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.

Wireless networks support numerous terminals, manage large data volumes, and provide diverse services, but the vulnerability to environmental changes leads to increased complexity and costs. Situational awareness has been widely applied in network management, but existing methods fail to find optimal solutions due to the high heterogeneity of base stations, numerous metrics, and complex intercell dependencies. To address this gap, this paper proposes a specialized framework for wireless networks, integrating an evaluation model and control approach. The framework expands the indicator set into four key areas, introduces an evaluation method, and proposes the indicator perturbation greedy (IPG) algorithm and the adjustment scheme selection method based on damping coefficient (DCSS) for effective network optimization. A case study in an urban area demonstrates the framework’s ability to balance and improve network performance, enhancing situational awareness and operational efficiency under dynamic conditions.

A thin compact broadband coplanarfed rectangular-ring monopole antenna parasiticallyloaded by three nested concentric rectangle rings and a π-shaped stub is proposed suitable for modern communication needs. It has an overall area of only 25 mm×6 mm (0.29λ0×0.07λ0 at 3.5 GHz), which can be the base radiating element of the MIMO array, being easily integrated into any wireless device. Its measured (simulated) fractional bandwidth is 24.6% (31.6%) ranging from 3.25 (3.09) to 4.16 (4.25) GHz, being applicable to the 5G N48, N77, and N78 bands. Practical guidelines are also provided to make the proposed design operate on some other additional 5G bands (e.g., N41 or N46) without compromising its overall size. As far as the radiation properties are concerned, the antenna with such small dimensions radiates nearly bidirectionally and omnidirectionally in the E- and H-plane, respectively, and has an average measured (simulated) peak realized gain of -0.1 (1.8) dBi over the band of interest. The proposed antenna is wideband, physically small and relatively easy to manufacture, making it straightforward to integrate with the RF electronics in IoT sensors.

High-selectivity common-mode (CM) and differential-mode (DM) reflectionless balanced bandpass filters (BBPFs) are proposed in this article. By loading absorption networks at single/both ends of the basic ring resonator, input-/two-port wideband CM and DM reflectionless performance, wideband filtering performance and all-stop CM suppression are obtained. The absorption network composed of K-sections of coupled-lines (CLs) terminated with grounded resistors can not only extend the filtering performance to high order, but also realize wideband absorption of CM noise and out-of-band DM signals. Absorptive stubs are loaded at ports to increase the design flexibility and enhance the absorption. As for the input-reflectionless type, multiple independently controlled transmission zeros (TZs) are obtained by the TZ control network, which improves the selectivity and out-of-band rejection. A set of 2 GHz micro-strip BBPFs are designed and measured, which shows simultaneous CM and DM absorption performance.

As a mechanism for managing emissions, carbon quota trading effectively controls and reduces carbon emissions from vehicles in vehicular ad hoc networks (VANETs). Nonetheless, the wireless transmission in VANETs is susceptible to various attacks. Recently, a certificateless aggregate signature (CLAS) scheme has been proposed to ensure communication security and privacy protection in VANETs. Unfortunately, through rigorous security analysis, we find that this scheme is vulnerable to both public key replacement attacks and coalition attacks. To address these vulnerabilities, we propose a carbon quota trading scheme to enhance the security and robustness of the system. Our proposed scheme utilizes blockchain technology to ensure the immutability of carbon quota transaction information, while using CLAS guarantees the integrity and nonrepudiation of data. Additionally, the scheme is resilient to the aforementioned attacks. The experimental results demonstrate that our scheme satisfies more security requirements without compromising computational performance.

The radio access network (RAN) connects the users to the core networks, where typically digitised radio over fiber (D-RoF) links are employed. The data rate of the RAN is limited by the hardware constraints of the D-RoF-based backhaul and fronthaul. In order to break this bottleneck, the potential of the analogue radio over fiber (A-RoF) based RAN techniques are critically appraised for employment in the next-generation systems, where increased-rate massive multiple-input-multiple-output (massive-MIMO) and millimeter wave (mmWave) techniques will be implemented. We demonstrate that huge bandwidth and power-consumption cost benefits may accrue upon using A-RoF for next-generation RANs. We provide an overview of the recent A-RoF research and a performance comparison of A-RoF and D-RoF, concluding with further insights on the future potential of A-RoF.

