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  • Yang Jie, Wang Langqi, Wang Yichen, Yan Wei, Wang Yifei, Zhu Jiajia
    Received: 2024-05-09; Revised: 2024-08-28; Accepted: 2024-09-29; Online: 2024-10-29
    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.
  • Zhijin Qin, Jingkai Ying, Gangtao Xin, Pingyi Fan, Wei Feng, Ning Ge, Xiaoming Tao
    Received: 2024-04-24; Revised: 2024-08-14; Accepted: 2024-10-22; Online: 2024-10-29
    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.
  • Nima Azadi-Tinat, Mohsen Koohestani
    Received: 2024-07-14; Revised: 2024-08-30; Accepted: 2024-09-25; Online: 2024-10-29
    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| Low Complexity Successive Cancellation List Decoding of U-UV Codes
    Wenhao Chen, Li Chen, Jingyu Lin, Huazi Zhang
    Received: 2024-01-19; Revised: 2024-08-21; Accepted: 2024-09-11; Online: 2024-10-29
    Constituted by BCH component codes and its ordered statistics decoding (OSD), the successive cancellation list (SCL) decoding of U-UV structural codes can provide competent error-correction performance in the short-to-medium length regime. However, this list decoding complexity becomes formidable as the decoding output list size increases. This is primarily incurred by the OSD. Addressing this challenge, this paper proposes the low complexity SCL decoding through reducing the complexity of component code decoding, and pruning the redundant SCL decoding paths. For the former, an efficient skipping rule is introduced for the OSD so that the higher order decoding can be skipped when they are not possible to provide a more likely codeword candidate. It is further extended to the OSD variant, the box-andmatch algorithm (BMA), in facilitating the component code decoding. Moreover, through estimating the correlation distance lower bounds (CDLBs) of the component code decoding outputs, a path pruning (PP)-SCL decoding is proposed to further facilitate the decoding of U-UV codes. In particular, its integration with the improved OSD and BMA is discussed. Simulation results show that significant complexity reduction can be achieved. Consequently, the U-UV codes can outperform the cyclic redundancy check (CRC)-polar codes with a similar decoding complexity.
  • Huihui Xu, Hongying Tang, Jiang Wang, Xiaobing Yuan
    Received: 2023-04-17; Revised: 2024-08-16; Accepted: 2024-09-29; Online: 2024-10-29
    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.
  • Yinzhi Wang, Dai Shaogang Dai, Keqiang Yue,Shilian Zheng
    Received: 2024-06-15; Revised: 2024-07-24; Accepted: 2024-08-28; Online: 2024-09-24
    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.
  • Sun Pengzhan, Ren Yinlin, Shao Sujie, Yang Chao, Qiu Xuesong
    Received: 2023-08-02; Revised: 2024-04-19; Accepted: 2024-08-28; Online: 2024-09-24
    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.
  • Luo Jiehao, Kong Dejin, Luo Shuang, Wang Baobing, Deng Zaihui
    Received: 2024-03-10; Revised: 2024-07-25; Accepted: 2024-08-28; Online: 2024-09-24
    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.
  • Ziqi Zhao, Yiping Duan, Xiaoming Tao, Ming Li, Zhoujuan Cui
    Received: 2024-03-21; Revised: 2024-07-13; Accepted: 2024-08-28; Online: 2024-09-24
    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.
  • Wei Chen, Yulong Zou, Liangsen Zhai
    Received: 2024-02-21; Revised: 2024-06-04; Accepted: 2024-08-28; Online: 2024-09-24
    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.
  • Muyao Wang, Wenchi Cheng, Hailin Zhang
    Received: 2023-11-08; Revised: 2024-05-21; Accepted: 2024-08-28; Online: 2024-09-24
    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.
