查看更多>>摘要：Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features.Differ-ent from the adaptive signal processing at the receiver in adaptive radar,the cognitive radar realizes closed-loop adaptive policy adjustment of both transmitter and receiver in the continuous interaction with the en-vironment.As a networked radar may significantly enhance the flexibility and robustness than its mono-static counterpart,the wireless networked cognitive radar(WNCR)attracts increasing research.This arti-cle firstly reviews the concept and development of cog-nitive radar,especially the related researches of net-worked cognitive radar.Then,the co-design of cog-nitive radar and communication is investigated.Al-though the communication quality between radar sens-ing nodes is the premise of detection,tracking,imag-ing and anti-jamming performance of the WNCR,the latest researches seldom consider the communication architecture design for WNCR.Therefore,this arti-cle mainly focuses on the proposal of WNCR concept based on the researches of cognitive radar and ana-lyzes research challenges of WNCR system in prac-tical application,and the corresponding guidelines are proposed to inspire future research.
查看更多>>摘要：The global Internet is composed of more than 70,000 autonomous domain networks intercon-nected through the Border Gateway Protocol(BGP).Studying the ecological evolution of BGP network is of great significance for analyzing the evolution trend of the global Internet.This paper focuses on the evolution of Country-Level BGP network ecosystems in 24 years,and innovatively studies the relationship between Country-Level BGP network and economy,breaking through the limitations of traditional research that only focuses on BGP network.The results re-vealed that the number of global BGP networks has increased by nearly 23 times and that network inter-connection has increased nearly 80 times over in 24 years.It was found that the growth of the global BGP network ecosystem has slowed overall due to ma-jor global security events,although the BGP network ecosystem in some Southeast Asian countries is de-veloping against the trend.At the same time,there is a significant positive correlation between the BGP network ecology and the national economy in the time dimension;there is a strong positive correlation in the spatial dimension,but the trend is weakening year by year.
查看更多>>摘要：In mmWave massive multiple-input multiple-output(MIMO)communication systems,the extension of low-complexity narrowband precoding schemes to be operated on wideband systems un-der frequency-selective channels remains an impor-tant challenge at the current time.This paper inves-tigates a low complexity wideband hybrid precoding scheme for mmWave massive MIMO multicarrier sys-tems under a single-user,fully-connected hybrid archi-tecture.We show that the radio frequency(RF)pre-coding/combining vectors can be directly derived from the eigenvectors of the optimal fully-digital covariance matrix over all subcarriers in order to maximize the sum rate of spectral efficiency.We also suggest a new method that iteratively reduces the residual error be-tween the covariance matrix and the sum of products of precoding matrices over all the subcarriers to im-prove the performance in the case where the number of RF chains is higher than the number of streams.The results of the simulation show that the proposed schemes'complexity is low compared to the present methods,and their performance can almost reach the upper bound achieved by the optimal full-baseband design.
查看更多>>摘要：Recently,cell-free(CF)massive multiple-input multiple-output(MIMO)becomes a promising architecture for the next generation wireless commu-nication system,where a large number of distributed access points(APs)are deployed to simultaneously serve multiple user equipments(UEs)for improved performance.Meanwhile,a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network.In this paper,taking into account the more realistic mobility scenarios,we pro-pose a hybrid small-cell(SC)and clustered CF mas-sive MIMO system through classifications of the UEs and APs,and constructing the corresponding pairs to run in SC or CF mode.A joint initial AP selection of this paradigm for all the UEs is firstly proposed,which is based on the statistics of estimated channel.Then,closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect.We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate.Nu-merical results demonstrate the performance gains and effective rates balancing of the proposed system.
查看更多>>摘要：Hybrid precoder design is a key tech-nique providing better antenna gain and reduced hard-ware complexity in millimeter-wave(mmWave)mas-sive multiple-input multiple-output(MIMO)systems.In this paper,Gaussian Mixture learned approximate message passing(GM-LAMP)network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems.Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit(OMP)and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high.This drawback can be addressed using classical iter-ative algorithms such as approximate message pass-ing(AMP),which has comparatively low computa-tional complexity.The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders.In this paper,the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP(LAMP)network,and is further enhanced as GM-LAMP network using the concept of Gaussian Mix-ture distribution of the hybrid precoders.The simula-tion results show that the proposed GM-LAMP net-work achieves optimal hybrid precoder design with enhanced achievable rates,better accuracy and low computational complexity compared to the existing al-gorithms.