This paper introduces a simple yet effective approach for developing fuzzy logic controllers (FLCs) to identify the maximum power point (MPP) and optimize the photovoltaic (PV) system to extract the maximum power in different environmental conditions. We propose a robust FLC with low computational complexity by reducing the number of membership functions and rules. To optimize the performance of the FLC, metaheuristic algorithms are employed to determine the parameters of the FLC. We evaluate the proposed FLC in various panel configurations under different environmental conditions. The results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability, responsiveness, and power transfer under various scenarios.

Current data storage methods often utilize centralized storage in cloud environments, which can raise data privacy concerns in scenarios involving data sharing and distribution. The article is presented a data sharing scheme that integrates Attribute-Based Encryption, symmetric encryption, blockchain, and IPFS. By employing a key encapsulation mechanism, these two encryption technologies collaborate effectively. The scheme is structured around access control policies, ensuring both fine-grained data access and efficiency for data requesters. Furthermore, storing data hashes on the blockchain and the actual encrypted data off-chain in IPFS resolves the scalability issues associated with blockchain systems. Experiments demonstrate that the proposed scheme can safeguard fine-grained access control and data security during data sharing, and it enhances performance in terms of data encryption and decryption times compared to traditional algorithms. This substantiates the feasibility and security of the proposed scheme.

The popularity of social networks and mobile intelligent devices, significantly promotes various Location-based services (LBSs). While benefiting from convenient LBSs, users are more concern about their data privacy. k-anonymity methods are the widely used methods to protect users' privacy. However, traditional k-anonymity methods are easily suffered from Location injection attacks (LIAs), which can be launched by injecting untrusted users and dummy information to cloaked regions, due to they assume that all users in cloaked regions are honest. Therefore, LIAs extremely decrease the protection degree of k-anonymity methods. To solve this problem, we propose a dynamic movement patterns-based privacy protection scheme to against LIAs in continuous queries. We first utilize time-dependent first_x0002_order Markov chains to model users' moving patterns through their Cloaked regions (CRs). Then we evaluate users' credits by their transition probability matrices and intersections of users' Maximal movement boundaries (MMBs). Next, we calculate coordinates of MMBs' intersections and Euclidean distances of users to evaluate their trajectory similarities. Finally, we select users whose credits and Euclidean distances are higher to achieve k-anonymity. Security analysis and substantial experiments indicate that our scheme can simultaneously defense the LIAs effectively and protect users' privacy efficiently.

The personal growth profile (PGP) is an important document for providing someone’s comprehensive quality proof by recording physical health, academic performance, quality level, integrity record, etc. PGPs are widely used in many scenarios, such as applying for jobs, checking enrollment qualifications, evaluating personal credit, etc. The traditional management of PGP has many problems, such as highly centralized data processing, insecure credential sharing and inconvenient off-line credential verification, etc. To solve these issues, the paper presents a blockchain-based certificateless attribute-based searchable encryption scheme (BB-CL-AB-SE) for encrypting, delivering, requesting, and using PGPs. In the scheme, a consortium blockchain with decentralized centers and tamper-proof features is constructed to securely share PGPs and trace the responsibilities of authorities, key generation centers, data users, and data generators in a cloud environment. In order to enhance PGP’s owner ship of data provider and data owner, the certificateless encryption technology is adopted to establish legal roles whose partial key mastered by themselves and produce ciphertext keyword and data user’s trapdoor key in ciphertext keyword retrieval process. Attribute-based encryption technology is used to encrypt the symmetric keys for protecting the confidentiality of PGP and realizes fine-grained access policies. Cloud storage provider checks user’s legitimacy before providing encrypted PGPs. The scheme provides the ciphertext keyword retrieval function and flexible access policy and can resist key escrow in protecting user’s PGP. The scheme also obtains the transparency, traceability, and anti-tampering of blockchain. The BB-CL-AB-SE scheme makes PGP’s management more real, credible, and easy to operate. The security proof and the experiment result illustrate the scheme is secure and has good computing performance.

With the rapid development of wireless virtual reality (VR) technology, the demand for immersive experiences has surged. Nevertheless, selecting an appropriate VR video coding strategy is challenging due to the limitations of network resources. Addressing this issue, this paper explores a wireless edge caching model for VR services and considers three different coding strategies: 1) transcoding and super-resolution, 2) multi-version video coding, and 3) scalable video coding. To minimize video service delay for multiple users while adhering to resource constraints, we introduce a constrained discrete-continuous two-delay deep deterministic policy gradient (CDC-TD3) algorithm, designed to optimize caching, computation, communication (3C) resource allocation, and the computing task offloading ratio. Simulation results demonstrate that our algorithm effectively reduces service delays under all three video coding strategies. Furthermore, by comparing the performance of different video coding strategies under varying resource conditions, we provide guidance for selecting the optimal video coding strategy.

