Time-sensitive networking (TSN) has emerged as a promising communication technique for hard real-time embedded systems. TSN generally comprises a control plane (i.e., planner) and a data plane (e.g., switch chip). While the planner generates policies that map critical traffics into the resources temporally and spatially, the switch chip forwards packets according to pre-scheduled results without any deviation. However, we observed that practical forwarding behaviors are inconsistent with the prescheduled results from the time dimension. We formalized and named the above phenomena an inconsistent problem, which leads to non-deterministic latency and losses of critical packets during packet forwarding. To address this problem, we propose the dual guard band (D-GB) mechanism to dissect the inconsistent problem. D-GB abstracts cross-queue and cross-chip guard bands based on the fine-grained latency analysis of the general forwarding model. Existing TSN planners integrating the D-GB can guarantee that no inconsistent problem occurs when critical packets are forwarded. We build an industrial sensing topology based on real-life TSN switch chips to verify our analysis and proposed mechanism. The results demonstrate that the D-GB mechanism solves the inconsistent problem.

Addressing the joint optimization problem of dual-function radar-communication (DFRC) waveforms, this paper proposes an enhanced waveform design method for multiple-input multiple output (MIMO) radar-communication systems. This approach is designed to minimize multi-user interference (MUI) energy in the downlink while adhering to constraints on total power, peak-to-average power ratio (PAPR), and the Cram′er-Rao Bound (CRB) for angle of arrival estimation. First, the study solves the optimal solution for both omnidirectional and directional beampattern design problems. Subsequently, a flexible trade-off optimization between radar and communication performance is conducted under the given constraints. Furthermore, this study introduces a joint optimization algorithm combining the golden section search and the alternating direction method of multipliers (ADMM), capable of solving for optimal solutions with low computational complexity for multiple variables. Finally, the Branch-and-Bound (BnB) algorithm is updated to address the constant modulus design problem, thereby significantly improving the algorithm’s convergence speed. Simulation experiments demonstrate the impact of the proposed constraints on the performance of the dual-functional system and verify that the designed waveforms are able to achieve an effective trade-off between MIMO radar and communication performance.

Time synchronization is a prerequisite for ensuring determinism in Time-Sensitive Networking. While time synchronization errors cannot be overlooked, pursuing minimal time errors may incur unnecessary costs. Using complex network theory, this study proposes a hierarchy for TSN and introduces the concept of bounded time error. A coupling model between traffic scheduling and time synchronization is established, deriving functional relationships among end-to-end delay, delay jitter, gate window, and time error. These relationships illustrate that time errors can trigger jumps in delay and delay jitter. To evaluate different time errors impact on traffic scheduling performance, an end-to-end transmission experiment scheme is designed, along with the construction of a TSN test platform implementing two representative cases. Case A is a closed TSN domain scenario with pure TSN switches emulating closed factory floor network. Case B depicts remote factory interconnection where TSN domains link via non-TSN domains composed of OpenFlow switches. Results from Case A show that delay and delay jitter on a single node are most significantly affected by time errors, up to one gating cycle. End-to-end delay jitter tends to increase with the number of hops. When the ratio of time error boundary to window exceeds 10%, the number of schedulable traffic flows decreases rapidly. Case B reveals that when time error is below 1 μs, the number of schedulable traffic flows begins to increase significantly, approaching full schedulability at errors below 0.6 μs.

As the demand for ocean exploration and exploitation increases, acoustic communication holds paramount importance and finds extensive applications ranging from scientific data collection at seabed stations to marine geological research. However, data transmission rate of underwater acoustic communication is constrained by the narrow bandwidth due to severe attenuation of high-frequency sounds underwater. By introducing orbital angular momentum (OAM) into the realm of acoustics, there arises a significant enhancement in the transmission capacity and spectral efficiency of underwater acoustic communication systems. This paper aims to study the concept and evolution of orbital angular momentum, elucidate its impact on the field of underwater acoustic communication, and offers insights into future development trends.

With the development of new Internet services with computation-intensive and delay-sensitive tasks, the traditional ``Best Effort" network communication mode has been greatly challenged. The network system is urgently required to provide end-to-end communication determinacy and computing determinacy for new applications to ensure the safe and efficient operation of services. Based on the research of the convergence of computing and networking, a new network paradigm named deterministic computing power networking (Det-CPN) is proposed. In this article, we firstly introduce the research advance of computing power networking. And then the motivations and scenarios of Det-CPN are analyzed. Following that, we present the system architecture, technological capabilities, workflow as well as key technologies for Det-CPN. Moreover, performance evaluation and simulation results are presented to illustrate the performance of the proposed scheme. Finally, the challenges and future trends of Det-CPN are analyzed and discussed.

