Based on current research, there is a lack of comprehensive review articles that systematically address the security issues of the internet of vehicles (IoV) using blockchain technology. In this study, we thoroughly analyze the threats and challenges within IoV systems and provide a systematic review of blockchain-based IoV security solutions. This paper summarizes blockchain solutions for addressing key challenges in IoV, including network security, communication efficiency, resource optimization, data management, and transaction operational efficiency. Our findings indicate that blockchain technology can enhance V2X communication security, optimize resource utilization, ensure data integrity, and improve transaction security. Additionally, this paper explores future research directions, including advanced security mechanisms, efficient consensus algorithms, the integration of edge computing, and intelligent applications, demonstrating the potential of blockchain in tackling IoV system challenges.

This work presents a concept of a multiple-input multiple-output (MIMO) antenna with wideband, high isolation, and high gain characteristics. Wideband performance is achieved using a patch antenna with a substrate suspended from an air gap. Unlike the conventional MIMO designs, in which only one element is excited for each port, the proposed antenna excites two MIMO elements simultaneously with the aid of a wideband hybrid coupler. Consequently, high gain radiation without requiring additional MIMO element and high isolation can be simultaneously achieved. A prototype designing at the central frequency of 2.45 GHz has been fabricated and measured, showing an impedance bandwidth of 25.7% (2.11--2.74~GHz) with a minimum isolation between two ports of 15 dB, and a peak gain of about 10.35 dBi.

The low Earth orbit (LEO) satellite networks play an important role in the future communication networks. However, under the end-to-end (E2E) transmission background, inter-satellite routing has been widely studied, but the influence of ground-satellite links (GSL) on routing has received less attention. In this paper, a fast E2E satellite routing algorithm based on access node selection is proposed. Firstly, the delay of four path modes generated by users accessing the network from different satellites is analyzed, and the influence of delay on E2E routing performance is presented. Then, jointly considering routing delay and node load, an access node selection strategy is proposed by using the shortest E2E delay to determine the access source and destination node within satellites. Finally, an optimization domain is divided from the network topology by using the shortest delay path based on hops constraints. And a routing optimization algorithm based on Q-learning has been proposed in the optimization domain, realizing high computational speed and stable results. The simulation results show that the access node selection strategy can decrease E2E delay by up to $10$~ms and enhance the performance of node load balancing. And the routing optimization algorithm can reduce the average computation time.

IEEE 802.11ax introduces orthogonal frequency division multiple access (OFDMA) technology to achieve parallel access for multiple users. However, when a large number of users access, the probability of collision will be greatly increased, resulting in greatly reduced throughput. In high-density wireless networks, in order to reduce collisions between users, a hybrid contention/designation-based uplink OFDMA random access (HCDRA) scheme is proposed. The proposed scheme adopts a hybrid of contention and transmission of designation, where the access point (AP) arranges one of the collided stations (STA) on the resource unit (RU) for transmission of designation in the next transmission, and other STAs still follow the traditional contention mechanism. The addition of designated transmission scheme can effectively reduce the intensity of contention and improve the utilization rate of RU without knowing the prior STA and without increasing the overhead of the sensing time. The proposed scheme can greatly improve throughput and latency performance without introducing additional overhead. Moreover, our proposed hybrid scheme has almost no changes to the IEEE 802.11ax standard protocol, and is suitable for IEEE 802.11ax and beyond. The feasibility of the proposed scheme has been demonstrated through numerical simulation results.

With the rise of the internet of things demanding low latency, the significance of real-time transmission communications is being increasingly emphasized. In real-time transmission systems, there are two ways to do the transmission: without and with channel coding, and therefore, it is worth comparing the performance of these ways. In this paper, the distortions of these two systems are derived. Specifically, a general formula for calculating distortion in wireless communication systems is first provided. Next, variations of the formula are developed for three standard wireless communication systems: single-input, single-output (SISO); multiple-input, multiple-output (MIMO) with a Gaussian channel; and mmWave MIMO. Theoretical derivations, supported by simulation results, demonstrate that systems without channel coding exhibit less distortion than those with channel coding on average.