  • Chen Liquan, Zhang Peng, Wang Yu, Yang Zixuan, Song Yufan
    Received: 2024-03-01; Revised: 2024-07-17; Accepted: 2024-08-28; Online: 2024-09-24
    Convolutional Neural Network(CNN); Homomorphic Encryption; Privacy preservation; Fast Homomorphic Convolution
  • Ze Chai, Zhipeng Gao, Yang Yang, Yijing Lin, Huangqi Li, Lanlan Rui
    Received: 2022-09-20; Revised: 2023-12-01; Accepted: 2024-08-28; Online: 2024-09-24
    blockchain; PBFT; consensus; reputation
  • Li Jun, Lu Xiang, Wang Xiang, Chang Tianghai, Bose Sanjay Kumar, Shen Gangxiang
    Received: 2024-03-08; Revised: 2024-06-21; Accepted: 2024-07-22; Online: 2024-09-04
    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.
  • Junyao Tan, Yujian Li, Junhong Wang, Bo Ai, Ruisi He
    Received: 2024-02-01; Revised: 2024-04-17; Accepted: 2024-06-25; Online: 2024-09-04
    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.
  • Sheng Hong, Pengzhen Xu, Xiang Li, Yuhao Wang, An Li
    Received: 2023-04-17; Revised: 2024-06-21; Accepted: 2024-07-25; Online: 2024-09-04
    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.
  • Gao Zhen, Wang Jingyan, Wang Ruize, Zhu Jinhua, Tian Dong
    Received: 2024-01-05; Revised: 2024-05-09; Accepted: 2024-05-30; Online: 2024-07-16
    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.
  • Liu Zhenwei, Zhang Wenjie, Cao Qi, Huang Wei, Kai Caihong
    Received: 2024-02-17; Revised: 2024-05-06; Accepted: 2024-05-13; Online: 2024-07-16
    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
  • Zhang Yidi, Jiang Ming, Zhao Chunming
    Received: 2023-12-19; Revised: 2024-04-25; Accepted: 2024-06-25; Online: 2024-07-16
    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.
  • Xiao Han, Tian Wenqiang, Jin Shi, Liu Wendong, Shen Jia, Shi Zhihua, Zhang Zhi
    Received: 2024-05-06; Revised: 2024-06-17; Accepted: 2024-06-25; Online: 2024-07-16
    In this paper, an interference cancellation based neural receiver for superimposed pilot (SIP) in multi-layer transmission is proposed, where the data and pilot are non-orthogonally superimposed in the same time-frequency resource. Specifically, to deal with the intra-layer and inter-layer interference of SIP under multi-layer transmission, the interference cancellation with superimposed symbol aided channel estimation is leveraged in the neural receiver, accompanied by the pre-design of pilot code-division orthogonal mechanism at transmitter. In addition, to address the complexity issue for inter-vendor collaboration and the generalization problem in practical deployments, respectively, this paper also provides a fixed SIP (F-SIP) design based on constant pilot power ratio and scalable mechanisms for different modulation and coding schemes (MCSs) and transmission layers. Simulation results demonstrate the superiority of the proposed schemes on the performance of block error rate and throughput compared with existing counterparts.
  • Zhao Shancheng, Li Guorong, Yang Chaojie, Wen Jinming
    Received: 2024-03-25; Revised: 2024-04-26; Accepted: 2024-06-25; Online: 2024-07-16
    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.
  • Subrota Kumar Mondal, Pan Wenxi, Dai Hongning, Chen Yijun, Wang Haocheng
    Received: 2023-08-17; Revised: 2024-04-29; Accepted: 2024-06-25; Online: 2024-07-16
    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.
  • Wang Zi, Wang Lingyi, Wu Wei
    Received: 2023-09-05; Revised: 2024-03-20; Accepted: 2023-06-25; Online: 2024-07-16
    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.
  • Liu Haiwei, Liao Bin
    Received: 2024-02-20; Revised: 2024-06-09; Accepted: 2024-06-25; Online: 2024-07-16
    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.
  • Hao Jianhong, Cao Xiangchun, Zhao Qiang, Zhang Fang, Fan Jieqing, Dong Zhiwei
    Received: 2023-10-18; Revised: 2024-03-19; Accepted: 2024-06-25; Online: 2024-07-16
    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.