Machine learning-based localization techniques have demonstrated effectiveness in complex indoor environments. However, they often suffer from overfitting issues due to oversimplified mappings between received signals and estimated positions. This paper presents a deep variational model-based indoor localization system for 5G networks. The proposed approach characterizes channel state information by constructing both location-dependent and location-independent latent variables. By extracting location-dependent features, our deep variational model jointly mitigates time-difference-of-arrival and angle-of-arrival estimation errors, thereby significantly improving localization accuracy. We validated the proposed system in complex indoor environments using a 3GPP-compliant 5G testbed, demonstrating its superior performance over traditional machine learning methods.

The high mobility of UAVs poses significant challenges for millimeter-wave beam prediction. Traditional beam training methods suffer from intolerable signaling overhead and face difficulties in establishing a reliable link within the channel coherence time. To address this, we propose a Beam Prediction Model (MIS) based on an Intelligent Multimodal Information Screening Mechanism. This model fuses UAV multimodal information and uses Recursive Feature Elimination (RFE) to dynamically identify the key modalities for the task. We designed a lightweight early fusion network architecture and combined it with a multilayer perceptron (MLP) to establish robust associations between key UAV information and the optimal beam index, enabling fast and accurate beam prediction. Experimental results on the Deepsense6G real-world dataset show that the MIS model can achieve 79.63% top-1 prediction accuracy and 98.41% top-3 prediction accuracy without the need for beam search. Compared to traditional position-assisted beam prediction models, it improves top-1 accuracy by 19.66%, and reduces measurement overhead by 69.14% compared to local beam search methods. This research provides an engineering-feasible beam prediction solution for high-mobility UAV communication systems.

Federated Learning (FL) is an innovative machine learning paradigm that eliminates the need to upload user data to a central server, enabling efficient collaborative learning across multiple distributed devices while ensuring data privacy and compliance with data localization regulations. However, in real-world scenarios, the inherent unreliability of wireless communication, such as network latency, packet loss, and connection interruptions, can cause unstable transmission of model updates, greatly hindering its effectiveness. This paper analyzes the influence of transmission errors on the convergence rate of the FL algorithm and formulates an adaptive power control problem to maximize the convergence rate. By solving this problem, we dynamically adjust the transmit power of each client according to the network condition and speed up the convergence process of the global model. Through extensive experiments, we not only prove the convergence of the adaptive power control algorithm but also demonstrate its significant performance improvement in the FL scenario characterized by unreliable wireless links.

Compared with one stage predistortion, the multistage predistortion that adopts a cascade of several models can further compensate the strong nonlinear distortions. However, the multistage method requires more complex identification process. To simplify the identification process and improve the generalization performance, we propose a novel two stage DPD based on generalized advanced neural network (GANN) model. In this framework, GANN is inserted into the DPD architecture as the first stage model. The residual nonlinearity of the power amplifier (PA) is compensated by a simplified GMP model in the second stage. By injecting the embedding vector into GANN, the GANN model is able to adapt to PAs with different states such as different bandwidths, power back-offs and other conditions. The proposed method is performed on a Doherty PA driven by the orthogonal frequency division multiplexing (OFDM) signals for experimental validation. The proposed two stage DPD based on GANN is validated to compensate strong nonlinear distortions and achieve the similar good linearization as the other multistage method while having low complexity of the identification process under different bandwidths and power conditions.

Integrated Sensing And Communication (ISAC) is one of the key technologies of 6G networks. With the development of large-scale array technology, target positioning estimated by Direction of Arrival (DOA)is indispensable in ISAC application scenarios. To address the limitations of traditional uniform linear arrays in expanding array aperture effectively due to spatial sampling theorem constraints, a novel atomic norm minimization algorithm based on generalized coprime Arrays is proposed. Firstly, an optimized co-prime array model is constructed by compressing inter-element spacing and introducing sub-array dis placement. Then, by utilizing array interpolation to f ill the holes, a virtual uniform linear array is con structed to fully utilize virtual array elements. Finally, the properties of virtual domain atomic norms are an alyzed. The interpolation virtual array covariance ma trix is reconstructed in an off-grid manner to over come the basis mismatch problem. This is combined with the Multiple Signal Classification (MUSIC) al gorithm for precise DOA estimation. Simulation re sults demonstrate that the proposed method achieves higher estimation accuracy and greater degrees of free dom compared to traditional methods. USing M+N 1 physical elements can provide O(MN) degrees of freedom and achieve 1° resolution.