Orthogonal time frequency space (OTFS) has been broadly studied and applied in various fields due to its robustness in high dynamic scenes. In this paper, we investigate the application of OTFS in radar-embedded communication systems, and propose an OTFS-based radar-embedded communication model suitable for high-speed mobile scenarios. By comparing the communication reliability at different velocities, the benefits of OTFS-based radar-embedded communication systems over conventional linear frequency modulation (LFM) based systems are verified. The proposed algorithm also shows excellent radar detection performance, and compensates the channel state information according to the motion parameters of the target to achieve more accurate communication reception. Besides that, our algorithm exhibits superior covertness performance to that of LFM.

Integrated super-resolution sensing and communication (ISSAC) has emerged as a promising technology to achieve extremely high precision sensing for those key parameters, such as the angles of the sensing targets. In this paper, we propose an efficient channel estimation scheme enabled by ISSAC for millimeter wave (mmWave) and terahertz (THz) systems with a hybrid analog/digital beamforming architecture, where both the pilot overhead and the cost of radio frequency (RF) chains are significantly reduced. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) can estimate channel parameters accurately without requiring dedicated a priori known pilots. In particular, the proposed method consists of two stages. First, the angles of the multi-path channel components are estimated in a pilot-free manner during the transmission of data symbols. For hybrid beamforming structure, two approaches are introduced to obtain spatial covariance matrix in this stage. Second, the multi-path channel coefficients are estimated with very few pilots. Compared to conventional channel estimation schemes that rely solely on channel training, our approach requires estimating much fewer parameters in the second stage. Furthermore, with channel multi-path angles obtained, the hybrid beamforming gain can be achieved when pilots are sent to estimate the channel path gains. To comprehensively investigate the performance of the proposed scheme, we consider both the basic line-of-sight (LoS) channels and more general multi-path channels. We compare the performance of the mean square error (MSE) of channel estimation and the resulting hybrid beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training. It is demonstrated that our proposed method significantly outperforms the benchmarking scheme. Simulation results are presented to validate our theoretical findings.

A covert communication approach in time-frequency domain is presented. By deliberately embedding covert signal into the primary transmitted signal, the covert signal is completely hidden, rendering conventional signal detection methods ineffective. However, the synchrosqueezing transform (SST) maps the signal into the time-frequency domain, resulting in improved time-frequency localization and more effective separation of its components. Based on the proposed instantaneous frequency (IF) curve extraction and filtered reconstruction scheme, the waveform of the covert signal can be automatically extracted. Experimental results from synthetic signal testing validate the efficiency of the proposed method, demonstrating its potential for covert communication applications.

Utilizing unmanned aerial vehicles (UAVs) for flexible and efficient mobile data dissemination offers a promising solution for infrastructure-less internet of things applications. This paper investigates a UAV-enabled data dissemination system in which a multiantenna UAV is deployed to disseminate data to ground terminals (GTs). Under the constraints of UAV speed, communication outage probability (OP), UAV transmission power, and task data size, we aim to minimize the system mission completion time (MCT) by optimizing the UAV trajectory, UAV-GT transmission scheduling, and bandwidth allocation, while considering the imperfect channel state information (CSI). To address this challenging problem, we introduce matched-filter precoding and orthogonal frequency division multiplexing (OFDM) techniques and propose a two-stage optimization algorithm. Initially, an offline optimization algorithm is developed to determine the optimum UAV trajectory and the MCT. Subsequently, an online optimization algorithm is proposed to enhance the subchannel and transmission power allocation. Finally, the numerical results confirm the effectiveness of the proposed scheme, demonstrating a substantial improvement in the time efficiency of UAV data dissemination.

