OFDMA-Based Unsourced Random Access in LEO Satellite Internet of Things

This paper investigates the low earth orbit(LEO)satellite-enabled coded compressed sensing(CCS)unsourced random access(URA)in orthogonal frequency division multiple access(OFDMA)framework,where a massive uniform planar array(UPA)is equipped on the satellite.In LEO satellite communications,unavoidable timing and frequency offsets cause phase shifts in the transmitted signals,substantially diminishing the decoding performance of current terrestrial CCS URA receiver.To cope with this issue,we expand the inner codebook with predefined timing and frequency offsets and formulate the inner decoding as a tractable compressed sensing(CS)problem.Additionally,we leverage the inherent sparsity of the UPA-equipped LEO satellite angular domain channels,thereby enabling the outer decoder to support more active devices.Furthermore,the outputs of the outer decoder are used to reduce the search space of the inner decoder,which cuts down the computational complexity and accelerates the convergence of the inner decoding.Simulation results verify the effectiveness of the proposed scheme.

A 24−30 GHz 8-element dual-polarized 5G FR2 phased-array transceiver IC with 20.8-dBm TX OP1dB and 4.1-dB RX NF in 65-nm CMOS

This article presents an 8-element dual-polarized phased-array transceiver(TRX)front-end IC for millimeter-wave(mm-Wave)5G new radio(NR).Power enhancement technologies for power amplifiers(PA)in mm-Wave 5G phased-array TRX are discussed.A four-stage wideband high-power class-AB PA with distributed-active-transformer(DAT)power combining and multi-stage second-harmonic traps is proposed,ensuring the mitigated amplitude-to-phase(AM-PM)distortions across wide carrier frequencies without degrading transmitting(TX)power,gain and efficiency.TX and receiving(RX)switching is achieved by a matching network co-designed on-chip T/R switch.In each TRX element,6-bit 360°phase shifting and 6-bit 31.5-dB gain tuning are respectively achieved by the digital-controlled vector-modulated phase shifter(VMPS)and differential attenuator(ATT).Fabricated in 65-nm bulk complementary metal oxide semiconductor(CMOS),the proposed TRX demonstrates the measured peak TX/RX gains of 25.5/21.3 dB,covering the 24−29.5 GHz band.The measured peak TX OP1dB and power-added efficiency(PAE)are 20.8 dBm and 21.1%,respectively.The measured minimum RX NF is 4.1 dB.The TRX achieves an output power of 11.0−12.4 dBm and error vector magnitude(EVM)of 5%with 400-MHz 5G NR FR2 OFDM 64-QAM signals across 24−29.5 GHz,covering 3GPP 5G NR FR2 operating bands of n257,n258,and n261.

A Correlation-Based Stochastic Model for Massive MIMO Channel

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.

Tunable superconducting resonators via on-chip control of local magnetic field

Superconducting microwave resonators play a pivotal role in superconducting quantum circuits.The ability to finetune their resonant frequencies provides enhanced control and flexibility.Here,we introduce a frequency-tunable superconducting coplanar waveguide resonator.By applying electrical currents through specifically designed ground wires,we achieve the generation and control of a localized magnetic field on the central line of the resonator,enabling continuous tuning of its resonant frequency.We demonstrate a frequency tuning range of 54.85 MHz in a 6.21-GHz resonator.This integrated and tunable resonator holds great potential as a dynamically tunable filter and as a key component of communication buses and memory elements in superconducting quantum computing.

Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

Magnetic Nonreciprocity in a Hybrid Device of Asymmetric Artificial Spin-Ice-Superconductors

Controlling the size and distribution of potential barriers within a medium of interacting particles can unveil unique collective behaviors and innovative functionalities.We introduce a unique superconducting hybrid device using a novel artificial spin ice structure composed of asymmetric nanomagnets.This structure forms a distinctive superconducting pinning potential that steers unconventional motion of superconducting vortices,thereby inducing a magnetic nonreciprocal effect,in contrast to the electric nonreciprocal effect commonly observed in superconducting diodes.Furthermore,the polarity of the magnetic nonreciprocity is in situ reversible through the tunable magnetic patterns of artificial spin ice.Our findings demonstrate that artificial spin ice not only precisely modulates superconducting characteristics but also opens the door to novel functionalities,offering a groundbreaking paradigm for superconducting electronics.

