D-Fi: Domain adversarial neural network based CSI fingerprint indoor localization

Deep learning based channel state information(CSI)fingerprint indoor localization schemes need to collect massive labeled data samples for training,and the parameters of the deep neural network are used as the fingerprints.However,the indoor environment may change,and the previously constructed fingerprint may not be valid for the changed environment.In order to adapt to the changed environment,it requires to recollect massive amount of labeled data samples and perform the training again,which is labor-intensive and time-consuming.In order to overcome this drawback,in this paper,we propose one novel domain adversarial neural network(DANN)based CSI Fingerprint Indoor Localization(D-Fi)scheme,which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment.Specifically,the previous environment and changed environment are treated as the source domain and the target domain,respectively.The DANN consists of the classification path and the domain-adversarial path,which share the same feature extractor.In the offline phase,the labeled CSI samples are collected as source domain samples to train the neural network of the classification path,while in the online phase,for the changed environment,only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domainadversarial path to update parameters of the feature extractor.In this case,the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment.Experiment results show that for the changed localization environment,the proposed D-Fi scheme significantly outperforms the existing convolutional neural network(CNN)based scheme.

In-situ manipulation of wireless link with reinforcement-learningdriven programmable metasurface in indoor environment

It is of great importance to control flexibly wireless links in the modern society,especially with the advent of the Internet of Things(IoT),fifth-generation communication(5G),and beyond.Recently,we have witnessed that programmable metasurface(PM)or reconfigurable intelligent surface(RIS)has become a key enabling technology for manipulating flexibly the wireless link;however,one fundamental but challenging issue is to online design the PM's control sequence in a complicated wireless environment,such as the real-world indoor environment.Here,we propose a reinforcement learning(RL)approach to online control of the PM and thus in-situ improve the quality of the underline wireless link.We designed an inexpensive one-bit PM working at around 2.442 GHz and developed associated RL algorithms,and demonstrated experimentally that it is capable of enhancing the quality of commodity wireless link by a factor of about 10 dB and beyond in multiple scenarios,even if the wireless transmitter is in the glancing angle of the PM in the realworld indoor environment.Moreover,we also prove that our RL algorithm can be extended to improve the wireless signals of receivers in dual-receiver scenario.We faithfully expect that the presented technique could hold important potentials in future wireless communication,smart homes,and many other fields.

Broadband millimeter-wave metasurface antenna array withprinted ridge gap waveguide for high front-to-back ratio

A novel broadband metasurface (MTS) antenna array with high front-to-back ratio (FBR) is proposed for 28 GHz millimeter-wave applications. With slot pairs loaded on patch cells, an aperturecoupled slotted-mushroom MTS antenna is designed to obtain broadband radiation characteristicswith a compact size. To suppress the backward radiation of this antenna, the printed ridge gapwaveguide (PRGW) technology with a perfect magnetic conductor (PMC) shielding made ofmushroom unit-cells underneath the microstrip feeding line is applied. On this basis, a 4×4 MTSantenna array with the PRGW feed network is developed. Simulated results show that the FBR canbe highly improved by over 16 dB within the entire bandwidth. To validate the design, a prototypeof the proposed antenna is fabricated. Measured results show that an FBR greater than 28 dB canbe obtained over a 24% impedance bandwidth (from 24.9 GHz to 31.7 GHz) with the reflectioncoefficient less than 10 dB. The measured antenna gain ranges from 17 dBi to 19.2 dBi and thecorresponding measured aperture efficiencies are 35% and 45.6%. The measured results alsosuggest that the proposed MTS antenna possesses -35 dB cross-polarization level and stable radiation patterns. In addition, the proposed antenna remains a very low profile of 1.7 mm (0.17λ_(0) at28 GHz). All the achieved features indicate that the proposed MTS antenna is an importantcandidate for B5G and 6G wireless communication.