Protocol Reverse Engineering (PRE) is of great practical importance in Internet security-related fields such as intrusion detection, vulnerability mining, and protocol fuzzing. For unknown binary protocols having fixed-length fields, and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance. Hence, this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty. Specifically, we design four related constructions between entropy changes and protocol field segmentation, introduce random variables, and construct joint probability distributions with traffic sample observations. Probabilistic inference is then performed to identify the possible protocol segmentation points. Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.

In hybrid beamforming design using the conventional gradient projection (GP) algorithm, it is common to use a fixed step size, which results in a slow convergence rate and unsatisfactory achievable rate performance. This paper employs a deep unfolding algorithm within a small fixed number of iterations to tackle the hybrid beamforming optimization problem. The optimal step size is obtained by combining the conventional GP algorithm with the deep learning technique, and every step in deep learning is explainable. Simulation results show that the proposed deep unfolding algorithm demonstrates a lower computational time and superior achievable rate performance than the conventional GP algorithm.

Recently, uniform circular array (UCA) based orbital angular momentum (OAM) beam steering schemes have been proposed to overcome the limitations of coaxial transmission. Unlike the traditional multiple-input-multiple-output (MIMO) beam steering, OAM beam steering includes both the OAM generation and the beam steering. Generally, the true time delay (TTD) or the phase shifter (PS) are required for beam steering in the radio domain. Previous studies suggest that TTD is preferred for wideband MIMO beam steering to avoid beam squint caused by PS. However, in this paper, we theoretically prove that to generate the OAM beam ideally, PS should be used, while TTD deteriorates the mode orthogonality, which is influenced by the relative bandwidth. Once the ideal OAM beam is generated, TTD is required to prevent beam squint. Based on this analysis, we propose to use the two-stage phase-shifting (TSPS) architecture for OAM beam steering: PS for OAM generation and TTD for beam steering. Simulation results suggest that compared to the spectrum efficiency (SE) of PS based OAM communication, the SE based on the TTD significantly declines as the relative bandwidth increases. Furthermore, OAM beam steering using the TSPS architecture greatly outperforms systems that adopt a single TTD or PS network.

High-speed power line communication (HPLC) is diffusely applied in smart grids due to its advantages, such as cheap cost, convenient networking, safety, and reliability. However, variable loads and the presence of numerous nodes in power lines result in impulse noise interference and multipath effects in HPLC channels. Traditional channel estimation algorithms fall short of accurately estimating HPLC channels. To address these challenges, this paper proposes a lightweight convolutional gating network (CnGRUNet) based on a combination of convolutional neural network (CNN) and gated recurrent unit (GRU) for accurate HPLC channel estimation. First, the discrete fourier transform (DFT) algorithm is used to filter out impulse noise and obtain initial channel features. Then, CNN is used to extract and refine multipath channel features. Finally, GRU is adopted to memorize and learn the channel time-varying characteristics, thereby accurately estimating multipath time-varying power line channels. Simulation results demonstrate that CnGRUNet can effectively combat multipath fading and reduce the impact of impulse noise. Its channel estimation accuracy surpasses that of the DFT algorithm by more than 95% and significantly outperforms the minimum mean square error (MMSE) algorithm. Furthermore, compared with deep learning algorithms such as deep neural network (DNN), CnGRUNet has lower computational complexity.

In recent years, deep learning-based semantic communications have shown great potential to enhance the performance of communication systems. This has led to the belief that semantic communications represent a breakthrough beyond the Shannon paradigm and will play an essential role in future communications. To narrow the gap between current research and future vision, after an overview of semantic communications, this article presents and discusses ten fundamental and critical challenges in today's semantic communication field. These challenges are divided into theory foundation, system design, and practical implementation. Challenges related to the theory foundation including semantic capacity, entropy, and rate-distortion are discussed first. Then, the system design challenges encompassing architecture, knowledge base, joint semantic-channel coding, tailored transmission scheme, and impairment are posed. The last two challenges associated with the practical implementation lie in cross-layer optimization for networks and standardization. For each challenge, efforts to date and thoughtful insights are provided.