Efficient energy utilization in covert communication sustains covertness while assuring communication quality and efficiency. This paper investigates covert communication energy efficiency (EE) in direct uplink satellite-ground communications, focusing on enhancing system EE via optimized transmit beamforming and satellite orbit altitude selection. This paper first establishes an optimization problem to maximize system EE in a direct uplink satellite-ground covert communication scenario. To solve this non-convex optimization problem, it is decomposed into two subproblems and solved using the successive convex approximation (SCA) method. Based on the above methods, this paper proposes an overall iterative optimization algorithm. Simulation results demonstrate that the proposed algorithm surpasses the baseline in terms of system EE. Furthermore, they elucidate the correlation between the amount of information received by the receiver and the variations in the satellite's orbital altitude.

In IPv6 (Internet Protocol Version 6) networks, the Duplicate Address Detection (DAD) protocol plays a critical role in verifying that an address is unique before it is assigned to an interface. This protocol operates using ICMPv6 (Internet Control Message Protocol Version 6) messages specifically, Neighbor Solicitation (NS) and Neighbor Advertisement (NA) messages for sending requests and receiving responses, respectively. However, DAD’s lack of authentication for ICMPv6 messages makes it susceptible to Denial-of-Service (DoS) attacks. Many existing solutions attempt to address these vulnerabilities, but they often fall short due to factors like high computational complexity, increased system demands, or reliance on centralized structures, all of which can hinder practical use. This paper introduces an alternative method that employs extended headers to enable the creation of multiple addresses for multiple Solicited Node Multicast Addresses (SNMA). By enhancing the efficiency of address configuration, this approach outperforms the traditional single address DAD. The evaluation of the proposed solution against the standard DAD protocol considers several performance metrics, including processing time, bandwidth usage, CPU and RAM utilization, and network overhead. Although this method may use slightly more resources, the resulting improvements in security, efficiency, and accuracy in unique IP address assignment make it a viable and effective solution.

Modulation signals are likely to experience channel fading when transmitted over wireless channels, and channel blind equalization is a powerful method to combat fading and guarantee high transmission quality and efficiency because no pilot information is required. In this paper, we propose a dualbranch blind equalization autoencoder with hybrid attention mechanisms, referred to as DBeA-HA, to restore various modulation signal waveforms. In the main branch, we employ multi-layer depthwise separable convolutions (DSC) with rich residual connections and squeeze-and-excitation (SE) mechanism in our encoder to extract channel features of faded signals at different resolutions, while reducing complexity. The auxiliary branch is constructed by incorporating a lightweight residual temporal dilated convolution to capture temporal correlations of faded signals. Additionally, the convolutional block attention module (CBAM) and multi-head self-attention (MHSA) mechanisms are applied within and between branches, respectively, to further enhance feature extraction and fusion capabilities. By integrating two branches effectively, the proposed method achieves high equalization performance and keeps low complexity. Experimental results reveal that in signal-to-noise ratio (SNR) ranging from -12 dB to 8 dB with six-path fading, our DBeA-HA effectively compensates for the effects of fading channels and surpasses existing equalization methods. For demodulation, compared with“ResNet+De” and the least mean square (LMS) methods, our DBeA-HA reduces the BER of QPSK by an average of 32.66% and 72.98%, and reduces the BER of 16QAM by an average of 19.41% and 55.33%, respectively, with a moderate level of complexity.

Previous methods of mitigating Non-Line_x005fof-Sight (NLOS) errors usually pre-assumed that the NLOS errors satisfy the interval set. However, these methods exhibit notable shortcoming: The interval set is too conservative to exploit fully the NLOS error properties. To address this shortcoming, we consider using the data-driven method to construct error sets. Precisely, We use the k-means method to cluster the ranging errors of the training points in the target region, thus constructing the data-driven error sets.Based on the data-driven error sets, we formulate the source localization problem as a positive semi-definite matrix form with uncertainty. Consider the number of training points that can determine the data-driven error set bounds. To ensure high-accuracy localization even with a few training points. Subsequently,we use robust optimization concepts and some mathematical methods to eliminate the uncertainty. Finally, we obtain a solvable robust optimization problem. The experiments demonstrate that the proposed method achieves better localization performance even with fewer training points.