A lightweight hardware implementation of CRYSTALS-Kyber

The security of cryptographic algorithms based on integer factorization and discrete logarithm will be threatened by quantum computers in future.Since December 2016,the National Institute of Standards and Technology(NIST)has begun to solicit post-quantum cryptographic(PQC)algorithms worldwide.CRYSTALS-Kyber was selected as the standard of PQC algorithm after 3 rounds of evaluation.Meanwhile considering the large resource consumption of current implementation,this paper presents a lightweight architecture for ASICs and its implementation on FPGAs for prototyping.In this implementation,a novel compact modular multiplication unit(MMU)and compression/decompression module is proposed to save hardware resources.We put forward a specially optimized schoolbook polynomial multiplication(SPM)instead of number theoretic transform(NTT)core for polynomial multiplication,which can reduce about 74%SLICE cost.We also use signed number representation to save memory resources.In addition,we optimize the hardware implementation of the Hash module,which cuts off about 48%of FF consumption by register reuse technology.Our design can be implemented on Kintex-7(XC7K325T-2FFG900I)FPGA for prototyping,which occupations of 4777/4993 LUTs,2661/2765 FFs,1395/1452 SLICEs,2.5/2.5 BRAMs,and 0/0 DSP respective of client/server side.The maximum clock frequency can reach at 244 MHz.As far as we know,our design consumes the least resources compared with other existing designs,which is very friendly to resource-constrained devices.

Cooperative sensing,communication and computation resource allocation in mobile edge computing-enabled vehicular networks

The combination of integrated sensing and communication(ISAC)with mobile edge computing(MEC)enhances the overall safety and efficiency for vehicle to everything(V2X)system.However,existing works have not considered the potential impacts on base station(BS)sensing performance when users offload their computational tasks via uplink.This could leave insufficient resources allocated to the sensing tasks,resulting in low sensing performance.To address this issue,we propose a cooperative power,bandwidth and computation resource allocation(RA)scheme in this paper,maximizing the overall utility of Cramer-Rao bound(CRB)for sensing accuracy,computation latency for processing sensing information,and communication and computation latency for computational tasks.To solve the RA problem,a twin delayed deep deterministic policy gradient(TD3)algorithm is adopted to explore and obtain the effective solution of the RA problem.Furthermore,we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks,as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations.Simulation demonstrates that compared to other benchmark methods,TD3 achieves an average utility improvement of 97.11%and 27.90%in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.

Aerial RIS-aided physical layer security design for satellitecommunication among similar channels

Satellite communications and reconfigurable intelligent surface (RIS) are considered as twopromising technologies that can significantly improve the coverage and energy efficiency of futurewireless communication networks. The satellite communications security is often threatened dueto its broadcasting nature. To enhance the physical layer security (PLS) of satellite communications with channel similarity, an aerial RIS-aided dual full-duplex (DFD-ARIS) cooperative jamming method is presented in this paper. Specifically, unlike the existing works that relied onchannel difference, DFD-ARIS utilizes the channel similarity against the eavesdroppers with thehelp of ARIS. In addition, the power allocation is further studied in conjunction with the phasedesign of RIS to minimize the total power under the constraints of data rate, satellite powerlimitation and secrecy rate. Then, the closed-form solutions are achieved. Simulation results showthat the performance of the proposed scheme is superior to the traditional method.

Radio frequency based distributed system for noncooperative UAV classification and positioning

With the increasing popularity of civilian unmanned aerial vehicles(UAVs),safety issues arising from unsafe operations and terrorist activities have received growing attention.To address this problem,an accurate classification and positioning system is needed.Considering that UAVs usually use radio frequency(RF)signals for video transmission,in this paper,we design a passive distributed monitoring system that can classify and locate UAVs according to their RF signals.Specifically,three passive receivers are arranged in different locations to receive RF signals.Due to the noncooperation between a UAV and receivers,it is necessary to detect whether there is a UAV signal from the received signals.Hence,convolutional neural network(CNN)is proposed to not only detect the presence of the UAV,but also classify its type.After the UAV signal is detected,the time difference of arrival(TDOA)of the UAV signal arriving at the receiver is estimated by the cross-correlation method to obtain the corresponding distance difference.Finally,the Chan algorithm is used to calculate the location of the UAV.We deploy a distributed system constructed by three software defined radio(SDR)receivers on the campus playground,and conduct extensive experiments in a real wireless environment.The experimental results have successfully validated the proposed system.