  • Zhu Hailong, Huang Tao, Zhang Yi, Chen Ning, Zhang Peiying
    Received: 2024-03-11; Revised: 2024-04-16; Accepted: 2024-06-25; Online: 2024-07-16
    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.
  • Liu Botao, Yang Mingchuan, Xue Guanchang, Yang Yupu, Liu Xiaofeng
    Received: 2023-11-04; Revised: 2024-03-24; Accepted: 2024-06-25; Online: 2024-07-16
    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.
  • Li Leran, Liu Yuan, Yuan Ye, Xiahou Wenqian, Chen Maonan
    Received: 2023-10-24; Revised: 2023-05-20; Accepted: 2023-06-25; Online: 2024-07-16
    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.
  • Yang Xiaoniu, Qian Liping, Lyu Sikai, Wang Qian, Wang Wei
    Received: 2024-01-17; Revised: 2024-06-12; Accepted: 2024-06-25; Online: 2024-07-16
    To address the contradiction between the explosive growth of wireless data and the limited spectrum resources, semantic communication has been emerging as a promising communication paradigm. In this paper, we thus design a speech semantic coded communication system, referred to as Deep-STS (i.e., Deep-learning based Speech To Speech), for the low-bandwidth speech communication. Specifically, we first deeply compress the speech data through extracting the textual information from the speech based on the conformer encoder and connectionist temporal classification decoder at the transmitter side of Deep-STS. In order to facilitate the final speech timbre recovery, we also extract the short-term timbre feature of speech signals only for the starting 2s duration by the long short-term memory network. Then, the Reed-Solomon coding and hybrid automatic repeat request protocol are applied to improve the reliability of transmitting the extracted text and timbre feature over the wireless channel. Third, we reconstruct the speech signal by the mel spectrogram prediction network and vocoder, when the extracted text is received along with the timbre feature at the receiver of Deep-STS. Finally, we develop the demo system based on the USRP and GNU radio for the performance evaluation of Deep-STS. Numerical results show that the accuracy of text extraction approaches 95%, and the mel cepstral distortion between the recovered speech signal and the original one in the spectrum domain is less than 10. Furthermore, the experimental results show that the proposed Deep-STS system can reduce the total delay of speech communication by 85% on average compared to the G.723 coding at the transmission rate of 5.4 kbps. More importantly, the coding rate of the proposed Deep-STS system is extremely low, only 0.2 kbps for continuous speech communication. It is worth noting that Deep-STS with lower coding rate can support the low-zero-power speech communication, unveiling a new era in ultra-efficient coded communications.
  • Yan Liuyan, Li Lang, Huang Xiantong
    Received: 2023-11-02; Revised: 2024-01-29; Accepted: 2024-06-25; Online: 2024-07-16
    Data confidentiality relies on cryptographic algorithms to a great extent. The critical nonlinear building block of cryptography is the S-box which plays a role in the design of various cryptographic algorithms to improve security and complexity. Therefore, a kind of newly dynamic and involutory S-box (DINV-S) was proposed in this paper. DINV-S is comprised of two-round Feistel structure. The proposed method has the capability to generate 256 parameter-dependent dynamic S-boxes within the F-function of each round in the Feistel structure by linearly reformulating any 8 × 8 static S-box. Theoretical analysis shows that differential uniformity and nonlinearity of DINV-S can be consistent with the static S-box. Additionally, specific experiments are carried out using AES S-box in conjunction with this method. The generated S-boxes in the F-function are found to outperform AES S-box in terms of average iteration cycle. The dynamic S-box derived by AES S-box has the optimal differential probability (DP=4/256), the optimal linear probability (LP=16/256), and the highest algebraic degree (def(S)=7).