This paper presents a novel approach to design a compact circular rat-race coupler with an ultrawide stopband, with the aim to reduce its size while maintaining performance. The design methodology begins with a common miniaturization technique to replace the conventional quarter-wavelength transmission line with an equivalent low-pass filter loaded with parallel coupled line and radial stubs. Since the latter leads to produce higher order harmonics, parasitic open-ended stubs are then properly introduced in the structure not only to overcome the issue but also to produce controllable transmission zeros. A versatile analytical model is also developed taking into account manufacturing restrictions, which makes it possible to extract the physical parameters of the coupler unit-cell for a given desired compactness percentage with respect to the conventional rat-race coupler. A prototype is fabricated and measured to validate the design, demonstrating the predicted behavior fairly achieved by numerical analysis. A significant size reduction of about 86.1% was achieved compared to the conventional design, while effectively suppressing higher order modes up to 23.4 GHz (including the 13th harmonic based on |S11|>?5 dB and |S21| Resource Allocation for Transmission Efficiency Maximization towards Concurrent Transmission in Wireless Sensor Networks

Designing a resource allocation scheme to increase resource utilization and thus boost transmission efficiency is the main challenge associated with concurrent transmission. Existing methods focus on node selection or power control, rarely considering the allocation of slot resources and ignoring the inefficiency caused by concurrency heterogeneity and scheduling overhead. In this paper, a resource allocation scheme jointly with concurrent scheduling and slot allocation is designed to achieve a better performance of transmission efficiency. We express the associated optimization problem as a mixed integer nonlinear programming (MINIP) problem, and then solve it in two steps. In the first step, a constrained rate maximization scheduling with a greedy-based three-step strategy is proposed to obtain the concurrent configurations with maximum transmission rate without violating the concurrent decoding requirements and balancing the traffic demands of concurrent nodes. In the second step, we design a low-complexity slot allocation strategy to assign transmission duration for the scheduled concurrent configurations and obtain a near-optimal solution by exploring the intrinsic fractional structure of the introduced individual transmission efficiency (iTE) and system transmission efficiency (STE). Numerical results are extensively studied to show that our proposed design significantly outperforms the existing schemes in terms of transmission efficiency.

Aiming at the poor performance of existing open set recognition algorithms for interference signals in the case of large openness, an open set recognition algorithm for interference signals based on time-frequency features and hyperspheres hybrid loss is proposed. The energy information of time-frequency spectrum is then fused with the signal's time-domain data as inputs to the feature extraction network. This fusion enriches the extracted feature information. The feature extraction network is designed as a one-dimensional network, incorporating the Inception module and the attention mechanism. To reduce the impact of noise and ensure a sufficiently large feature space for the unknown class, a hypersphere hybrid loss function is introduced. This loss function comprises a sub-hypersphere-based cross entropy loss, center loss, and a prototype radius loss. Simulation results show that the unknown class rejection performance under large openness conditions is greatly improved while ensuring no degradation of the recognition performance of the known classes. Additionally, the proposed algorithm exhibits superior performance in open-set recognition tasks with small-sample datasets.

With more and more IoT terminals being deployed in various power grid business scenarios, terminal reliability has become a practical challenge that threatens the current security protection architecture. Most IoT terminals have security risks and vulnerabilities, and limited resources make it impossible to deploy costly security protection methods on the terminal. In order to cope with these problems, this paper proposes a lightweight trust evaluation model TCL, which combines three network models, TCN, CNN, and LSTM, with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device, and the trust evaluation of the terminal's continuous behavior can be achieved by combining the scores of different periods. After experiments, it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763, 94.456, 99.923, and 99.195 on HDFS, BGL, N-BaIoT, and KDD99 datasets, respectively, and the size of TCL is only 91KB, which can achieve similar or better performance than CNN-LSTM, RobustLog and other methods with less computational resources and storage space.

Residual loop-interference (LI) poses a significant challenge for the full-duplex (FD) unmanned aerial vehicle (UAV). To address the issue of residual LI, this paper proposes an amplify-and-forward (AaF) FD-UAV relay system based on a novel Orthogonal frequency division multiplexing (OFDM) technique, in which a signal model of infinite impulse response (IIR) is established, instead of the classical finite impulse response (FIR). In the proposed scheme, the residual LI is considered a useful signal and can be combined with the novel OFDM to establish the IIR signal model. Meanwhile, the guard interval (GI) is designed to maintain the circular convolution structure, which differs from the cyclic prefix (CP) applied by the classical OFDM. At the receiver, the IIR signals are influenced only by Gaussian white noise. The proposed FD-UAV relay system can maintain a satisfactory bit error rate (BER) even in the presence of significant residual LI, compared to conventional solutions for suppressing LI on FD-UAV relay. Numerical simulations validate that our proposed scheme offers a fresh solution to the residual LI problem in FD-UAV communication.