A statistical sensing method by utilizing Wi-Fi CSI subcarriers:Empirical study and performance enhancement

In modern Wi-Fi systems,channel state information(CSI)serves as a foundational support for various sensing applications.Currently,existing CSI-based techniques exhibit limitations in terms of environmental adaptability.As such,optimizing the utilization of subcarrier CSI stands as a critical avenue for enhancing sensing performance.Within the OFDM communication framework,this work derives sensing outcomes for both detection and estimation by harnessing the CSI from every individual measured subcarrier,subsequently consolidating these outcomes.When contrasted against results derived from CSI based on specific extraction protocols or those obtained through weighted summation,the methodology introduced in this study offers substantial improvements in CSI-based detection and estimation performance.This approach not only underscores the significance but also serves as a robust exemplar for the comprehensive application of CSI.

ε-arithmetics for real vectors and linear processing of real vector-valued signals

In this paper,we introduce a new concept,namelyε-arithmetics,for real vectors of any fixed dimension.The basic idea is to use vectors of rational values(called rational vectors)to approximate vectors of real values of the same dimension withinεrange.For rational vectors of a fixed dimension m,they can form a field that is an mth order extension Q(α)of the rational field Q whereαhas its minimal polynomial of degree m over Q.Then,the arithmetics,such as addition,subtraction,multiplication,and division,of real vectors can be defined by using that of their approximated rational vectors withinεrange.We also define complex conjugate of a real vector and then inner product and convolutions of two real vectors and two real vector sequences(signals)of finite length.With these newly defined concepts for real vectors,linear processing,such as linear filtering,ARMA modeling,and least squares fitting,can be implemented to real vectorvalued signals with real vector-valued coefficients,which will broaden the existing linear processing to scalar-valued signals.

Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication,sensing and computing

The integration of communications,sensing and computing(I-CSC)has significant applications in vehicular ad hoc networks(VANETs).A roadside unit(RSU)plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication.Due to the constraints of limited resources,RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method.However,direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue.In this paper,we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks.The renormalization method is compared with two deployment schemes:genetic algorithm(GA)and memetic framework-based optimal RSU deployment(MFRD),to verify the improvement of communication performance.Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

BloomDT-An improved privacy-preserving decision tree inference scheme

Outsourcing decision tree models to cloud servers can allow model providers to distribute their models at scale without purchasing dedicated hardware for model hosting.However,model providers may be forced to disclose private model details when hosting their models in the cloud.Due to the time and monetary investments associated with model training,model providers may be reluctant to host their models in the cloud due to these privacy concerns.Furthermore,clients may be reluctant to use these outsourced models because their private queries or their results may be disclosed to the cloud servers.In this paper,we propose BloomDT,a privacy-preserving scheme for decision tree inference,which uses Bloom filters to hide the original decision tree's structure,the threshold values of each node,and the order in which features are tested while maintaining reliable classification results that are secure even if the cloud servers collude.Our scheme's security and performance are verified through rigorous testing and analysis.