With the development of internet of things (IoT) technology, sensor networks are used in a wide variety of fields. However, the energy replenishment of sensor nodes (SNs) is still a challenge. In this paper, we propose an unmanned aerial vehicle (UAV) wireless charging strategy (WCS), which can jointly maximize the system profit and the number of survival nodes through an efficient charging order. To effectively reduce the data transmission delay, we group the nodes to realize the data collection solely by cluster head (CH). In the proposed charging order algorithm, we particularly design a CH replacement scheme when the original head runs out of energy before the UAV arrives. Compared with other UAV charging schemes, the proposed WCS shows a better performance in terms of system profit and time delay. Furthermore, it ensures a rather satisfying node survival rate.

Given the rapid advancements in wireless communication and terminal devices, high-speed and convenient Wi-Fi has permeated various aspects of people's lives, and attention has been drawn to the location services that Wi-Fi can provide. Fingerprint-based methods, as an excellent approach for localization, have gradually become a hot research topic. However, in practical localization, fingerprint features of traditional methods suffer from low reliability and lacking robustness in complex indoor environments. To address the identified shortcomings, this paper introduces Secci, an innovative feature-enhanced intelligent localization system underpinned by a squeeze-and-excitation convolutional neural network (SE-CNN) model, utilizing a rich array of channel state information (CSI) data. By modifying the device driver, diversified CSI data are extracted and converted into red, green, and blue (RGB) CSI images, which serve as input to the SE-CNN training in the offline stage, leveraging the SE network's attention mechanism for improved performance. Employing a greedy probabilistic approach, rapid prediction of the estimated location is performed in the online stage using test CSI images. The Secci system is implemented using off-the-shelf Wi-Fi devices, and comprehensive experiments are carried out in two representative indoor environments to showcase the superior performance of Secci compared to four existing algorithms.

Full-duplex (FD) integrated access and backhaul (IAB) system requires effective self-interference (SI) suppression, relying on accurate channel estimation with high pilot overheads. This paper proposes a cost-effective end-to-end (E2E) hybrid beamforming framework for FD enabled millimeter-wave (mmWave) IAB systems with implicit channel state information (CSI), which unifies the pilot training and data communication to avoid accurate channel estimation. We introduce a Sensing Transformer block to sense spatial channels of the network, where sensing vectors are designed for pilot observation. Specifically, the Sensing Transformer is designed to implement an attention mechanism to effectively leverage implicit CSI from sequential pilot observations with an arbitrary length. Thereafter, we combine graph neural networks and Transformer mechanisms as a novel Graphformer algorithm to design hybrid beamformers with high spectrum efficiency, which can efficiently mitigate multi-user interference and SI via capturing both local and global spatial structure features of the whole network. Numerical results verify that: 1) The proposed E2E framework can significantly improve the spectrum efficiency with lower pilot overheads compared with the conventional method with explicit channel estimation. 2) The proposed E2E framework exhibits superior channel sensing and interference cancellation performance over traditional learning-based schemes across various network configurations, even with modest pilot overheads.

In the process of minimizing the training loss of machine learning model pursuit, it is very easy to inadvertently remember sensitive private data, which leads to data reconstruction, member reasoning attacks and other security problems. In order to mitigate these risks, differentiated privacy has become a key standard for privacy preserving machine learning. The classical differential privacy depth learning algorithm, differential privacy stochastic gradient descent (DP-SGD), that adapts the standard stochastic gradient descent (SGD) algorithm to incorporate differential privacy, ensuring that the trained model doesn't reveal sensitive information about individual training data points. However, DP-SGD has the problems of slow convergence speed and large utility loss. We propose an effective solution that is the cooperative combination of selective updating and early vertical gradient disturbance. Selective updating ensures that the training track of the model is aligned with the optimal direction, significantly accelerating convergence. Subsequently, the application of vertical gradient perturbation ensures that the model with significantly improved accuracy can be achieved even under strict privacy constraints (small privacy budget). Through theoretical analysis and a large number of experiments, this paper proves that DP-VGPSU has superior performance in convergence speed and accuracy.