查看更多>>摘要：The massive connectivity and limited en-ergy pose significant challenges to deploy the enor-mous devices in energy-efficient and environmentally friendly in the Internet of Things(IoT).Motivated by these challenges,this paper investigates the energy ef-ficiency(EE)maximization problem for downlink co-operative non-orthogonal multiple access(C-NOMA)systems with hardware impairments(HIs).The base station(BS)communicates with several users via a half-duplex(HD)amplified-and-forward(AF)relay.First,we formulate the EE maximization problem of the system under HIs by jointly optimizing transmit power and power allocated coefficient(PAC)at BS,and transmit power at the relay.The original EE max-imization problem is a non-convex problem,which is challenging to give the optimal solution directly.First,we use fractional programming to convert the EE max-imization problem as a series of subtraction form sub-problems.Then,variable substitution and block coor-dinate descent(BCD)method are used to handle the sub-problems.Next,a resource allocation algorithm is proposed to maximize the EE of the systems.Fi-nally,simulation results show that the proposed algo-rithm outperforms the downlink cooperative orthogo-nal multiple access(C-OMA)scheme.
查看更多>>摘要：Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent years.Many research studies have achieved splen-did results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of things.Nevertheless,most of these adopted studies have some shortcomings in the ma-chine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth se-quential computation.Therefore,in this paper,we propose a deep-learning approach based solely on at-tention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi signals.The Sole-SAM was deployed to learn the features representing different activities and mo-tions from the raw CSI data.Experiments were carried out to evaluate the performance of the proposed Sole-SAM architecture.The experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolu-tions like Long Short-Term Memory(LSTM)and Re-current Neural Network(RNN).Sole-SAM archived a 0.94％accuracy level,which is 0.04％better than RNN and 0.02％better than LSTM.
查看更多>>摘要：This paper considers link scheduling in a wireless network comprising of two types of nodes:(i)hybrid access points(HAPs)that harvest solar en-ergy,and(ii)devices that harvest radio frequency(RF)energy whenever HAPs transmit.Our aim is to de-rive the shortest possible link schedule that determines the transmission time of inter-HAPs links,and uplinks from devices to HAPs.We first outline a mixed in-teger linear program(MILP),which can be run by a central node to determine the optimal schedule and transmit power of HAPs and devices.We then out-line a game theory based protocol called Distributed Schedule Minimization Protocol(DSMP)that is run by HAPs and devices.Advantageously,it does not require causal energy arrivals and channel gains in-formation.Our results show that DSMP produces schedule lengths that are at most 1.99x longer than the schedule computed by MILP.
查看更多>>摘要：Satellite-to-ground terahertz communica-tion is limited by the power of signal source and an-tenna gain level,and has large path loss,which is dif-ficult to implement.In this paper,a feasible scheme of satellite-to-ground terahertz communication using High Altitude Platforms(HAPs)as relay is presented,and the path loss on terahertz communication links is modeled and analyzed.Combined with the path loss model,the transmission loss along HAP-to-ground paths under different seasons and complex weather en-vironment in Ali,Xizang,China is calculated.The re-sults show that the transmission characteristics of tera-hertz waves in winter and summer are significantly dif-ferent,mainly reflected in the number and bandwidth of usable atmospheric windows.Furthermore,the ad-ditional attenuation caused by the typical sand dust and ice cloud environment on terahertz band can reach 6.1 dB and 1.9 dB at the maximum respectively.With the aid of high gain antenna,the usable communica-tion frequencies of the HAP-to-ground links in winter are significantly more than those in summer.When the transmitting and receiving antenna gain is 40 dBi respectively,the usable communication frequency can reach 1.35 THz in winter,while it is limited to less than 1 THz in summer,up to 0.493 THz.
查看更多>>摘要：Deep neural networks(DNNs)are poten-tially susceptible to adversarial examples that are ma-liciously manipulated by adding imperceptible pertur-bations to legitimate inputs,leading to abnormal be-havior of models.Plenty of methods have been pro-posed to defend against adversarial examples.How-ever,the majority of them are suffering the follow-ing weaknesses:1)lack of generalization and prac-ticality.2)fail to deal with unknown attacks.To ad-dress the above issues,we design the adversarial na-ture eraser(ANE)and feature map detector(FMD)to detect fragile and high-intensity adversarial examples,respectively.Then,we apply the ensemble learning method to compose our detector,dealing with adver-sarial examples with diverse magnitudes in a divide-and-conquer manner.Experimental results show that our approach achieves 99.30％and 99.62％Area un-der Curve(AUC)scores on average when tested with various Lp norm-based attacks on CIFAR-10 and Im-ageNet,respectively.Furthermore,our approach also shows its potential in detecting unknown attacks.
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