An electromechanically reconfigurable intelligent surface for enhancing sub-6G wireless communication signal

Reconfigurable intelligent surface(RIS)is emerged as a promising technique to solve the challenges faced by future wireless communication networks.Although the most commonly used electrically-controlled RISs can achieve millisecond-scale speed of dynamic switch,they have a large number of microwave circuit elements(such as PIN diodes or varactors)which will bring non-negligible insertion loss,and the complicity of the bias network to electrically addressing each element will increase with the expansion of the RIS aperture.Aiming at further reducing the fabrication cost and power consumption,herein an electromechanical RIS used for sub-6G wireless communication is proposed.The electromechanical RIS is designed with a passive metasurface and step-motor driver modules,providing simultaneous high-efficiency reflection(over 80%)and continuous reflection phase coverage of 360.Through electromechanical control,the RIS system can realize different reflective wavefront shaping,and has been employed in the indoor sub-6G wireless environment demonstrating a maximum signal improvement of 8.3 dB.The proposed electromechanical RIS is particularly useful for wireless signal enhancement in static blind area,and has the obvious advantage of not requiring continuous power supply after the RIS being regulated.Therefore,it greatly reduces the overall cost and power consumption which may have potentials in indoor application scenarios for improving wireless communication performance.

Constructions of correlation immnue S-boxes with high nonlinearity

S-boxes play a central role in the design of symmetric cipher schemes.For stream cipher appli-cations,an s-box should satisfy several criteria such as high nonlinearity,balanceness,correlation immunity,and so on.In this paper,by using disjoint linear codes,a class of s-boxes possessing high nonlinearity and 1st-order correlation immunity is given.It is shown that the constructed correlation immune S-boxes can possess currently best known nonlinearity,which is confirmed by the example 1st-order correlation immune(12,3)s-box with nonlinearity 2000.In addition,two other frameworks concerning the criteria of balanced and resiliency are obtained respectively.

A heuristic mixed real-time task allocation of virtual utilization in multi-core processor

Multi-core processor is widely used as the running platform for safety-critical real-time systems such as spacecraft,and various types of real-time tasks are dynamically added at runtime.In order to improve the utilization of multi-core processors and ensure the real-time performance of the system,it is necessary to adopt a reasonable real-time task allocation method,but the existing methods are only for single-core processors or the performance is too low to be applicable.Aiming at the task allocation problem when mixed real-time tasks are dynamically added,we propose a heuristic mixed real-time task allocation algorithm of virtual utilization VU-WF(Virtual Utilization Worst Fit)in multi-core processor.First,a 4-tuple task model is established to describe the fixedpoint task and the sporadic task in a unified manner.Then,a VDS(Virtual Deferral Server)for serving execution requests of fixed-point task is constructed and a schedulability test of the mixed task set is derived.Finally,combined with the analysis of VDS's capacity,VU-WF is proposed,which selects cores in ascending order of virtual utilization for the schedulability test.Experiments show that the overall performance of VU-WF is better than available algorithms,not only has a good schedulable ratio and load balancing but also has the lowest runtime overhead.In a 4-core processor,compared with available algorithms of the same schedulability ratio,the load balancing is improved by 73.9%,and the runtime overhead is reduced by 38.3%.In addition,we also develop a visual multi-core mixed task scheduling simulator RT-MCSS(open source)to facilitate the design and verification of multi-core scheduling for users.As the high performance,VU-WF can be widely used in resource-constrained and safety-critical real-time systems,such as spacecraft,self-driving cars,industrial robots,etc.

Generation and regulation of two-dimensional autofocusing Airy beams based on holographic metasurfaces

This paper proposes a new method to generate a two-dimensional(2D)Airy beam and Airy autofocusing beam by using the scalar holographic metasurface with amplitude-phase modulation in the microwave band.The proposed holographic metasurface comprises subwavelength patch unit cells with a period of fewer than 1/8 wavelengths,which means that it has the finer sampling for electromagnetic waves and can simultaneously achieve precise modulations for the amplitude and phase of electromagnetic waves.Firstly,the 2D-Airy beam with quasi-non-diffraction and selfbending characteristics is generated,from which the holographic metasurface is designed to realize four different 2D-Airy beams with the same focus,achieving the 2D-Airy autofocusing beam in the microwave frequency.The holographic metasurface for Airy beam generation has high efficiency and an ultra-lower profile.Meanwhile,for applying the Airy beam in wireless power transfer(WPT),the efficiency of the generated Airy beam and Airy autofocusing beam is calculated for the first time in the microwave field.The simulation results show that the efficiency of the 2D-Airy beam can reach 66%at 150 mm away from the metasurface,while the efficiency of the 2D-Airy autofocusing beam at the focus,which is 280 mm from the metasurface,can reach 35%.The theoretical,simulated,and measured results show that the proposed method and holographic metasurfaces can flexibly achieve the special characteristics of self-autofocusing and self-bending Airy beams in the microwave domain,providing an effective path for wireless power transfer(WPT)scenario with radial obstructions.