RIS-Assisted Cell-Free MIMO: A Survey

Cell-free(CF)multiple-input multiple-output(MIMO)is a promising technique to enable the vision of ubiquitous wireless connectivity for next-generation network communications.Compared to traditional co-located massive MIMO,CF MIMO allows geographically distributed access points(APs)to serve all users on the same time-frequency resource with spatial multiplexing techniques,resulting in better performance in terms of both spectral efficiency and coverage enhancement.However,the performance gain is achieved at the expense of deploying more APs with high cost and power consumption.To address this issue,the recently proposed reconfigurable intelligent surface(RIS)technique stands out with its unique advantages of low cost,low energy consumption and programmability.In this paper,we provide an overview of RIS-assisted CF MIMO and its interaction with advanced optimization designs and novel applications.Particularly,recent studies on typical performance metrics such as energy efficiency(EE)and spectral efficiency(SE)are surveyed.Besides,the application of RIS-assisted CF MIMO techniques in various future communication systems is also envisioned.Additionally,we briefly discuss the technical challenges and open problems for this area to inspire research direction and fully exploit its potential in meeting the demands of future wireless communication systems.

352-Gbit/s single line rate THz wired transmission based on PS-4096QAM employing hollow-core fiber

We successfully demonstrate 32-Gbaud Probabilistically Shaped 4096-ary Quadrature Amplitude Modulation(PS-4096QAM)TeraHertz(THz)signal wired transmission at 325 GHz over the 1-m Hollow-Core Fiber(HCF)in a photon-assisted THz-wave communication system.By employing advanced Digital Signal Processing(DSP)and the PS technique,the 352-Gbit/s line rate(288-Gbit/s net rate)delivery with a net Spectral Efficiency(SE)of 9 bit/s/Hz is realized in the experiment,satisfying the 0.86-Normalized Generalized Mutual Information(NGMI)Low-Density Parity-Check(LDPC)threshold.

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.

Radio Communication Scenarios in 5G-Railways

With the rapid development of railways,especially high-speed railways,there is an increasingly urgent demand for new wireless communication system for railways.Taking the mature 5G technology as an opportunity,5G-railways(5G-R)have been widely regarded as a solution to meet the diversified demands of railway wireless communications.For the design,deployment and improvement of 5GR networks,radio communication scenario classification plays an important role,affecting channel modeling and system performance evaluation.In this paper,a standardized radio communication scenario classification,including 18 scenarios,is proposed for 5GR.This paper analyzes the differences of 5G-R scenarios compared with the traditional cellular networks and GSM-railways,according to 5G-R requirements and the unique physical environment and propagation characteristics.The proposed standardized scenario classification helps deepen the research of 5G-R and promote the development and application of the existing advanced technologies in railways.

SNWPM:A Siamese Network Based Wireless Positioning Model Resilient to Partial Base Stations Unavailable

Artificial intelligence(AI)models are promising to improve the accuracy of wireless positioning systems,particularly in indoor environments where unpredictable radio propagation channel is a great challenge.Although great efforts have been made to explore the effectiveness of different AI models,it is still an open problem whether these models,trained with the data collected from all base stations(BSs),could work when some BSs are unavailable.In this paper,we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work.Particularly,a Siamese Network based Wireless Positioning Model(SNWPM)is proposed to predict the location of mobile user equipment from channel state information(CSI)collected from 5G BSs.Furthermore,a Feature Aware Attention Module(FAAM)is introduced to reinforce the capability of feature extraction from CSI data.Experiments are conducted on the 2022 Wireless Communication AI Competition(WAIC)dataset.The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable.Compared with other AI models,the proposed SNWPM can reduce the positioning error by nearly 50%to more than 60%while using less parameters and lower computation resources.

A Provably Secure and PUF-Based Authentication Key Agreement Scheme for Cloud-Edge IoT

With the exponential growth of intelligent Internet of Things(IoT)applications,Cloud-Edge(CE)paradigm is emerging as a solution that facilitates resource-efficient and timely services.However,it remains an underlying issue that frequent end-edgecloud communication is over a public or adversarycontrolled channel.Additionally,with the presence of resource-constrained devices,it’s imperative to conduct the secure communication mechanism,while still guaranteeing efficiency.Physical unclonable functions(PUF)emerge as promising lightweight security primitives.Thus,we first construct a PUF-based security mechanism for vulnerable IoT devices.Further,a provably secure and PUF-based authentication key agreement scheme is proposed for establishing the secure channel in end-edge-cloud empowered IoT,without requiring pre-loaded master keys.The security of our scheme is rigorously proven through formal security analysis under the random oracle model,and security verification using AVISPA tool.The comprehensive security features are also elaborated.Moreover,the numerical results demonstrate that the proposed scheme outperforms existing related schemes in terms of computational and communication efficiency.

Physical-Layer Key Generation Based on Multipath Channel Diversity Using Dynamic Metasurface Antennas

Physical layer key generation(PKG)technology leverages the reciprocal channel randomness to generate the shared secret keys.The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels.To improve the wireless key generation rate,we propose a multipath channel diversity-based PKG scheme.Assisted by dynamic metasurface antennas(DMA),a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multipath parameter estimation.The proposed algorithm first estimates the angle of arrival(AOA)based on the reconfigurable radiation pattern of DMA,and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain.After multipath separation and parameter estimation,multi-dimensional independent path gains are utilized for generating secret keys.Finally,we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio(SNR)region.The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.