  • Liu Fangfang, Wang Sicheng, Sun Lunan, Yang Yang
    Received: 2024-03-20; Revised: 2024-05-20; Accepted: 2024-06-25; Online: 2024-07-16
    Given the increasing demand for secure transmission and advancements in steganalysis techniques, it is necessary to design a steganography algorithm with enhanced steganography performance to ensure information security. In this paper, we preposed a semantic-weighted image steganography algorithm (SISA) based on convolutional neural network (CNN) . Firstly, SISA is proposed to trade off steganography capacity and performance. SISA adopts an autoencoder consisting of a semantic-based preparation-network, a hiding-network, and a reveal-network. Subsequently, a semantic-weighted and secure-enhanced image steganography algorithm (SSISA) is further proposed to trade off steganography capacity, performance and security. SSISA adopts a generative adversarial network (GAN) consisting of a generative network and a discrimination network. SSISA makes minimal sacrifices in steganography performance while significantly improving security. Experimental results show that two proposed steganography algorithms not only realize large-capacity image steganography, but also achieve state-of-the-art steganography performance. The average pixel deviation (APD) between the embedded images and the cover images is reduced by up to about 36.73% using the proposed SISA, compared with the existing algorithms that do not consider semantic weighting. The steganalysis detection rate is reduced by up to about 6.75% using the proposed SSISA, compared with the existing algorithms that do not consider security enhancement.
  • Atefeh Roostaei, Mostafa Derakhtian
    Received: 2023-12-04; Revised: 2024-05-05; Accepted: 2024-06-25; Online: 2024-07-16
    The quality of spectrum sensing plays a significant role in determining the outage probability during the data transmission phase in an interweave cognitive radio network. If the secondary user (SU) fails to detect the primary user (PU) activity, it can result in interference that limits the system performance. Additionally, since the wireless medium is broadcast in nature, there is a risk of eavesdroppers intercepting the cognitive users' data. Therefore, it is crucial to consider secrecy in the system analysis. In this paper, we analyze the secrecy outage probability (SOP) at the secondary receiver and derive the secret diversity gain for an interweave cognitive multiple-input-multiple-output (MIMO) fading channel in the presence of an eavesdropper. Our study takes into account the effects of the fading channel, the PU interference, and the eavesdropper on both spectrum sensing and data transmission phases. We demonstrate that utilizing all the antennas for sensing eliminates the limiting effects of missed detection probability and PU interference on the secret diversity gain. As a result, the cognitive user can achieve the same level of secret diversity gain as a conventional non-cognitive system (CNCS). Our analytical results are further validated through simulations.
  • G Indumathi, R Sarala
    Received: 2023-11-27; Revised: 2024-05-25; Accepted: 2024-06-25; Online: 2024-07-16
    Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer. However, the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services. Moreover, the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources. In this paper, a Fire hawk Optimization-Enabled Deep Learning Scheme (FHOEDLS) is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability-based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications. It specifically adopted the Fire Hawk Optimization Algorithm (FHOA) for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead. It considered the factors of resource capacity, transmission cost, demand, and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration. The cloud simulation of this FHOEDLS is achieved using a container, Virtual Machine (VM), and Physical Machine (PM). The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.
  • Peng Xiang, Xu Hua, Qi Zisen, Wang Dan, Zhang Yue, Rao Ning, Gu Wanyi
    Received: 2023-08-24; Revised: 2024-02-07; Accepted: 2024-05-07; Online: 2024-06-14
    This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks (WCNs). We propose a novel jamming channel allocation and power decision-making (JCAPD) approach based on multi-agent deep reinforcement learning (MADRL). In high-dynamic and multi-target aviation communication environments, the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information. This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning (DRL) approaches. In response, we design a distributed multi-agent decision architecture (DMADA). We formulate multi-jammer resource allocation as a multi-agent Markov decision process (MDP) and propose a fingerprint-based double deep Q-Network (FBDDQN) algorithm for solving it. Each jammer functions as an agent that interacts with the environment in this framework. Through the design of a reasonable reward and training mechanism, our approach enables jammers to achieve distributed cooperation, significantly improving the jamming success rate while considering jamming power cost, and reducing the transmission rate of links. Our experimental results show the FBDDQN algorithm is superior to the baseline methods.