This paper presents an 8-port multiple-input multiple-output (MIMO) array with single-layer and compact size characteristics for 5G internet of things (IoT) devices. The proposed array employs four dual-polarized radiators arranged in a $2 \times 2$ configuration to realize 8 uncorrelated streams. Each radiator is formed by combining a crossed patch with four parasitic elements for bandwidth enhancement. For mutual coupling mitigation, multiple shorting pins are inserted between the radiators. Accordingly, high isolation can be obtained without requiring large spacing between the MIMO elements. The final array has overall dimensions of $57.2 \text{ mm} \times 57.2 \text{ mm} \times 1.6 \text{ mm}$, or about $0.82\lambda \times 0.82\lambda \times 0.02\lambda$ at the lower edge frequency of the operating band from $4.32$ to $4.68 \text{ GHz}$, corresponding to about 8%. Across this band, the inter-port isolations are always better than $17 \text{ dB}$.

Due to the elimination of cell boundaries, cell-free massive multiple-input multiple-output (MIMO) facilitates seamless handover and ensures consistent service, making it suitable for high-speed railway communications. However, high mobility causes rapid variations in the train-to-ground wireless channel, leading to significant channel aging, which severely undermines channel state information accuracy and degrades system performance, especially in cell-free setups where channel hardening is limited. In this paper, the impact of channel aging caused by high-speed train movement and the performance gains achievable through downlink training are investigated. We derive the downlink spectral efficiency (SE) and compare the outcomes for cases with and without downlink training for cell-free massive MIMO systems. Given the limited coherence time, an algorithm is proposed to mitigate the overhead from downlink training, which enables users to select distributed access points based on large-scale fading coefficients. Orthogonal pilot sequences are allocated to users associated with the same access point, effectively lowering pilot overhead and mitigating pilot contamination. Simulation results demonstrate that the benefits of downlink training offset the performance loss due to limited data transmission time, thereby enhancing system SE. Moreover, in situations characterized by elevated user density, the proposed pilot assignment algorithm markedly improves the system's total SE.

In this paper, we investigate the secure communication in internet-of-things (IoT) network. Specifically, we propose a novel dual-IRS-UAV assisted secure transmission scheme, wherein two unmanned aerial vehicles (UAVs), each carriying an intelligent reflecting surface (IRS), can adjust their horizontal positions to efficiently assist the secure transmission from the IoT data sender to the receivers. We aim to maximize the minimum system secrecy rate by jointly optimizing the horizontal locations of UAVs, the transmit beamforming of IoT data sender and the reflection phase shifts of IRSs. The considered problem is non-convex. We first divide it into several sub-problems which are then transformed using the first-order Taylor expansion approach, which can be solved by standard optimization tools. Numerical results show the performance gain in minimum system secrecy rate achieved by the proposed dual-IRS-UAV assisted secure transmission scheme over the benchmark schemes.

In this paper, we proposed a novel millimeter wave channel emulation platform for satellite communication scenarios that can support Q/V frequency bands with a maximum bandwidth of 1 GHz. The platform has been calibrated for frequency flatness and frequency offset, and its reliability has been demonstrated through closed-loop verification based on standard testing instruments. At the same time, a method for modeling and analyzing satellite channels and real-time emulation environment was constructed, which can model and analyze satellite channels in different application scenarios. The obtained channel parameters are injected into the channel emulation platform to achieve real-time emulation of satellite channels in various application scenarios (stationary, vehicle mounted, shipborne, airborne, etc.). Finally, we conducted performance verification on the constructed satellite channel hardware-in-the-loop simulation system. This platform can provide support for key technology research and equipment performance evaluation in satellite communication.

Cognitive radio enables efficient utilization of spectrum resources and adapts to changing environments through intelligent sensing and adaptability. However, centralized spectrum allocation limits their efficiency. This paper proposes a blockchain-based priority auction consensus mechanism for dynamic spectrum allocation in cognitive radio. The distributed spectrum allocation improves efficiency, the use of blockchain ensures transparent and secure transactions, while the priority auction consensus ensures the communication quality of users. Simulation results validate that primary users achieve near-optimal performance, while secondary and other users guarantee communication performance, resulting in an overall spectrum utilization close to 100%. These highlight the potential of the priority auction consensus mechanism in enhancing the spectrum management of cognitive radio networks.

