In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency. Due to different amounts of local data, computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate. The above factors will affect the local calculation time and upload time of the local model, and the vehicle may also be affected by Byzantine attacks, leading to the deterioration of the vehicle data. However, based on deep reinforcement learning (DRL), we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL. At the same time, when aggregating AFL, we can focus on those vehicles with better performance to improve the accuracy and safety of the system. In this paper, we proposed a vehicle selection scheme based on DRL in VEC. In this scheme, vehicle's mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment, we consider few-shot learning-based automatic modulation classification (AMC) to improve its reliability. A data enhancement module (DEM) is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC. Multimodal network is designed to have multiple residual blocks, where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction. Moreover, a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM. Since different model may output different results, cooperative classifier is designed to avoid the randomness of single model and improve the reliability. Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

With the rapid development and application of energy harvesting technology, it has become a prominent research area due to its significant benefits in terms of green environmental protection, convenience, and high safety and efficiency. However, the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks, thereby resulting in low energy transmission efficiency. To enhance the energy transmission efficiency of IoT devices in energy harvesting, this paper focuses on the utilization of collaborative communication, along with pricing-based incentive mechanisms and auction strategies. We propose a dynamic relay selection scheme, including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism, to encourage more IoT devices to participate in the collaboration process. Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

In this paper, an intelligent reflecting surface (IRS)-and-unmanned aerial vehicle (UAV)-assisted two-way amplify-and-forward (AF) relay network in maritime Internet of Things (IoT) is proposed, where ship1 ($\text{S}_1$) and ship2 ($\text{S}_2$) can be viewed as data collecting centers. To enhance the message exchange rate between $\text{S}_1$ and $\text{S}_2$, a problem of maximizing minimum rate is cast, where the variables, namely AF relay beamforming matrix and IRS phase shifts of two time slots, need to be optimized. To achieve a maximum rate, a low-complexity alternately iterative (AI) scheme based on zero forcing and successive convex approximation (LC-ZF-SCA) algorithm is presented. To obtain a significant rate enhancement, a high-performance AI method based on one step, semidefinite programming and penalty SCA (ONS-SDP-PSCA) is proposed. Simulation results show that by the proposed LC-ZF-SCA and ONS-SDP-PSCA methods, the rate of the IRS-and-UAV-assisted AF relay network surpass those of with random phase and only AF relay networks. Moreover, ONS-SDP-PSCA perform better than LC-ZF-SCA in aspect of rate.

To protect vehicular privacy and speed up the execution of tasks, federated learning is introduced in the Internet of Vehicles (IoV) where users execute model training locally and upload local models to the base station without massive raw data exchange. However, heterogeneous computing and communication resources of vehicles cause straggler effect which weakens the reliability of federated learning. Dropping out vehicles with limited resources confines the training data. As a result, the accuracy and applicability of federated learning models will be reduced. To mitigate the straggler effect and improve performance of federated learning, we propose a reconfigurable intelligent surface (RIS)-assisted federated learning framework to enhance the communication reliability for parameter transmission in the IoV. Furthermore, we optimize the phase shift of RIS to achieve a more reliable communication environment. In addition, we define vehicular competence to measure both vehicular trustworthiness and resources. Based on the vehicular competence, the straggler effect is mitigated where training tasks of computing stragglers are offloaded to surrounding vehicles with high competence. The experiment results verify that our proposed framework can improve the reliability of federated learning in terms of computing and communication in the IoV.

Vehicular edge computing (VEC) is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle (IoV). Non-orthogonal multiple access (NOMA) has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost. It is an encouraging progress combining VEC and NOMA. In this paper, we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system. To solve the optimization problem, we propose a multi-agent deep graph reinforcement learning algorithm. The algorithm extracts the topological features and relationship information between agents from the system state as observations, outputs task offloading decision and resource allocation simultaneously with local policy network, which is updated by a local learner. Simulation results demonstrate that the proposed method achieves a 1.52%$\sim$5.80% improvement compared with the benchmark algorithms in system service utility.

