on derivative-free optimization,in which a series of quadratic functions are iteratively constructed to approximate the objective function under a set of interpolation conditions,and accordingly,the UAVs’placement locations are updated by maximizing the approximate function subject to a trust region constraint.Finally,numerical results are presented to validate the performance of the proposed designs.It is shown that the Kriging method can construct accurate CKMs for UAVs.Furthermore,the proposed derivative-free placement optimization design based on the Kriging-constructed CKMs achieves a weighted sum rate that is close to the optimal exhaustive search design based on ground-truth CKMs,but with much lower implementation complexity.In addition,the proposed design is shown to significantly outperform other benchmark schemes.

Planar dual-polarized millimeter-wave shared-aperture array antenna with high band isolation

A planar millimeter-wave shared-aperture array antenna is proposed and designed in this paper.By composing the substrate integrated waveguide(SIW)and the stripline,the K-band antenna is embedded inside the Ka-band antenna to achieve a smaller size and a low profile by sharing an aperture.The Ka-band antenna radiates through the parallel slot pairs on the surface of the SIW cavities with horizontal polar‐ization,while the K-band antenna radiates through the butterfly-shaped slots with vertical polarization,which are also designed on the surface.Then the two array antennas can radiate by sharing a common ap‐erture with high isolation.To verify this idea,a proto‐type of an 8×8 shared-aperture array antenna has been designed with center frequencies of 19 and 30 GHz and fabricated using multilayer printed circuit board(PCB)technology.

Revolutionary meta-imaging:from superlens to metalens

The refractive-lens technique has been well developed over a long period of evolution,offering powerful imaging functionalities,such as microscopes,telescopes,and spectroscopes.Nevertheless,the ever-growing requirements continue to urge further enhanced imaging capabilities and upgraded devices that are more compact for convenience.Metamaterial as a fascinating concept has inspired unprecedented new explorations in physics,material science,and optics,not only in fundamental researches but also novel applications.Along with the imaging topic,this paper reviews the progress of the flat lens as an important branch of metamaterials,covering the early superlens with super-diffraction capability and current hot topics of metalenses including a paralleled strategy of multilevel diffractive lenses.Numerous efforts and approaches have been dedicated to areas ranging from the new fascinating physics to feasible applications.This review provides a clear picture of the flat-lens evolution from the perspective of metamaterial design,elucidating the relation and comparison between a superlens and metalens,and addressing derivative designs.Finally,application scenarios that favor the ultrathin lens technique are emphasized with respect to possible revolutionary imaging devices,followed by conclusive remarks and prospects.

Metamaterials:From fundamental physics to intelligent design

Metamaterials are artificial structures with the ability to efficiently control light-field,attracting intensive attention in the past few decades.People have studied the working principles,design strategies,and fabrication methods of metamaterials,making this field cross and combine with many disciplines,including physics,material science,electronics,and chemistry.In recent years,with the rapid development of high-efficiency and multifunctional metasurfaces,which are a two-dimensional version of metamaterials,great efforts have been made to push this material to practical applications.In particular,the introduction of artificial intelligent(AI)algorithms enables metamaterials-based photonic devices that exhibit excellent performances and intelligent functionalities.In this review,we first introduce the basic concepts,working principles,design methods,and applications of metamaterials,and then focus on the rapidly developing metamaterials research combined with AI algorithms.Finally,we conclude this review with personal perspectives on the current problems and future directions of metamaterials research and developments.

DeceFL:a principled fully decentralized federated learning framework

Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently fragmented.Such a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy concerns.Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central client.Here we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning framework.It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly convex.Finally,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.