An Evolutionary Approach to Improve the Beam Steering Performance over Conventional Approach in CHAA

In this work,evolutionary algorithms are applied for the first time to achieve better radiation characteristics over the conventional beamforming algorithm in the concentric hexagonal antenna array(CHAA),which improves the performance of wireless communication.Multiple signal classification(MUSIC)algorithm is employed for direction of arrival(DoA)estimation.The conventional adaptive beam steering algorithm,least mean-square(LMS)algorithm,is used to steer the beam.Further,the proposed approach is employed by novel particle swarm optimization(NPSO)to reduce sidelobe level(SLL)even further.A six-ring CHAA with 126 elements for DoA estimation and beam steering is simulated.The simulation results of the MUSIC,LMS,NPSO,and particle swarm optimization(PSO)algorithms are provided for various DoAs.

Block Markov superposition transmission with pulse position modulation over free-space optical links

In FSO(Free-Space Optical)communications,performance of the communication systems is severely degraded by atmospheric turbulence.PPM(Pulse Position Modulation)is widely used in FSO communication systems owing to its high power efficiency.In this paper,we present a combination of the BMST(Block Markov Superposition Transmission)technique and the PPM scheme to improve the reliability of the transmission over FSO links.Based on analyzing an equivalent system,a lower bound on the bit-error-rate of the proposed scheme is presented.Extensive simulations are performed which show that the BMST-PPM system performs well under a wide range of turbulence conditions and improves the performance of the basic code.Simulation results also show that,the performance of the system with the sliding-window detection/decoding algorithm matches well with the lower bound in the low-error-rate region.

Atmospheric Ducting Effect in Wireless Communications:Challenges and Opportunities

Atmospheric ducting has a significant impact on electromagnetic wave propagation.Radio signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the atmosphere.Atmospheric ducting brings both challenges and opportunities to wireless communications.On one hand,the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel interference.On the other hand,a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight communications.In this article,the formation of the atmospheric duct and its effects on radio wave propagation are first overviewed.Then solutions and standardization activities in the 3rd Generation Partnership Project(3GPP)to mitigate atmospheric duct induced remote interference are presented.Finally,the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested.

Cooperative Jobs Dispatching in Edge Computing Network with Unpredictable Uploading Delay

In this paper,the cooperative jobs dispatching problem in an edge computing network with multiple access points(APs)and edge servers is considered.Due to the uncertain traffic in the network between APs and edge servers,the job uploading delay can not be predicted accurately.Specifically,the job arrivals at the APs,the job uploading delay from APs to edge servers and the job computation time at the edge servers are all modeled as random variables.Since each job dispatching decision will affect the system state in the future,we formulate the joint optimization of jobs dispatching at all the APs and all the scheduling time slots as an infinite-horizon Markov decision process(MDP).The minimization objective is a discounted measurement of the average processing time per job,including the uploading delay,the waiting time and the computation time at the edge servers.In this problem,the approximate MDP should be adopted to address the curse of dimensionality.Conventional low-complexity approximate solution of MDP is usually hard to predict the performance analytically.In this paper,a novel approximate MDP solution framework is proposed via one-step policy iteration over a baseline policy,where the analytical performance bound can be obtained.Moreover,since the expression of the approximate value function is derived,the value iteration in conventional methods can be eliminated,which can essentially reduce the computation complexity.It is shown by simulations that the proposed low-complexity algorithm has significantly better performance than various benchmark schemes.

Power Allocation Optimization of Multibeam High-Throughput Satellite Communication Systems

The increasing demands in terms of high data rate and quality of services over the hybrid satellite-terrestrial relay networks(HSTRN)have pushed for the development of millimeter-wave(mmWave)band high-throughput satellites(HTS)with multibeams.The next generation of mmWave multibeam HTS communication systems(HTSCS)is viewed as the backbone network to enhance the throughput of the HSTRN.The article first investigates the basic backbone topology architecture of HTSCS,and an M-state Markov channel for the Ka/Q/V band mmWave systems is reviewed.Then,we propose a long-term optimal power allocation scheme over two in-dependent and identical spot beams based on the partially observable Markov decision process(POMDP),which can partly mitigate the negative effects of severe weather conditions.The key conditions for selecting the optimal power allocation action in the multibeam HTSCS are given.Simulation results show that our POMDP-based power allocation scheme can enhance the long-term throughput of the HTSCS.