In the areas without terrestrial communication infrastructures, unmanned aerial vehicles (UAVs) can be utilized to serve field robots for mission-critical tasks. For this purpose, UAVs can be equipped with sensing, communication, and computing modules to support various requirements of robots. In the task process, different modules assist the robots to perform tasks in a closed-loop way, which is referred to as a sensing-communication-computing-control ($\textbf{SC}^3$) loop. In this work, we investigate a UAV-aided system containing multiple $\textbf{SC}^3$ loops, which leverages non-orthogonal multiple access (NOMA) for efficient resource sharing. We describe and compare three different modelling levels for the $\textbf{SC}^3$ loop. Based on the entropy $\textbf{SC}^3$ loop model, a sum linear quadratic regulator (LQR) control cost minimization problem is formulated by optimizing the communication power. Further for the assure-to-be-stable case, we show that the original problem can be approximated by a modified user fairness problem, and accordingly gain more insights into the optimal solutions. Simulation results demonstrate the performance gain of using NOMA in such task-oriented systems, as well as the superiority of our proposed closed-loop-oriented design.

In a post-disaster environment characterized by frequent interruptions in communication links, traditional wireless communication networks are ineffective. Although the "store-carry-forward" mechanism characteristic of Delay Tolerant Networks (DTNs) can transmit data from Internet of things devices to more reliable base stations or data centres, it also suffers from inefficient data transmission and excessive transmission delays. To address these challenges, we propose an intelligent routing strategy based on node sociability for post-disaster emergency network scenarios. First, we introduce an intelligent routing strategy based on node intimacy, which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility values. Second, we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility values. Finally, experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate, network cost ratio and average transmission delay.

Large number of antennas and higher bandwidth usage in massive multiple-input-multiple-output (MIMO) systems create immense burden on receiver in terms of higher power consumption. The power consumption at the receiver radio frequency (RF) circuits can be significantly reduced by the application of analog-to-digital converter (ADC) of low resolution. In this paper we investigate bandwidth efficiency (BE) of massive MIMO with perfect channel state information (CSI) by applying low resolution ADCs with Rician fadings. We start our analysis by deriving the additive quantization noise model, which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar. We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency (BE) of the system. We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing (RZF) combining algorithm. We also provide a generic analysis of energy efficiency (EE) with different options of bits by calculating the energy efficiencies (EE) using the achievable rates. We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

This paper studies large-scale multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) communications in a broadband frequency-selective channel, where a massive MIMO base station (BS) communicates with multiple users equipped with multi-antenna. We develop a hybrid precoding design to maximize the weighted sum-rate (WSR) of the users by optimizing the digital and the analog precoders alternately. For the digital part, we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR. For the analog part, the optimization of the PSN is formulated as an unconstrained problem, which can be efficiently solved by a gradient descent method. Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

The increasing demand for industrial automation and intelligence has put forward higher requirements for the reliability of industrial wireless communication technology. As an international standard based on 802.11, Wireless networks for Industrial Automation-Factory Automation (WIA-FA) greatly improves the reliability in factory automation scenarios by Time Division Multiple Access (TDMA). However, in ultra-dense WIA-FA networks with mobile users, the basic connection management mechanism is inefficient. Most of the handover and resource management algorithms are all based on frequency division multiplexing, not suitable for the TDMA in the WIA-FA network. Therefore, we propose Load-aware Connection Management (LACM) algorithm to adjust the linkage and balance the load of access devices to avoid blocking and improve the reliability of the system. And then we simulate the algorithm to find the optimal settings of the parameters. After comparing with other existing algorithms, the result of the simulation proves that LACM is more efficient in reliability and maintains high reliability of more than 99.8% even in the ultra-dense moving scenario with 1500 field devices. Besides, this algorithm ensures that only a few signaling exchanges are required to ensure load balancing, which is no more than 5 times, and less than half of the best state-of-the-art algorithm.