SBFT:A BFT Consensus Mechanism Based on DQN Algorithm for Industrial Internet of Thing

With the development and widespread use of blockchain in recent years,many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT).However,due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together,the mainstream blockchain system cannot be applied to IIoT scenarios.In order to solve these problems,this paper proposes SBFT(Speculative Byzantine Consensus Protocol),a flexible and scalable blockchain consensus mechanism for the Industrial Internet of Things.SBFT has a consensus process based on speculation,improving the throughput and consensus speed of blockchain systems and reducing communication overhead.In order to improve the compatibility and scalability of the blockchain system,we select some nodes to participate in the consensus,and these nodes have better performance in the network.Since multiple properties determine node performance,we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning(DQL)to solve it.Finally,we evaluate the performance of the scheme through simulation,and the simulation results prove the superiority of our scheme.

基于语音识别的电磁调控智能超表面

本文提出并实现了一种基于人类语音识别的智能超表面,用于对电磁波束进行可编程调控。该智能超表面平台由数字编码超表面、语音识别模块、单片机和数模转换器(DAC)电路组成,可根据预先存储的语音指令对电磁波进行智能控制。所构建的数字编码超表面包含6×6个超级子单元,每个超级子单元由4×4个嵌入了变容二极管的有源数字单元组成。语音识别模块配合DAC和单片机对语音指令进行识别,并生成对应的电压序列来控制超表面。此外,在超表面的设计过程中引入遗传算法,可有效优化超表面相位分布。为了验证智能超表面平台的性能,演示了雷达散射截面积缩减、涡旋波束生成和波束分裂三种典型功能。所提出的方案为调控电磁波提供了一种新的途径,并在电磁和声学通信之间架起了连接桥梁。

Federated Learning with Blockchain for Privacy-Preserving Data Sharing in Internet of Vehicles

Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised about the security and privacy of vehicle data.The mobility and real-time characteristics of vehicle data make data sharing more difficult in Io V.The emergence of blockchain and federated learning brings new directions.In this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io V.First,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data.Then,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality models.Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.

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 deep learning driven hybrid beamforming method for millimeter wave MIMO system

The hybrid beamforming is a promising technology for the millimeter wave MIMO system,which provides high spectrum efficiency,high data rate transmission,and a good balance between transmission performance and hardware complexity.The most existing beamforming systems transmit multiple streams by formulating multiple orthogonal beams.However,the Neural network Hybrid Beamforming(NHB)adopts a totally different strategy,which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation strategy.Driven by the Deep Learning(DL)hybrid beamforming,in this work,we propose a DL-driven nonorthogonal hybrid beamforming for the single-user multiple streams scenario.We first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate(BER)performance than the orthogonal hybrid beamforming even with the optimal power allocation.Inspired by the NHB,we propose a new DL-driven beamforming scheme to simulate the NHB behavior,which avoids time-consuming neural network training and achieves better BERs than traditional hybrid beamforming.Moreover,our simulation results demonstrate that the DL-driven nonorthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of subconnected schemes and imperfect Channel State Information(CSI).

Parity-check and G-matrix based intelligent early stopping criterion for belief propagation decoder for polar codes

The error correction performance of Belief Propagation(BP)decoding for polar codes is satisfactory compared with the Successive Cancellation(SC)decoding.Nevertheless,it has to complete a fixed number of iterations,which results in high computational complexity.This necessitates an intelligent identification of successful BP decoding for early termination of the decoding process to avoid unnecessary iterations and minimize the computational complexity of BP decoding.This paper proposes a hybrid technique that combines the“paritycheck”with the“G-matrix”to reduce the computational complexity of BP decoder for polar codes.The proposed hybrid technique takes advantage of the parity-check to intelligently identify the valid codeword at an early stage and terminate the BP decoding process,which minimizes the overhead of the G-matrix and reduces the computational complexity of BP decoding.We explore a detailed mechanism incorporating the parity bits as outer code and prove that the proposed hybrid technique minimizes the computational complexity while preserving the BP error correction performance.Moreover,mathematical formulation for the proposed hybrid technique that minimizes the computation cost of the G-matrix is elaborated.The performance of the proposed hybrid technique is validated by comparing it with the state-of-the-art early stopping criteria for BP decoding.Simulation results show that the proposed hybrid technique reduces the iterations by about 90%of BP decoding in a high Signal-to-Noise Ratio(SNR)(i.e.,3.5~4 dB),and approaches the error correction performance of G-matrix and conventional BP decoder for polar codes.

Multi-User MmWave Beam Tracking via Multi-Agent Deep Q-Learning

Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynamic environments.To reduce the overhead cost,we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method.With online learning of users’moving trajectories,the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate.Considering practical implementation,we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity.Furthermore,we propose a scalable state-action-reward design for scenarios with different users and antenna numbers.Simulation results verify the effectiveness of the designed method.