In autonomous driving, there are multiple possibilities with respect to the future motion of agents. However, multimodal prediction is a difficult task. Multimodal trajectory prediction is heavily dependent on the modelling of semantic information contained in high-definition (HD) maps. A proposal-based method is a method that uses human prior knowledge to generate prediction. Typically, a two-stage approach of intention classification followed by trajectory regression is used. However, the performance of this method is highly dependent on the quality of the proposal. To address this issue, this paper proposes a path-planning-based multimodal trajectory prediction network that automatically plans possible future paths for the target agent based on an HD map and uses these paths as pseudo proposals for trajectory prediction. The advantage of pseudo proposals is that the network can adjust the output trajectory of each proposal through learning, reducing the dependence on the quality of the proposal. In addition, we introduce self-supervised learning into trajectory prediction. We construct pretext tasks based on the multimodality of paths to enable the map encoder to learn multimodal features. Experiments with the Argoverse dataset show that the proposed method outperforms existing methods and achieves the best performance in terms of final displacement error.

This paper investigates a wireless powered communication network (WPCN) facilitated by an unmanned aerial vehicle (UAV) in Internet of Things (IoT) networks, where multiple IoT devices (IoTDs) gather energy from a terrestrial energy station (ES) during the wireless energy transfer (WET) stage, followed by the UAV collecting data from these powered IoTDs with the time division multiple access (TDMA) protocol in the wireless information transfer (WIT) stage. To overcome the challenges of radio propagation caused by obstructions, we incorporate a reconfigurable intelligent surface (RIS) to enhance the link quality of the ES-IoTDs and IoTDs-UAV. The primary objective is to maximize the average sum rate of all IoTDs by jointly optimizing UAV trajectory, ES transmit power, and RIS phase shifts, along with the time allocation for WET and WIT. To this end, we reformulate the optimization problem as a markov decision process (MDP) and introduce a deep reinforcement learning (DRL) approach for addressing the formulated problem, called the proximal policy optimization (PPO) based energy harvesting with trajectory design and phase shift optimization (PPO-EHTDPS) algorithm. By continuously exploring within the environment, the PPO algorithm refines its policy to optimize the UAV trajectory, the energy phase shifts, ES transmit power, and WET/WIT time allocation. Additionally, a continuous phase shift optimization algorithm is employed to determine the information phase shifts for each IoTD to maximize average sum rate. Finally, numerical results demonstrate that the proposed PPO-EHTDPS algorithm can significantly achieve higher average sum rate and show better convergence performance over the benchmark algorithms.

The rapid development of wearable devices, as well as the realms of virtual reality (VR), and augmented reality (AR), has garnered significant attention. Anticipated developments in these products include a transition towards greater compactness, an augmentation of their functional capacities, and the integration of energy harvesting capabilities. Meeting these requirements demands technological advancements, particularly in supporting high-rate communications and real-time sensing within the constraints of a limited frequency spectrum. One promising solution lies in extending Orbital Angular Momentum (OAM) within the terahertz (THz) range. The application of OAM in the THz range offers several key benefits. It enables communication within a more confined spectrum without sacrificing data rates. Its unique beam structure and multiple orbital modes allow real-time motion tracking and power transfer with low-interference communication. These features possess the potential to revolutionize communication systems, rendering them more compact and substantially extending their battery life. In this paper, we first commence by elucidating the principles of integrating OAM into the THz band, referred to as OAM-THz. Second, we delve into the advantages and potential applications of this technology. Third, we present a typical case of OAM-THz using a VR scenario and research its methods and theories. Finally, our approach encompasses the identification of potential challenges that may surface, the proposition of viable solutions, and the delineation of prospective research directions within the domain of OAM-THz.