In this paper, a contention-based connection-free transmission scheme is proposed to meet the stringent requirements of ultra-reliability and low-latency for critical machine-type communication (cMTC). To improve reliability, we design multiple independent sparse orthogonal pilots (MISOP) to significantly reduce the probability of pilot collision to the order of $10^{-5}$. Besides, the advancements of massive MIMO (mMIMO) are exploited to further enhance the reliability. To achieve low latency, connection-free slot-based one-shot transmission without retransmissions is adopted. On the receiver side, single round of multi-user detection (MUD) without interference cancellation (IC) can reduce the processing delay. The imprecise synchronization between cMTC device and the gNB in connection-free transmission, e.g., time and frequency offsets, are also considered. The simulation results shows that the proposed scheme can well satisfy the ambitious requirements of cMTC, and has the potential applications in supporting massive cMTC devices in 6G.

Inter-datacenter elastic optical networks (EON) need to provide the service for the requests of cloud computing that require not only connectivity and computing resources but also network survivability. In this paper, to realize joint allocation of computing and connectivity resources in survivable inter-datacenter EONs, a survivable routing, modulation level, spectrum, and computing resource allocation algorithm (SRMLSCRA) algorithm and three datacenter selection strategies, i.e. Computing Resource First (CRF), Shortest Path First (SPF) and Random Destination (RD), are proposed for different scenarios. Unicast and manycast are applied to the communication of computing requests, and the routing strategies are calculated respectively. Simulation results show that SRMLCRA-CRF can serve the largest amount of protected computing tasks, and the requested calculation blocking probability is reduced by 29.2%, 28.3% and 30.5% compared with SRMLSCRA-SPF, SRMLSCRA-RD and the benchmark EPS-RMSA algorithms respectively. Therefore, it is more applicable to the networks with huge calculations. Besides, SRMLSCRA-SPF consumes the least spectrum, thereby exhibiting its suitability for scenarios where the amount of calculation is small and communication resources are scarce. The results demonstrate that the proposed methods realize the joint allocation of computing and connectivity resources, and could provide efficient protection for services under single-link failure and occupy less spectrum.

Mobile edge computing (MEC) is a promising paradigm by deploying edge servers (nodes) with computation and storage capacity close to IoT devices. Content Providers can cache data in edge servers and provide services for IoT devices, which effectively reduces the delay for acquiring data. With the increasing number of IoT devices requesting for services, the spectrum resources are generally limited. In order to effectively meet the challenge of limited spectrum resources, the Non-Orthogonal Multiple Access (NOMA) is proposed to improve the transmission efficiency. In this paper, we consider the caching scenario in a NOMA-enabled MEC system. All the devices compete for the limited resources and tend to minimize their own cost. We formulate the caching problem, and the goal is to minimize the delay cost for each individual device subject to resource constraints. We reformulate the optimization as a non-cooperative game model. We prove the existence of Nash equilibrium (NE) solution in the game model. Then, we design the Game-based Cost-Efficient Edge Caching Algorithm (GCECA) to solve the problem. The effectiveness of our GCECA algorithm is validated by both parameter analysis and comparison experiments.

In recent years, network attacks have been characterized by diversification and scale, which indicates a requirement for defense strategies to sacrifice generalizability for higher security. As the latest theoretical achievement in active defense, mimic defense demonstrates high robustness against complex attacks. This study proposes a Function-aware, Bayesian adjudication, and Adaptive updating Mimic Defense (FBAMD) theory for addressing the current problems of existing work including limited ability to resist unknown threats, imprecise heterogeneous metrics, and over-reliance on relatively-correct axiom. FBAMD incorporates three critical steps. Firstly, the common features of executors' vulnerabilities are obtained from the perspective of the functional implementation (i.e, input-output relationships extraction). Secondly, a new adjudication mechanism considering Bayes' theory is proposed by leveraging the advantages of both current results and historical confidence. Furthermore, posterior confidence can be updated regularly with prior adjudication information, which provides mimic system adaptability. The experimental analysis shows that FBAMD exhibits the best performance in the face of different types of attacks compared to the state-of-the-art over real-world datasets. This study presents a promising step toward the theoretical innovation of mimic defense.