Mobile edge computing (MEC) has been envisioned as an important technique of the 5th generation (5G) wireless networks to handle computation tasks from mobile terminal devices (TDs). To accommodate the low-delay requirements of mobile TDs with latency-sensitive and computation-intensive applications, we propose an edge-edge collaborative paradigm for software-defined networking (SDN)-enabled MEC networks by merging adjacent MEC servers with redundant computing resources to execute offloading tasks. Meanwhile, since task data distributed among various edge computation nodes is vulnerable to malicious attacks and eavesdropping, resulting in data and privacy leakage. We take secure offloading service into account and attempt to minimize the total cost of all offloading tasks by the optimal scheme, jointly considering offloading decision, security level and computing resource assignment. To reduce the total cost of all offloading tasks mainly caused by wireless communication, energy consumption and secure computing, we propose an optimization framework which can dynamically adjust the task offloading decision, security level and computing resource allocation policy simultaneously in MEC networks. We formalize the problem as a multi-agent decision based optimization problem, and further address it by using a multi-agent deep reinforcement learning (DRL) based method. Numerical results illustrate that the proposed DRL-based method is superior to the benchmark methods in terms of the total cost and support ratio.

A brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60Hz, which is higher than the critical fusion frequency (CFF), were compared with those at 15Hz and 30Hz. SSVEP components in electroencephalogram (EEG) were detected using task related component analysis (TRCA) method. Offline analysis with 17 subjects indicated that the highest information transfer rate (ITR) was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%. The online BCI system reached an averaged classification accuracy of 87.75±3.50% at 60Hz with 4s, resulting in an ITR of 16.73±1.63bpm. In particular, the maximum ITR for a subject was 80bpm with 0.5s at 60Hz. Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz, the results of the behavioral test indicated that, with no perception of flicker, the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz. Correlation analysis revealed that SSVEP with higher signal-to-noise ratio (SNR) corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance. This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

With rapid advances of solar blind ultraviolet LED and ultraviolet detecting technology in recent years, ultraviolet communication gradually becomes a research hotspot due to its inherent advantages: low solar background noise, non-line-of-sight(NLOS) and good secrecy. The strong scattering characteristics in atmospheric render ultraviolet waveband the ideal choice for achieving NLOS optical communication. This paper reviews the research history and status of ultraviolet communication both in China and abroad, and especially introduces three main issues of ultraviolet communication: channel model, system analysis and design, light sources and detectors. For each aspect, current open issues and prospective research directions are analyzed.

Unsourced multiple access (UMA) is a multi-access technology for massive, low-power, uncoordinated, and unsourced Machine Type Communication (MTC) networks. It ensures transmission reliability under the premise of high energy efficiency. Based on the analysis of the 6G MTC key performance indicators (KPIs) and scenario characteristics, this paper summarizes its requirements for radio access networks. Following this, the existing multiple access models are analyzed under these standards to determine UMA's advantages for 6G MTC according to its design characteristics. The critical technology of UMA is the design of its multiple-access coding scheme. Therefore, the existing UMA coding schemes from different coding paradigms are further summarized and compared. In particular, this paper comprehensively considers the energy efficiency and computational complexity of these schemes, studies the changes of the above two indexes with the increase of access scale, and considers the trade-off between the two. It is revealed by the above analysis that some guiding rules of UMA coding design. Finally, the open problems and potentials in this field are given for future research.