Congested link detection (CLD) has attracted more and more attention due to the rapid development of data traffic and services. In this work, we proposed a novel compressive sensing (CS) aided deep learning CLD scheme. Firstly, considering the network tomography structure, we proposed a CS-based preliminary CLD process to estimate the congestion probabilities for each link by utilizing the sparsity of congestions, it enables a decrease in the number of monitors needed, which in turn, enhances the flexibility and practicality of our scheme in various real-world scenarios. Then, based on the CS aided preliminary estimation, a long-short term memory network (LSTMN) is exploited to extract the time relationship of the congested states for each link, which can improve the accuracy of CLD. Moreover, since LSTMN utilizes the CS-aided preliminary estimation results to extract time relationships, our proposed scheme can reduce the monitoring cost and improve CLD accuracy. Ultimately, the simulation results substantiate the efficacy of our proposed scheme.

Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access to devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. In this paper, we design two-level incentive mechanisms based on game theory for HFL in a device-edge-cloud coordinating architecture, aiming at encouraging the participation of entities in each level. To be specific, in the design of the lower-level incentive mechanism, we propose a coalition formation game to optimize the device-edge association and bandwidth allocation. We first develop efficient coalition partitions based on preference rules for optimizing device-edge association, which can be proven to be stable by constructing a potential function. Then, we develop a gradient projection method to optimally allocate bandwidth among the coalitions. In the upper-level game, we design a Stackelberg game algorithm to jointly maximize the utilities of the cloud and each edge server. The Stackelberg game algorithm is able to determine the optimal number of aggregations at each edge server, as well as the reward provided by the cloud server to each edge server for the performance improvement due to the edge aggregations. Numerical results indicate that the proposed method outperforms recent benchmark schemes, including the Accelerated device coalition formation (ADCF) algorithm and the Max-Q mechanism. Furthermore, our proposed method demonstrates significant accuracy improvements on real datasets, such as CIFAR-10, compared to the three benchmark schemes.

This paper centres on achieving the maximization of weighted throughput (WTP) in a multiuser cell-free massive multiple-input multiple-output (mMIMO) system with both finite blocklength (FBL) and infinite blocklength (INFBL), which is conducted against the backdrop of constrained time-frequency resources. We aim to ensure a quality of service (QoS) for all users, particularly in the FBL scenario, maintaining an acceptable latency and block error rate (BLER). To counteract the impact of reduced DoF of channel matrix due to a large number of users access, which leads to decreased system performance, we strive to optimize WTP by scheduling multiple users to different resource elements (RE) and applying precoding operation accordingly, subject to the limitations imposed by total power consumption per time slot and requisite QoS parameters. Simulation results demonstrate the superiority of the proposed multiuser processing (MUP) scheme over both single-user processing (SUP) and alluser processing (AUP) alternatives, and the proposed iterative algorithm based on genetic algorithm (GA) achieves up to 49.36% system performance gains compared to the benchmark algorithms. This substantiates the efficacy of our method in enhancing network performance and user satisfaction.

Non-orthogonal multiple access (NOMA) techniques assisted mobile edge computing (MEC) has attracted attention due to its advantages in reducing the energy consumption and the latency of MEC offloading. One of the critical challenges is how to efficiently allocate limited resources and reasonably group users to enhance system performance. In this work, we propose a hybrid NOMA assisted MEC offloading scheme based on user grouping. Our objective is to minimize the overall system energy consumption by jointly optimizing user grouping, transmit power, transmit time and the offloading ratio. The optimization problem is solved in two stages: user grouping and resource optimization. In the first stage, users are initially grouped based on channel gains, and then regrouped according to their latency requirements to reduce inter-user interference, thereby achieving more efficient resource allocation. In the second stage, the problem is simplified to a single-group energy minimization problem. By applying KKT conditions, a suboptimal solution for each user’s transmission power, transmission time, and offloading ratio is derived. Simulation results indicate that, under the same conditions, the proposed scheme achieves lower system energy consumption compared to other benchmark schemes.