Deep Learning for Secure Transmission in Reconfigurable Intelligent Surface-Assisted Communications

This paper investigates the secure transmission for reconfigurable intelligent surface(RIS)-assisted wireless communication systems.In the studied model,one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point.Therefore,it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem.This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined as the ratio of the secure rate to the total energy consumption of the system via jointly optimizing the RIS reflection reflector as well as the number of RIS elements,which results in a non-convex optimization problem that is intractable to solve by traditional methods.To tackle this issue,a new algorithm by leveraging the advance of the established deep learning(DL)technique is proposed so as to find the optimal RIS reflection vector and determine the optimal number of RIS reflection elements.Simulation results show that the proposed method reaches 96%of the optimal secure energy efficiency of the genie-aided algorithm.

Distributed Throughput and Energy Efficient Resource Optimization When D2D and Massive MIMO Coexist

Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources.In this paper,we optimize the throughput and energy efficiency(EE)performance for the coexistence of two technologies that have been identified for the future cellular networks,namely,massive multiple-input multiple-output(MIMO)and network-assisted device-to-device(D2D)communications.In such a hybrid network,the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge.To this end,we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming(MINLP).We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance.It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88%of the average transmission rate and 86%of the energy efficiency performance of the optimal matching with lower complexity.

Adaptive Data-Driven Wideband Compressive Spectrum Sensing for Cognitive Radio Networks

This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure.On the contrary,a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements.Then,the spectrum occupancy is determined directly from the reconstructed support vector.To carry out this process,a data-driven methodology is utilized to obtain the mini-mum number of necessary samples required for support reconstruction,and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels.Following this phase,an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision.Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance.Furthermore,the remainder of the sensing time can be employed by secondary users for data transmission,thus leading to the enhancement of the total throughput of the CR network.

Securing acoustics-based short-range communication systems:an overview

Mobile devices such as smartphones and tablets have continued to grow in recent years.Nowadays,people rely on these ubiquitous smart devices and carry them everywhere in their daily lives.Acoustic signal,as a simple and prevalent transmitting vector for end-to-end communication,shows unique characteristics comparing with another popular communication method,i.e.,optical signal,especially on the applications performed over smart devices.Acoustic signal does not require line-of-sight when transmission,the computational power of most smart devices are sufficient to modulate/demodulate acoustic signal using software acoustic modem only,which can be easily deployed on current off-the-shelf smart devices.Therefore,many acoustics-based short range communication systems have been developed and are used in sensitive applications such as building access control and mobile payment system.However,past work shows that an acoustic eavesdropper snooping on the communication between a transmitter and its legitimate receiver can easily break their communication protocol and decode the transmitted information.To solve this problem,many solutions have been proposed to protect the acoustic signal against eavesdroppers.In this overview,we explore the designs of existing solutions,the corresponding implementations,and their methodologies to protect acoustic signal communication.For each dependable and secure acoustics-based short range communication system,we present the major technical hurdles to be overcome,the state-of-the-art,and also offer a vision of the future research issues on this promising technology.

Modulation Recognition of Radio Signals Based on Edge Computing and Convolutional Neural Network

Software defined radio(SDR)is a wireless communication technology that uses modern software to control the traditional“pure hardware circuit”.It can provide an effective and secure solution to the problem of building multi-mode,multi-frequency and multifunction wireless communication equipment.Although the concept and application of SDR have been studied a lot,there is little discussion about the operating efficiency of the established system.For the purpose of shortening the delay of mapping and reducing the high computing load in the cloud,a radio monitoring system based on edge computing is developed to achieve the flexible,extensible and real-time monitoring of high-performance SDR applications.To promote the edge intelligence of deep learning(DL)service deployment through edge computing(EC),we developed an edge intelligence algorithm of convolutional neural network(CNN)based on attention mechanism to carry out modulation recognition(MR)of the edge signal and make MR closer to the antenna terminal.Through the experiment of the system and the edge algorithm,this thesis verifies the effectiveness of the developed multifunction radio signal monitoring system.