Terahertz (THz) communication has been envisioned as a key enabling technology for sixth-generation (6G). In this paper, we present an extensive THz channel measurement campaign for 6G wireless communications from 220 GHz to 330 GHz. Furthermore, the path loss is analyzed and modeled by using two single-frequency path loss models and a multiple-frequencies path loss model. It is found that at most frequency points, the measured path loss is larger than that in the free space. But at around 310 GHz, the propagation attenuation is relatively weaker compared to that in the free space. Also, the frequency dependence of path loss is observed and the frequency exponent of the multiple-frequencies path loss model is 2.1. Moreover, the cellular performance of THz communication systems is investigated by using the obtained path loss model. Simulation results indicate that the current inter-site distance (ISD) for the indoor scenario is too small for THz communications. Furthermore, the tremendous capacity gain can be obtained by using THz bands compared to using microwave bands and millimeter wave bands. Generally, this work can give an insight into the design and optimization of THz communication systems for 6G.

Security and privacy issues are magnified by velocity, volume, and variety of big data. User’s privacy is an even more sensitive topic attracting most people’s attention. While XcodeGhost, a malware of iOS emerging in late 2015, leads to the privacy-leakage of a large number of users, only a few studies have examined XcodeGhost based on its source code. In this paper we describe observations by monitoring the network activities for more than 2.59 million iPhone users in a provincial area across 232 days. Our analysis reveals a number of interesting points. For example, we propose a decay model for the prevalence rate of XcodeGhost and we find that the ratio of the infected devices is more than 60%; that a lot of popular applications, such as Wechat, railway 12306, didi taxi, Youku video are also infected; and that the duration as well as the traffic volume of most XcodeGhost-related HTTP-requests is similar with usual HTTP-request which makes it difficult to be found. Besides, we propose a heuristic model based on fingerprint and its web-knowledge to identify the infected applications. The identifying result shows the efficiency of this model.

To accommodate the ever-increasing wireless capacity, the terahertz (THz) orbital angular momentum (OAM) beam that combines THz radiation and OAM technologies has attracted much attention recently, with contributing efforts to explore new dimensions in the THz region. In this paper, we provide an overview of the generation and detection techniques of THz-OAM beams, as well as their applications in communications. The principle and research status of typical generation and detection methods are surveyed, and the advantages and disadvantages of each method are summarized from a viewpoint of wireless communication. It is shown that developing novel THz components in generating, detecting and (de)multiplexing THz-OAM beams has become the key engine to drive this direction forward. Anyway, beneficial from the combination of infinite orthogonal modes and large bandwidth, THz-OAM beams will have great potential in delivering very large capacity in next generation wireless communications.

Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS (Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.

Network virtualization (NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks (VNs) with different protocols to coexist on a shared substrate network (SN). One main challenge in NV is virtual network embedding (VNE). VNE is a NP-hard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in small-scaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT (Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNE-RSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNE-RSOT, a simulation against typical and state-of-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request (VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.

An Efficient and flexible implementation of block ciphers is critical to achieve information security processing. Existing implementation methods such as GPP, FPGA and cryptographic application-specific ASIC provide the broad range of support. However, these methods could not achieve a good tradeoff between high-speed processing and flexibility. In this paper, we present a reconfigurable VLIW processor architecture targeted at block cipher processing, analyze basic operations and storage characteristics, and propose the multi-cluster register-file structure for block ciphers. As for the same operation element of block ciphers, we adopt reconfigurable technology for multiple cryptographic processing units and interconnection scheme. The proposed processor not only flexibly accomplishes the combination of multiple basic cryptographic operations, but also realizes dynamic configuration for cryptographic processing units. It has been implemented with 0.18µmCMOS technology, the test results show that the frequency can reach 350MHz, and power consumption is 420mw. Ten kinds of block and hash ciphers were realized in the processor. The encryption throughput of AES, DES, IDEA, and SHA-1 algorithm is 1554Mbps, 448Mbps, 785Mbps, and 424Mbps respectively, the test result shows that our processor’s encryption performance is significantly higher than other designs.