With the rapid development of the Internet, network security and data privacy are increasingly valued. Although classical Network Intrusion Detection System (NIDS) based on Deep Learning (DL) models can provide good detection accuracy, but collecting samples for centralized training brings the huge risk of data privacy leakage. Furthermore, the training of supervised deep learning models requires a large number of labeled samples, which is usually cumbersome. The "black-box" problem also makes the DL models of NIDS untrustworthy. In this paper, we propose a trusted Federated Learning (FL) Traffic IDS method called FL-TIDS to address the above-mentioned problems. In FL-TIDS, we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked samples. At the same time, we use FL for model training to protect data privacy. In addition, we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained model. We conducted several experiments to evaluate the proposed FL-TIDS. We first determine experimentally the structure and the number of neurons of the unsupervised AE model. Secondly, we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 datasets. The experimental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision, recall and f1-score. Then, federated learning is used to train the intrusion detection model. The experimental results indicate that the model is more accurate than the local learning model. Finally, we use an improved SHAP explainability method based on Chi-square test to analyze the explainability. The analysis results show that the identification characteristics of the model are consistent with the attack characteristics, and the model is reliable.

There are numerous terminals in the satellite Internet of Things (IoT). To save cost and reduce power consumption, the system needs terminals to catch the characteristics of low power consumption and light control. The regular random access (RA) protocols may generate large amounts of collisions, which degrade the system throughout severally. The near-far effect and power control technologies are not applicable in capture effect to obtain power difference, resulting in the collisions that cannot be separated. In fact, the optimal design at the receiving end can also realize the condition of packet power domain separation, but there are few relevant researches. In this paper, an auxiliary beamforming scheme is proposed for power domain signal separation. It adds an auxiliary reception beam based on the conventional beam, utilizing the correlation of packets in time-frequency domain between the main and auxiliary beam to complete signal separation. The roll-off belt of auxiliary beam is used to create the carrier-to-noise ratio (CNR) difference. This paper uses the genetic algorithm to optimize the auxiliary beam direction. Simulation results show that the proposed scheme outperforms slotted ALOHA (SA) in terms of system throughput performance and without bringing terminals additional control burden.

To improve the bit error rate (BER) performance of multi-user signal detection in satellite-terrestrial downlink non-orthogonal multiple access (NOMA) systems, an iterative signal detection algorithm based on soft interference cancellation with optimal power allocation is proposed. Given that power allocation has a significant impact on BER performance, the optimal power allocation is obtained by minimizing the average BER of NOMA users. According to the allocated powers, successive interference cancellation (SIC) between NOMA users is performed in descending power order. For each user, an iterative soft interference cancellation is performed, and soft symbol probabilities are calculated for soft decision. To improve detection accuracy and without increasing the complexity, the aforementioned algorithm is optimized by adding minimum mean square error (MMSE) signal estimation before detection, and in each iteration soft symbol probabilities are utilized for soft-decision of the current user and also for the update of soft interference of the previous user. Simulation results illustrate that the optimized algorithm i.e. MMSE-IDBSIC significantly outperforms joint multi-user detection and SIC detection by 7.57dB and 8.03dB in terms of BER performance.

Low-Earth-Orbit satellite constellation networks (LEO-SCN) can provide low-cost, large-scale, flexible coverage wireless communication services. High dynamics and large topological sizes characterize LEO-SCN. Protocol development and application testing of LEO-SCN are challenging to carry out in a natural environment. Simulation platforms are a more effective means of technology demonstration. Currently available simulators have a single function and limited simulation scale. There needs to be a simulator for full-featured simulation. In this paper, we apply the parallel discrete-event simulation technique to the simulation of LEO-SCN to support large-scale complex system simulation at the packet level. To solve the problem that single-process programs cannot cope with complex simulations containing numerous entities, we propose a parallel mechanism and algorithms LP-NM and LP-YAWNS for synchronization. In the experiment, we use ns-3 to verify the acceleration ratio and efficiency of the above algorithms. The results show that our proposed mechanism can provide parallel simulation engine support for the LEO-SCN.