Modeling and coverage analysis of heterogeneous sub-6GHz-millimeter wave networks

The joint adoption of sub-6GHz and millimeter wave(mmWave)technology can prevent the blind spots of coverage,enabling comprehensive coverage while realizing high-speed communication rate.According to the sensitivity of mmWave,base stations should be more densely deployed,which is not well described by existing Poisson hole process(PHP)and the Poisson point process(PPP)models.This paper establishes a sub-6GHz and mmWave hybrid heterogeneous cellular network based on the modified Poisson hole process(MPHP).In our proposed model,the sub-6GHz base stations follow the PPP,and the mmWave base stations(MBSs)follow MPHP distribution.The expressions of the coverage probability are derived by using the interference calculation method of integrating the nearest sector exclusion area.Our theoretical analysis has been verified through simulation results,suggesting that the increase in the cell radius decreases the coverage probability of signal-to-interference-plus-noise ratio(SINR),whereas the increase in the sector parameter has the opposite effect.The variation of sub-6GHz base stations(SBSs)density imposes more significant impact than the MBSs on the SINR coverage probability.In addition,the decrease in MBSs density will reduce the average bandwidth allocated to the user equipment(UE),thus reducing the rate coverage probability.

Data security and privacy computing in artificial intelligence

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,problem-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse industries.The development of AI,such as machine learning and deep learning,has revolutionized data processing and analysis.This transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized recommendations.However,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

A survey on membership inference attacks and defenses in machine learning

Membership inference(MI)attacks mainly aim to infer whether a data record was used to train a target model or not.Due to the serious privacy risks,MI attacks have been attracting a tremendous amount of attention in the research community.One existing work conducted-to our best knowledge the first dedicated survey study in this specific area:The survey provides a comprehensive review of the literature during the period of 2017~2021(e.g.,over 100 papers).However,due to the tremendous amount of progress(i.e.,176 papers)made in this area since 2021,the survey conducted by the one existing work has unfortunately already become very limited in the following two aspects:(1)Although the entire literature from 2017~2021 covers 18 ways to categorize(all the proposed)MI attacks,the literature during the period of 2017~2021,which was reviewed in the one existing work,only covered 5 ways to categorize MI attacks.With 13 ways missing,the survey conducted by the one existing work only covers 27%of the landscape(in terms of how to categorize MI attacks)if a retrospective view is taken.(2)Since the literature during the period of 2017~2021 only covers 27%of the landscape(in terms of how to categorize),the number of new insights(i.e.,why an MI attack could succeed)behind all the proposed MI attacks has been significantly increasing since year 2021.As a result,although none of the previous work has made the insights as a main focus of their studies,we found that the various insights leveraged in the literature can be broken down into 10 groups.Without making the insights as a main focus,a survey study could fail to help researchers gain adequate intellectual depth in this area of research.In this work,we conduct a systematic study to address these limitations.In particular,in order to address the first limitation,we make the 13 newly emerged ways to categorize MI attacks as a main focus on the study.In order to address the second limitation,we provide-to our best knowledge-the first review of the various insights leveraged in the entire literature.We found that the various insights leveraged in the literature can be broken down into 10 groups.Moreover,our survey also provides a comprehensive review of the existing defenses against MI attacks,the existing applications of MI attacks,the widely used datasets(e.g.,107 new datasets),and the eva luation metrics(e.g.,20 new evaluation metrics).