Anchor Selection for Topology Inference and Routing in Wireless Sensor Networks

Anchor-based ad-hoc networks utilize hop measurements to generate a virtual coordinate system for topology inference and routing applications.A common problem with such coordinate system is its sensitivity to anchor placement.We present a general formulation to the anchor node selection problem.Then,we relax the optimization problem by deriving an upper-bound of the objective function.We finally propose an iterative algorithm that consists in choosing additional anchor nodes based on the connectivity information provided by the current anchor set.Numerical simulations indicate that our anchor selection method is robust to missing measurements and improves network topology inference and routing performance.

An Overview on Backscatter Communications

As a key low-power communication tech-nique,backscatter communications exploits the reflected or backscattered signals to transmit data,where the backscattered signals can be the reflection of ambient radio frequency(RF)signals,the RF signals from the dedicated carrier emitter or the signal photons in the non-classical quantum entangled pairs,etc.In the past 70 years,various kinds of backscatter communication systems have been developed,which will enable the low-power communications as required in the Internet of things(IoTs)and green communications.This article provides a historical view on the development and the research achievements on backscatter communications,including the fundamental principles,the applications,the challenges,and the potential research topics.This article will benefit the researchers and engineers concerning the area of backscatter communications,especially for applications in IoTs.

Game strategies for distributed denial of service defense in the Cloud of Things

Integration of the IoT(Internet of Things)with Cloud Computing,termed as the CoT(Cloud of Things)can help achieve the goals of the envisioned IoT and future Internet.In a typical CoT infrastructure,the data collected from wireless sensor networks and IoTs is transmitted through a SG(Smart Gateway)to the cloud.The bandwidth between an IoT access point and SG becomes a bottleneck for information transmission between the IoT and the cloud.We propose a novel game theory model to describe the CoT attacker,who expects to use minimum set and energy consumption of IoT attack devices to occupy as many bandwidth resources as possible in a given time period;and the defender,who expects to minimize false alarms.By analyzing this model,we have found that the game theory model is a non-cooperative and repeated incomplete information game,and Nash equilibrium is existent,perfected by the subgame.The best strategy for each stage of the attack is to adjust the attack link number dynamically based on the comparison results of value E and turning point E_(0)for each time period.At the same time,the defender adjusts the threshold valueβdynamically,based on the comparison results of the Load value and expected value ofαfor each time period.The simulation result shows that our strategy can significantly mitigate the harm of a distributed denial of service attack.

Full-Duplex Cooperative Relaying with Non-Linear Energy Harvesting for Vehicular Communication

This paper analyses the performance of fullduplex(FD)dual-hop vehicular cooperative network with decode-and-forward(DF)protocol.At FD relay nodes,we examine the effects of non-linear hybrid power time splitting(PTS)based energy harvesting(EH).All three nodes—source(S),relay(R),and destination(D)are assumed to be moving vehicles.The expressions for the system's outage probability(OP)over double(cascaded)Rayleigh fading channels are derived.We also analyse the impact of residual self-interference(RSI)caused at FD relay on system's performance.We compare the performance of system with two relay selection techniques,namely,maximum channel gain-based(Max-G)relay selection and minimum RSI-based(Min-SI)relay selection.This paper considers the joint effect of time splitting ratio and self-interference cancellation(SIC)level to find the optimum EH duration.Additionally,the effect of time splitting ratio and average signal-to-noise ratio(SNR)on outage and throughput performance of the system are also investigated in this paper.The derived expressions are validated through Monte Carlo simulations.

An Indoor Environment Sensing and Localization System via mmWave Phased Array

In this paper,an indoor layout sensing and localization system with testbed in the 60-GHz millimeter wave(mmWave)band,named mmReality,is elaborated.The mmReality system consists of one transmitter and one mobile receiver,both with a phased array and a single radio frequency(RF)chain.To reconstruct the room layout,the pilot signal is delivered from the transmitter to the receiver via different pairs of transmission and receiving beams,so that multipath signals in all directions can be captured.Then spatial smoothing and the two-dimensional multiple signal classification(MUSIC)algorithm are applied to detect the angle-of-departures(AoDs)and angle-of-arrivals(AoAs)of propagation paths.Moreover,the technique of multi-carrier ranging is adopted to measure the path lengths.Therefore,with the measurements of the receiver in different locations of the room,the receiver and virtual transmitters can be pinpointed to reconstruct the room layout.Experiments show that the reconstructed room layout can be utilized to localize a mobile device via the AoA spectrum.