Convolutional Neural Network(CNN); Homomorphic Encryption; Privacy preservation; Fast Homomorphic Convolution

blockchain; PBFT; consensus; reputation

Time Division Multiplexing-Passive Optical Networks (TDM-PON) play a vital role in Fiber-to-the-Home (FTTH) deployments. To improve the service quality of home networks, FTTH is expanding to the Fiber-to-the-Room (FTTR) scenario, where fibers are deployed to connect individual rooms (i.e., Fiber In-premises Network (FIN) in the ITU-T G.9940 standard). In this scenario, a point-to-multipoint (P2MP) fiber network is deployed as FTTR FIN to offer gigabit access to each room, which forms a two-tier cascaded network together with the FTTH segment. To optimize the capacity utilization of the cascaded network and reduce the overall system cost, a centralized architecture, known as Centralized Fixed Access Network (C-FAN), has been introduced. C-FAN centralizes the medium access control (MAC) modules of both the FTTH and FTTR networks at the FTTH’s Optical Line Terminal (OLT) for unified control and management of the cascaded network. We develop a unified bandwidth scheduling protocol by extending the ITU-T PON standard for both the upstream and downstream directions of C-FAN. We also propose a unified dynamic bandwidth allocation (UDBA) algorithm for efficient bandwidth allocation for multiple traffic flows in the two-tier cascaded network. Simulations are conducted to evaluate the performance of the proposed control protocol and the UDBA algorithm. The results show that, in comparison to the conventional DBA algorithm, the UDBA algorithm can utilize upstream bandwidth more efficiently to reduce packet delay and loss, without adversely impacting downstream transmission performance.

A wideband low-profile aperture-coupled antenna based on a novel dual-mode-composite scheme is presented. The mode-composite scheme where the TM10 cavity mode and the TE121 dielectric resonator (DR) mode are combined offers an approach to obtain a wide bandwidth accompanied by stable unidirectional radiation and high efficiency. The use of a lengthened coupling aperture that supports the one-wavelength resonance in the band of interest is an effective feed method of simultaneously exciting the two composite modes without compromising the increased complexity of the antenna geometry. An impedance bandwidth of 49% for |S11| of less than -10 dB, a maximum gain of 10.8 dBi, and stable radiation patterns with low cross-polarization are realized experimentally by a fabricated prototype. Considering the simplicity of the geometry, the wide bandwidth that can cover n77, n78, and n79 bands for the fifth generation (5G) mobile communications and the satisfying radiation performance, the proposed antenna would be a promising candidate for advanced wireless applications.

In this paper, we investigate the position performance of a three-dimensional (3D) positioning system that utilizes multiple reconfigurable intelligent surfaces (RISs) embedded into the wireless communication system as anchor nodes to assist positioning. The position error bounds (PEB) and orientation error bounds (OEB) derived from the Cramer-Rao lower bounds (CRLB) are used to evaluate the estimation performance of the position and orientation of a mobile station (MS) in both synchronous and asynchronous scenarios. The phase shift profile of RIS suitable for positioning is analyzed, and the impact of RIS locations on the position accuracy is investigated. The position performance under different scenarios of blocked direct link, blockage-free direct link, multiple RISs, synchronization, and asynchronization is compared. Moreover, we formulate and solve an optimization problem of the locations of RISs to improve the regional positioning performance of the potential MS in the area of interest. Numerical results show that the synchronous scenario provides better positioning accuracy than the asynchronous scenario, the position performance can be significantly improved with the aid of multiple RISs, and the phase shifts and locations of RISs are important to achieve the performance gain.

Due to the strong error correction ability for short messages, Polar code is applied in 5G system for control channels, and is also applied in space communications. In space communication systems, polar decoder can be efficiently implemented by SRAM-FPGA. However, SRAM-FPGA is very sensitive to the soft errors caused by cosmic particles, and Single Event Upsets (SEUs) is the most common effect. In particular, SEUs on the configuration memory of SRAM-FPGA will change the circuit functionality. Therefore, protection of SRAM-FPGA based polar decoders against SEUs is of great significance. In this paper, we first evaluate the reliability of the FPGA based polar decoder by the fault injection experiments. Then, an Enhanced Duplication with Comparison (E-DWC) based protection scheme is proposed to protect the decoder from SEUs on configuration memory. The hardware evaluation results indicate that the proposed scheme can almost completely eliminate the impact of SEUs on the decoder, and the hardware overhead is 2.11 times that of the unprotected decoder.