  • Mei Miao, Tang Miao, Zhou Long
    Received: 2024-02-27; Revised: 2024-04-15; Accepted: 2024-05-07; Online: 2024-06-14
    The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era, the quick development of telecommunications services, the implementation of the number portability policy, and the intensifying competition among operators. At the same time, users’ consumption preferences and choices are evolving. Excellent churn prediction models must be created in order to accurately predict the churn tendency, since keeping existing customers is far less expensive than acquiring new ones. But conventional or learning-based algorithms can only go so far into a single subscriber’s data; they cannot take into consideration changes in a subscriber’s subscription and ignore the coupling and correlation between various features. Additionally, the current churn prediction models have a high computational burden, a fuzzy weight distribution, and significant resource economic costs. The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures, ignoring the reference value supplied by other users with the same package. This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network (GAT-CNN) to address the aforementioned issues. The main contributions of this paper are as follows: Firstly, we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device, edge, and cloud layers. Second, we extend the use of users’ own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously. Lastly, we build an integrated offline-online system for churn prediction based on the strengths of the two models, and we experimentally validate the efficacy of cloud-side collaborative training and inference. In summary, the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.
  • Sun Hao, Jing Wenpeng, Lu Zhaoming, Wen Xiangming, Zheng Ziyuan, Liu Changhao, Li Wei
    Received: 2023-11-08; Revised: 2024-01-18; Accepted: 2024-05-07; Online: 2024-06-14
    Progressive pitch proposed by OneWeb is a promising solution to prevent huge non-geostationary orbit (NGSO) satellite constellations from interfering with geostationary orbit (GSO) satellite systems. However, the traditional progressive pitch primarily focuses on the protection of GSO systems, while the energy efficiency of NGSO systems is not optimized. In this paper, we propose an energy-efficient progressive pitch strategy for NGSO satellites in the spectral coexistence scenario between GSO and NGSO systems. By combining power control and spatial isolation, this strategy can not only protect the GSO system from interference, but also reduce power consumption and improve the quality of service for the NGSO system. Specifically, we formulate a joint pitch angle and beam power optimization problem to maximize energy efficiency of the NGSO system. Due to the fractional form of the energy efficiency function, we reformulate it into an approximately equivalent convex one by quadratic transform. Besides, the non-convex interference constraint can be solved by a low-complexity binary search method. Lastly, a joint power and pitch angle alternating optimization (JPPA-AO) algorithm is proposed to deal with this multivariate optimization problem. Simulation results demonstrate the proposed strategy outperforms conventional methods in communication capacity and energy reduction for the NGSO system.
  • Zhang Zepeng, Li Cuiran, Wu Hao, Xie Jianli
    Received: 2024-02-04; Revised: 2024-03-13; Accepted: 2024-05-07; Online: 2024-06-14
    This paper investigates the Reconfigurable Intelligent Surface (RIS)-aided MIMO covert communications in high-speed railway (HSR) scenario. In the scenario, RIS controls the phases of reflection elements dynamically to send the signal in the desired direction, which facilitates the covert communication between base station (BS) and train mobile relay (MR) in the existence of a watchful warden (Willie). To protect the desired transmission, it is assumed that MR sends out jamming signals with a varying power to confuse the Willie. Considering the Doppler spread caused by the time-varying wireless channel, the joint optimization problem of the BS beamforming matrix, MR beamforming matrix, and the RIS phase shifts is established to maximize the covert throughput. An alternating optimization (AO) method for handling non convex problems is proposed based on coupling effects and the constraints of constant modulus, and a semidefinite relaxation method is provided. Finally, we achieve the optimal solutions of the multi-objective optimization problem by interior-point method. The simulation results demonstrate that the proposed algorithm exhibits the superior robustness and covert performances in high-speed railway scenarios.