The complexity of underwater environments renders underwater acoustic signals vulnerable to various forms of noise during transmission, creating significant challenges for signal demodulation tasks. This paper presents a novel demodulation method for multi-class single-carrier underwater acoustic signals. Our approach employs two innovative structures for modeling in the time and frequency domains, integrating these features for comprehensive discrimination. Specifically, we introduce a High-Efficient Convolution (HEC) Block to extract time-domain waveform features and a Local-Global Attention (LGA) structure for time-frequency features, utilizing cross-attention to fuse these features. This method enables the network to learn hidden frequency, phase, and amplitude characteristics within high-dimensional features, effectively capturing both fine-grained local and long-distance global features. A classifier is then constructed to categorize multi-class modulation signals, completing the demodulation process. Simulation results highlight the method's exceptional performance: in a Gaussian channel with a Signal-to-Noise Ratio (SNR) of 0 dB, the demodulation error rates for 2PSK, 2FSK, 2ASK, 4PSK, 4FSK, and 8PSK signals are all below 0.01, while the error rate for 16QAM modulated signals is less than 0.1. Additionally, validation using BELLHOP simulation data and real-world data collected from the Yellow Sea further demonstrates the proposed method's remarkable noise resistance and demodulation capabilities.

High penetration, high-resolution ranging, high efficiency, low cost, low latency, high security, and anti-multipath nature of ultra-wideband technology make it a good candidate for precise ranging and communication. In the existing iterative reference enhancement (IRE) techniques, the erroneous detection of a symbol in the previous iteration significantly deteriorates its detection. In this paper, the effect of the symbol on its own detection in the next iteration is called the self-effect. We show that if the symbol is incorrectly detected, the self-effect appears as a detrimental bias term in its decision statistic causing a significant performance loss. In this paper, we propose a novel IRE technique to exclude the self-effect and achieve a considerable performance gain in both detection and time-of-arrival (ToA) estimation. The proposed IRE technique outperforms the existing IRE technique over IEEE 802.15.3a channel model 4 by at least $2.5$ dB in detection and $6$ dB in ToA estimation with less complexity, higher speed, and less processing burden. Moreover, it achieves the optimal localization performance for $E_b/N_0>20$ dB. Furthermore, we develop an accurate analysis framework to calculate the bit-error probability of the proposed IRE method in the presence of monobit quantization, noise, and multipath.

In the sixth generation mobile communication (6G) system, Non-Terrestrial Networks (NTN), as a supplement to terrestrial network, can meet the requirements of wide area intelligent connection and global ubiquitous seamless access, establish intelligent connection for wide area objects, and provide intelligent services. Due to issues such as massive access, doppler shift, and limited spectrum resources in NTN, research on resource management is crucial for optimizing NTN performance. In this paper, a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided. Firstly, the key technologies involved in NTN resource management is summarized. Secondly, NTN resource management is discussed from network pattern and resource pattern. The network pattern focuses on the application of different optimization methods to different network dimension communication resource management, and the resource type pattern focuses on the research and application of multi-domain resource management such as computation, cache, communication and sensing. Finally, future research directions and challenges of 6G NTN resource management are discussed.

Time synchronization is the basis for ensuring coordinated linkage among various subsystems of the train communication networks (TCN), and it is directly related to the safety of train operation. To enhance the reliability of time synchronization, multiple master clocks are typically used for redundancy. However, during the switching of grandmaster (GM) clock, significant time difference will be caused, making it difficult to ensure reliable synchronization. This paper proposes a reliable synchronization solution for the next-generation TCN (NG-TCN). Specifically, the solution adopts a networking architecture based on the SDN paradigm, integrating a synchronization controller for synchronization configuration and compensation management. Furthermore, a reliable synchronization method based on intelligent prediction is proposed for the compensation of time difference during GM switching. This paper builds a network testbed based on the GMs and TSN switches, and evaluates the reliable synchronization performance according to the 1-PPS outputs from the clocks. Experimental results show that the proposed solution can maintain the performance of synchronization accuracy within 1 us during the GM switching.