Large-scale antennas array is one of the key technologies in future wireless communication, which results in the users operating in the mixed far- and near-filed regions. In this work, we investigate the potential of beam focusing and steering in the mixed field to improve the performance of the simultane- ous wireless information and power transfer (SWIPT) in the intelligent reflecting surface (IRS) aided cell- free systems, where our goal is to maximize the har- vested energy of all receivers by jointly optimizing the transmit and reflective precoding vectors/matrices for the full digital (FD) and hybrid (HY) architec- tures. The formulated non-convex optimization prob- lem with unit-modulus constraints and coupled con- straints is challenging to solve. To address the issue, we transform the transmit and reflective precoding op- timization problem into two convex subproblems for the FD architecture. Then, based on the obtained so- lutions in FD architecture, we propose a block coor- dinate descent for subspace decomposition (BCD-SD) algorithm to design digital and analog precoding vec- tors/matrices for the HY architecture, where the per- formance of HY architecture is close to that of the FD architecture. Numerical results reveal that the pro- posed optimization schemes are more effective than the conventional schemes

This paper proposes a genetic optimization method for the construction of non-binary quasi-cyclic low-density parity-check (NB-QC-LDPC) codes with short block lengths. In our scheme, the initial template base matrices and the corresponding non-binary replacement matrices are constructed by the progressive edge growth algorithm and randomly generated, respectively. The genetic algorithm is then utilized to optimize the base matrices and the replacement ones. The simulation results show that the NB-QC-LDPC codes constructed by the proposed method achieve better decoding performance and lower implementation complexity compared to the existing NB-LDPC codes such as consultative committee for space data system and BeiDou satellite navigation system.

Parallel concatenated codes~(PCC) are widely used in practice. Spatial coupling is useful for enhancing the performance of coding techniques. However, existing constructions of spatially coupled PCC~(SC-PCC) have inferior performance in either the waterfall or error-floor region. Recently, a novel construction of SC-PCC, called hybrid coupled PCCs~(HC-PCCs), was proposed. The preliminary results showed the advantages of HC-PCC in terms of iterative decoding thresholds. In this paper, we intend to give a further investigation on HC-PCC. First, we present the HC-PCC. Second, we derive the density evolution~(DE) equations of HC-PCCs over the binary erasure channels~(BECs). Third, we study the construction of rate-compatible HC-PCCs~(RC-HC-PCCs) via DE analysis. Fourth, we show that we can lower bound for the performance of an HC-PCC with a hybrid concatenated code~(HCC) ensemble. We then derive the weight enumerator of the resulting HCC, which is used in conjunction with the union bound to estimate the error-floor of HC-PCC. Finally, we show numerical results to demonstrate the impact of various parameters on the performance of HC-PCCs. By selecting the parameters appropriately, the performance of HC-PCCs can be significantly enhanced. Furthermore, the simulation results show that HC-PCCs outperform GSC-PCCs and HC-SCCs in the waterfall region.

Over the years and nowadays container technology is widely used among the communities for application deployment and maintenance. Especially, comparing with virtual machines, containers are lightweight and occupy fewer hardware resources since they share the kernel with the host system. However, due to the weak isolation of container mechanisms, the host system or other containers are vulnerable when a container is attacked exploiting the kernel vulnerabilities. Besides, unexpected control of container engines and tainted images are also threats to containers. Thus, it is important to know about the security patterns of these issues toward enhancing security. To this, in this paper, we present an empirical study of container escape, which is a kind of risk to gain permissions to take control of other containers or the host from one container. Basically, our study includes the common patterns, root causes, exploits, possible fixes, and many more. Particularly, we study nine (09) related vulnerabilities discovered in recent years by analyzing their root causes, deploying environments to simulate the attack and comparing the official patches. For some of these vulnerabilities, we also present alternative defense or fix methods. Additionally, we summarize our learning outcomes for each vulnerability, and propose further analysis using these experiences.

Spectrum sensing is crucial for enabling opportunistic spectrum access (OSA) in cognitive radio (CR). However, the accuracy of spectrum sensing can be compromised by several malicious attacks, such as primary user emulation attack (PUEA). A PUEA represents that the attacker sends imitated primary signal to mislead spectrum sensing results and thereby prevent the secondary users from accessing the idle spectrum band, thereby leading to a low spectrum utilization. In this paper, we propose a novel intelligent reflecting surface (IRS)-enhanced cooperative spectrum sensing (CSS) scheme and exploit IRS to suppress PUEA. The maximal ratio combining (MRC) is adopted as the fusion rule for CSS. To this end, an optimization problem is formulated to maximize the probability of detection by designing the weight coefficients of MRC at a fusion center (FC) and the phase shifts of the elements in IRS. To solve the non-convex problem with coupled variables, an efficient alternating optimization (AO) algorithm is proposed. In particular, the optimal weight coefficients are obtained by adopting the transformation of Rayleigh quotient. Then, semi-definite relaxation (SDR), Charnes-Cooper transformation and Gaussian Randomization are exploited to obtain the solutions of IRS phase shifts. Simulation results demonstrate that the proposed scheme is computationally efficient and can improve the sensing performance while reducing the impact of PUEA.