  • Zou Guoxue, Wang Nina, Zhang Zongshuai, Tian Yu, Zou Wenhao, Tian Lin
    Received: 2023-11-06; Revised: 2024-04-23; Accepted: 2024-05-28; Online: 2024-06-14
    Mobile edge networks (MENs) based on integrated computing and communication are the main trends in the future with the emergence of mobile edge computing. This development brings computing, storage, and network resources closer to the edge, improving the quality of experience for users. However, the large-scale deployment of MENs has led to increased energy consumption and resource synergy issues due to network densification and service diversification. Therefore, improving the resource synergy efficiency of MENs for energy efficiency (EE) is a key issue for future networks. In this paper, we first review EE optimization techniques in traditional cellular networks and discuss the opportunities and challenges these techniques present for MENs. Furthermore, we examine the evolution and role of typical resource sharing scenarios and key technologies in MENs. Furthermore, we explore ways to optimize EE for typical mobile scenarios. Lastly, we discuss the future challenges and directions in EE optimization research within MENs.
  • Xiao Wenshi, Luo Zhongqiang, Zhang Xueqin
    Received: 2023-10-31; Revised: 2024-04-10; Accepted: 2024-05-22; Online: 2024-06-14
    Deep learning techniques have been extensively validated and recognized in the field of modulation identification. However, in many practical communication scenarios, such as underwater acoustic communication systems and radar communication systems with various channel variations, it is difficult to obtain a large number of labeled signal samples. In such scenarios, deep learning-based modulation identification methods are prone to overfitting, leading to poor recognition performance. Therefore, improving the modulation identification performance in environments with limited signal samples is a challenging problem. For solving this problem, this paper proposes a method based on data enhancement and migration learning automatic modulation recognition. This method first uses a data enhancement mechanism proposed in this paper to achieve signal sample expansion. Then, it combines the reconstructed signal with the original signal to achieve data enhancement. Secondly, a transfer learning model called Residual Dense Long and Short Term Memory Networks (ResLDNN) is designed for modulation recognition. This model is trained on AMR2016.10a data as the source domain data and fine-tuned on a small number of signal samples as the target domain, ultimately achieving modulation recognition for small sample signals. In order to validate the effectiveness of the proposed data augmentation method, experiments were conducted on both the original dataset and the augmented dataset. The results of the experiments demonstrate that the data augmentation method significantly improves the modulation recognition rate of the model. Additionally, experiments were conducted on datasets with different numbers of signal samples to verify the recognition performance of the ResLDNN model in a small sample environment. The experimental results show that the proposed ResLDNN model achieves higher recognition accuracy than the baseline model in a small sample environment, further confirming that the proposed method based on data augmentation and transfer learning effectively addresses the modulation recognition problem in a small sample environment.
  • Zhang Afei, Zhu Jia, Zou Yulong, Li Yizhi, Qin Hao, Hui Hao
    Received: 2023-11-30; Revised: 2024-03-22; Accepted: 2024-05-07; Online: 2024-06-14
    This paper considers a multi-antenna access point (AP) transmitting secrecy message to a single-antenna user in the presence of a single-antenna illegal eavesdropper (Eve) and proposes a double active reconfigurable intelligent surfaces (DARISs) assisted physical layer security (PLS) scheme denoted by DARISs-PLS to protect the secrecy message transmission. We formulate a secrecy rate maximization problem for the proposed DARISs-PLS scheme by considering a power budget constraint for the two active reconfigurable intelligent surfaces (ARISs) and AP. To address the formulated optimization problem, we jointly optimize the reflecting coefficients for the two ARISs and the beamforming at the AP in an iterative manner by applying Dinkelbach based alternating optimization (AO) algorithm and a customized iterative algorithm together with the semidefinite relaxation (SDR). Numerical results reveal that the proposed DARISs-PLS scheme outperforms the double passive reconfigurable intelligent surfaces-assisted PLS method (DPRISs-PLS) and single ARIS-assisted PLS method (SARIS-PLS) in terms of the secrecy rate.