In recent years, with the growth in Unmanned Aerial Vehicles (UAVs), UAV-based systems have become popular in both military and civil applications.In these scenarios, the lack of reliable communication infrastructure has motivated UAVs to establish a network as flying nodes, also known as Flying Ad Hoc Networks (FANETs).However, in FANETs, the high mobility degree of flying and terrestrial users may be responsible for constant changes in the network topology, making end-to-end connections in FANETs challenging.Mobility estimation and prediction of UAVs can address the challenge mentioned above since it can provide better routing planning and improve overall FANET performance in terms of continuous service availability.We thus develop a Software Defined Network (SDN)-based heterogeneous architecture for reliable communication in FANETs.In this architecture, we apply an Extended Kalman Filter (EKF) for accurate mobility estimation and prediction of UAVs.In particular, we formulate the routing problem in SDN-based Heterogeneous FANETs as a graph decision problem.As the problem is NP-hard, we further propose a Directional Particle Swarming Optimization (DPSO) approach to solve it.The extensive simulation results demonstrate that the proposed DPSO routing can exhibit superior performance in improving the goodput, packet delivery ratio, and delay.

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication networks. Since the frequency-hopping (FH) sequence is usually generated by a certain model with certain regularity, the FH frequency is thus predictable. In this paper, we investigate the FH frequency reconnaissance and prediction of a non-cooperative communication network by effective FH signal detection, time-frequency (TF) analysis, wavelet detection and frequency estimation. With the intercepted massive FH signal data, long short-term memory (LSTM) neural network model is constructed for FH frequency prediction. Simulation results show that our parameter estimation methods could estimate frequency accurately in the presence of certain noise. Moreover, the LSTM-based scheme can effectively predict FH frequency and frequency interval.

In this paper, we investigate the reconfigurable intelligent surface (RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing (MISO-OFDM) system under frequency-selective channels, and propose a low-complexity alternating optimization (AO) based joint beamforming and RIS phase shifts optimization algorithm to maximize the achievable rate. First, with fixed RIS phase shifts, we devise the optimal closed-form transmit beamforming vectors corresponding to different subcarriers. Then, with given active beamforming vectors, near-optimal RIS reflection coefficients can be determined efficiently leveraging fractional programming (FP) combined with manifold optimization (MO) or majorization-minimization (MM) framework. Additionally, we also propose a heuristic RIS phase shifts design approach based on the sum of subcarrier gain maximization (SSGM) criterion requiring lower complexity. Numerical results indicate that the proposed MO/MM algorithm can achieve almost the same rate as the upper bound achieved by the semidefinite relaxation (SDR) algorithm, and the proposed SSGM based scheme is only slightly inferior to the upper bound while has much lower complexity. These results demonstrate the effectiveness of the proposed algorithms.

Compared with wired communication, the wireless communication link is more vulnerable to be attacked or eavesdropped because of its broadcast nature. To prevent eavesdropping, many researches on transmission techniques or cryptographic methods are carried out. This paper proposes a new index parameter named as eavesdropping area, to evaluate the anti-eavesdropping performance of wireless system. Given the locations of legitimate transmitter and receiver, eavesdropping area index describes the total area of eavesdropping regions where messages can be wiretapped in the whole evaluating region. This paper gives detailed explanations about its concept and deduces mathematical formulas about performance curves based on region classification. Corresponding key system parameters are analyzed, including the characteristics of eavesdropping region, transmitted beam pattern, beam direction, receiver sensitivity, eavesdropping sensitivity, path loss exponent and so on. The proposed index can give an insight on the confirmation of high-risk eavesdropping region and formulating optimal transmitting scheme for the confidential communications to decrease the eavesdropping probability.

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature. VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth. Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources. The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.

Decoding by alternating direction method of multipliers (ADMM) is a promising linear programming decoder for low-density parity-check (LDPC) codes. In this paper, we propose a two-step scheme to lower the error floor of LDPC codes with ADMM penalized decoder. For the undetected errors that cannot be avoided at the decoder side, we modify the code structure slightly to eliminate low-weight code words. For the detected errors induced by small error-prone structures, we propose a post-processing method for the ADMM penalized decoder. Simulation results show that the error floor can be reduced significantly over three illustrated LDPC codes by the proposed two-step scheme.