Reconfigurable intelligent surface (RIS) is an economically effective solution for improving the spectral efficiency and energy efficiency. The sum capacity of an RIS-aided multiple-input multiple-output (MIMO) broadcast channel (BC) depends on the phase shifts. This motivates us to investigate the problem of phase shift optimization in an RIS-aided MIMO BC via sum capacity maximization in this letter. More concretely, we consider a common scenario where there is only a finite number of discrete phase shifts available for each element of the RIS. By exploiting the duality between the MIMO BC and the MIMO multiple access channel, the RIS phase shifts are optimized along with the users' input covariance matrices through maximizing the sum capacity using an alternating optimization algorithm. In order to deal with the optimization subproblems, an accelerated algorithm based on water-filling with fast convergence is proposed to optimize the input covariance matrices, and a channel separation technique with reduced computational complexity is exploited for RIS phase shift optimization. Simulation results are illustrated to verify the effectiveness and superiority of the proposed method.

As a common marine atmospheric structure, evaporation duct can provide favorable conditions for the long-range propagation of terahertz waves. To further study and predict the propagation characteristics of terahertz waves in evaporation duct, a new method is proposed to calculate the variable atmospheric absorption loss. Combined with the parabolic equation, the propagation model is constructed. The propagation characteristics of terahertz waves under different propagation distances, frequencies and evaporation duct heights (EDHs) are studied. The results show that the propagation factor of terahertz waves is mainly manifested by atmospheric absorption, and the absorption loss increases with the increase of frequency. The effect of EDH on propagation factor is related to frequency and propagation distance, and the change of EDH is accompanied by the jitter of propagation factor. The higher the frequency and the greater the propagation distance, the greater the jitter amplitude of propagation factor caused by changing EDH. The influence of the heights of transmitting and receiving antennas is further discussed, and the optimal setting heights of antennas for 140 GHz terahertz wave at different distances are given, which provides theoretical basis and numerical reference for effective planning and propagation experiment of terahertz wireless communication systems on the sea.

With the rapid development of Intelligent Cyber-Physical Systems (ICPS), diverse services with varying Quality of Service (QoS) requirements have brought great challenges to traditional network resource allocation. Furthermore, given the open environment and a multitude of devices, enhancing the security of ICPS is an urgent concern. To address these issues, this paper proposes a novel trusted virtual network embedding (T-VNE) approach for ICPS based combining blockchain and edge computing technologies. Additionally, the proposed algorithm leverages a Deep Reinforcement Learning (DRL) model to optimize decision-making processes. It employs the policy-gradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search (BFS) algorithm to determine the optimal embedding paths. Finally, through simulation experiments, the efficacy of the proposed method was validated, demonstrating outstanding performance in terms of security, revenue generation, and Virtual Network Request (VNR) acceptance rate.

The rapid increase in the number of Low Earth orbit (LEO) satellites launched into space has brought unprecedented spectrum pressure. In order to obtain more flexible available bandwidth, current LEO operators are seeking to share spectrum with existing GEO satellites. In order to improve the capacity of the LEO system in spectrum sharing scenarios without interfering with the GEO system, the resources allocation problem of the LEO satellite system is studied in this paper. By modeling the problem as a mixed integer nonlinear programming problem, and solving three sub-problems decomposed by it, a frequency multiplexing based beam hopping algorithm is proposed to reasonably allocate space, frequency and power resources of the LEO system. The simulation results show that the proposed algorithm not only ensures the signal quality of GEO users, but also effectively improves the LEO system capacity.

Differential pulse-position modulation (DPPM) can achieve a good compromise between power and bandwidth requirements. However, the output sequence has undetectable insertions and deletions. This paper proposes a successive cancellation (SC) decoding scheme based on the weighted Levenshtein distance (WLD) of polar codes for correcting insertions/deletions in DPPM systems. In this method, the WLD is used to calculate the transfer probabilities recursively to obtain likelihood ratios, and the low-complexity SC decoding method is built according to the error characteristics to match the DPPM system. Additionally, the proposed SC decoding scheme is extended to list decoding, which can further improve error correction performance. Simulation results show that the proposed scheme can effectively correct insertions/deletions in the DPPM system, which enhances its reliability and performance.