Currently, with the development of satellite communication, there are many challenges faced by satellite communication. Scholars have attempted to use the modern mathematical theory of differential geometry to solve problems in the fields of information and communication, and this emerging theoretical system is called information geometry, which is considered a revolutionary theory of information. Its application in the field of satellite communication has become a research hotspot and future development direction.This paper provides a detailed overview of the application of information geometry in satellite communication systems. Firstly, the feasibility of geometric analysis of satellite communication signals is explored . Subsequently, the basic concepts of information geometry are introduced. Then, the paper reviews the applications of information geometry in channel coding, on-board filtering, satellite spectrum sensing, interference signal detection techniques, and incoming wave direction estimation. In addition, this paper presents the information geometry representation of polar code BP decoding, the on-board nonlinear filtering process based on information geometry, and the satellite interference signal detection process based on information geometry. Finally, a summary and outlook are provided.

Multiple UAVs cooperative target search has been widely used in various environments, such as emergency rescue and traffic monitoring. However, uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states. This limitation hinders timely information sharing and insightful path decisions for UAVs, resulting in inefficient or even failed collaborative search. Aiming at this issue, this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments. Specifically, an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment. Based on the fused information, we formalize the trajectory planning as a multi-objective optimization problem by jointly considering search performance and UAV energy harnessing. A multi-agent deep reinforcement learning based algorithm is proposed to solve it, where the reward-guided real-time path is determined to achieve an energy efficient search. Finally, extensive experimental results show that the proposed algorithm outperforms.

In this paper,an improved ALOHA-based unsourced random access (URA) scheme is proposed in MIMO channels. The channel coherent interval is divided into multiple sub-slots and each active user selects several sub-slots to send its codeword, namely, the channel access pattern. To be more specific, the data stream of each active user is divided into three parts. The first part is mapped as the compressed sensing (CS) pilot, which also serves for the consequent channel estimation. The second part is modulated by binary phase shift keying (BPSK). The obtained CS pilot and the antipodal BPSK signal are concatenated as its codeword. After that, the codeword of each active user is sent repeatedly based on its channel access pattern, which is determined by the third part of the information bits, namely, index modulation (IM). On the receiver side, a hard decision-based decoder is proposed which includes the CS decoder, maximal likelihood (ML based superposed codeword decomposer (SCD), and IM demodulator. To further reduce the complexity of the proposed decoder, a simplified SCD based on convex approximation is considered. The performance analysis is also provided. The exhaustive computer simulations confirm the superiority of our proposal.

For large-scale multiple-input multiple-output (MIMO) systems, iterative detections based on belief propagation (BP) have shown near-optimal performance. However, the existing deterministic implementations of BP detection are considerably complex, particularly for MIMO systems with a large scale. This paper proposes an alternative approach using stochastic computation. This paper introduces a hardware architecture for stochastic message updating of observation nodes and symbol nodes. The signed stochastic real division (SRD) and the stochastic real addition (SRA) are proposed for latency consideration, as well as multi-bit designs are proposed to improve the performance and reduce the stream length.The Look-Up Table (LUT) has been modified for serial input to enhance hardware efficiency. Simulation results demonstrate that for large-scale MIMO systems with either QPSK or 16-QAM, the stochastic BP detector achieves similar performance as the deterministic one. To highlight its implementation advantage, an 8×32 stochastic MIMO detector with QPSK is implemented. Results show that its hardware efficiency is about 10 times greater than existing works and has lower complexity compared with deterministic designs.

The rapid development of the Internet of Vehicles (IoVs) underscores the importance of Vehicle-to-Everything (V2X) communication for ensuring driving safety. V2X supports control systems by providing reliable and real-time information, while the control system's decisions, in turn, affect the communication topology and channel state. Depending on the coupling between communication and control, radio resource allocation (RRA) should be control-aware. However, current RRA methods often focus on optimizing communication metrics, neglecting the needs of the control system. To promote the co-design of communication and control, this paper proposes a novel RRA method that integrates both communication and control considerations. From the communication perspective, the Age of Information (AoI) is introduced to measure the freshness of packets. From the control perspective, a weighted utility function based on Time-to-Collision (TTC) and driving distance is designed, emphasizing the neighboring importance and potentially dangerous vehicles. By synthesizing these two metrics, an optimization objective minimizing weighted AoI based on TTC and driving distance is formulated. The RRA process is modeled as a partially observable Markov decision process, and a multi-agent reinforcement learning algorithm incorporating positional encoding and attention mechanisms (PAMARL) is proposed. Simulation results show that PAMARL can reduce Collision Risk (CR) with better Packet Delivery Ratio (PDR) than others.

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.