Secure and Privacy-Preserving Decision Tree Classification with Lower Complexity

As a widely-used machine-learning classifier,a decision tree model can be trained and deployed at a service provider to provide classification services for clients,e.g.,remote diagnostics.To address privacy concerns regarding the sensitive information in these services(i.e.,the clients’inputs,model parameters,and classification results),we propose a privacy-preserving decision tree classification scheme(PDTC)in this paper.Specifically,we first tailor an additively homomorphic encryption primitive and a secret sharing technique to design a new secure two-party comparison protocol,where the numeric inputs of each party can be privately compared as a whole instead of doing that in a bit-by-bit manner.Then,based on the comparison protocol,we exploit the structure of the decision tree to construct PDTC,where the input of a client and the model parameters of a service provider are concealed from the counterparty and the classification result is only revealed to the client.A formal simulation-based security model and the security proof demonstrate that PDTC achieves desirable security properties.In addition,performance evaluation shows that PDTC achieves a lower communication and computation overhead compared with existing schemes.

Resource Allocation in UAV-NOMA Communication Systems Based on Proportional Fairness

This paper investigates the power allocation problem in non-orthogonal multiple access(NOMA)integrated unmanned aerial vehicle(UAV)communication systems.In particular,we propose a novel resource allocation scheme to increase the transmission rate of the users that have relatively worse channel state information,while reducing the sum rate loss.To solve this problem efficiently,we decouple the optimization problem into three subproblems.First,we solve the problem of user pairing and subchannel allocation.Second,the optimum power proportional factor is derived to allocate transmit power among different users on the same subchannel.At last,different subchannels are allocated with appropriate power to improve the performance of the subchannels.Simulation results show that the proposed scheme can enjoy a better performance than the benchmark methods since it can achieve a proper trade-off between the system sum rate and the proportional fairness.

MobileNet-Based IoT Malware Detection with Opcode Features

In recent years,with the rapid development of Internet and hardware technologies,the number of Internet of things(IoT)devices has grown exponentially.However,IoT devices are constrained by power consumption,making the security of IoT vulnerable.Malware such as Botnets and Worms poses significant security threats to users and enterprises alike.Deep learning models have demonstrated strong performance in various tasks across different domains,leading to their application in malicious software detection.Nevertheless,due to the power constraints of IoT devices,the well-performanced large models are not suitable for IoT malware detection.In this paper we propose a malware detection method based on Markov images and MobileNet,offering a cost-effective,efficient,and high-performing solution for malware detection.Additionally,this paper innovatively analyzes the robustness of opcode sequences.

Multi-State-Space Reasoning Reinforcement Learning for Long-Horizon RFID-Based Robotic Searching and Planning Tasks

In recent years,reinforcement learning(RL)has shown high potential for robotic applications.However,RL heavily relies on the reward function,and the agent merely follows the policy to maximize rewards but lacks reasoning ability.As a result,RL may not be suitable for long-horizon robotic tasks.In this paper,we propose a novel learning framework,called multiple state spaces reasoning reinforcement learning(SRRL),to endow the agent with the primary reasoning capability.First,we abstract the implicit and latent links between multiple state spaces.Then,we embed historical observations through a long short-term memory(LSTM)network to preserve long-term memories and dependencies.The proposed SRRL’s ability of abstraction and long-term memory enables agents to execute long-horizon robotic searching and planning tasks more quickly and reasonably by exploiting the correlation between radio frequency identification(RFID)sensing properties and the environment occupation map.We experimentally validate the efficacy of SRRL in a visual game-based simulation environment.Our methodology outperforms three state-of-the-art baseline schemes by significant margins.

Direction Finding with Cyclostationarity Analysis Against Frequency Interference

In this paper, a novel anti-interference direction finding(DF)method for amplitude comparison method based on cyclostationarity is proposed. With the periodic properties of the communication signals, the desired signal’s amplitude value can be effectively obtained even though there is an interference signal whose frequency spectrum overlaps with the desired signal in the environment,and the corresponding incident angle can be estimated accurately with the amplitude comparison method. The influence of interference signal on the amplitude comparison method is discussed and the proposed method’s theoretical feasibility is also analyzed. Compared with the conventional method,simulations are provided to demonstrate the anti-interference capability of the proposed method. The amplitude comparison DF system working at 2.44 GHz and 5.8 GHz is also constructed to verify its feasibility.