Software-Defined Networking (SDN) provides flexible and global network management by decoupling control plane from data plane, and multiple controllers are deployed in the network in a logically centralized and physically distributed way. However, the existing approaches generally deploy the controllers with the same type in the network, which easily causes homogeneous controller common-mode fault. To this end, this paper proposes heterogeneous controller deployment in the SDN, considering the different types of controllers and relevant criteria (e.g., delay, control link interruption rate, and controller fault rate). Then, we introduce a Safe and Reliable Heterogeneous Controller Deployment (SRHCD) approach, consisting of two stages. Stage 1 determines the type and the number of heterogeneous controllers required for the SDN network based on the dynamic programming. Stage 2 divides the SDN network into multiple subnets by k-means algorithm and improves the genetic algorithm to optimize the heterogeneous controller deployment in these SDN subnets to ensure reliable switch-controller communications. Finally, the simulation results show that the proposed approach can effectively reduce the control plane fault rate and increase the attack difficulties. Besides, the switch- controller delay has been lowered by 16.5% averagely.

The highly dynamic channel (HDC) in an extremely dynamic environment mainly has fast time-varying nonstationary characteristics. In this article, we focus on the most difficult HDC case, where the channel coherence time is less than the symbol period. To this end, we propose a symbol detector based on a long short-term memory (LSTM) neural network. Taking the sampling sequence of each received symbol as the LSTM unit's input data has the advantage of making full use of received information to obtain better performance. In addition, using the basic expansion model (BEM) as the preprocessing unit significantly reduces the number of neural network parameters. Finally, the simulation part uses the highly dynamic plasma sheath channel (HDPSC) data measured from shock tube experiments. The results show that the proposed BEM-LSTM-based detector has better performance and does not require channel estimation or channel model information.

To meet the demands for the explosive growth of mobile data rates and scarcity of spectrum resources in the near future, the terahertz (THz) band has widely been regarded as a key enabler for the upcoming beyond fifth-generation (B5G) wireless communications. An accurate THz channel model is crucial for the design and deployment of the THz wireless communication systems. In this paper, a three-dimensional (3-D) dynamic indoor THz channel model is proposed by means of combining deterministic and stochastic modeling approaches. Clusters are randomly distributed in the indoor environment and each ray is characterized with consideration of molecular absorption and diffuse scattering. Moreover, we present the dynamic generation procedure of the channel impulse responses (CIRs). Statistical properties are investigated to indicate the non-stationarity and feasibility of the proposed model. Finally, by comparing with delay spread and K-factor results from the measurements, the utility of the proposed channel model is verified.

This paper analyzes the threat of TCG Software Stack (TSS)/TCM Service Module (TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism. In addition, this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat. The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue. In this strategy, an algorithm for real-time value computing is also designed, and it can adjust the production curves of the real time value by setting parameters in different environment, thus enhancing its adaptability, which is followed by scheduling and algorithm description. This paper also implements the algorithm and carries out its performance optimization. Due to the experiment result from Intel NUC, it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.

This work focuses on an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system based on device-to-device (D2D) communication. In this system, the UAV exhibits caching, computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes. To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.

Data sharing in Internet of Vehicles (IoV) makes it possible to provide personalized services for users by service providers in Intelligent Transportation Systems (ITS). As IoV is a multi-user mobile scenario, the reliability and efficiency of data sharing need to be further enhanced. Federated learning allows the server to exchange parameters without obtaining private data from clients so that the privacy is protected. Broad learning system is a novel artificial intelligence technology that can improve training efficiency of data set. Thus, we propose a federated bidirectional connection broad learning scheme (FeBBLS) to solve the data sharing issues. Firstly, we adopt the bidirectional connection broad learning system (BiBLS) model to train data set in vehicular nodes. The server aggregates the collected parameters of BiBLS from vehicular nodes through the federated broad learning system (FedBLS) algorithm. Moreover, we propose a clustering FedBLS algorithm to offload the data sharing into clusters for improving the aggregation capability of the model. Some simulation results show our scheme can improve the efficiency and prediction accuracy of data sharing and protect the privacy of data sharing.