The integrated satellite-terrestrial network (ISTN) with inter-satellite free space optical (FSO) links and satellite-to-ground (S2G) radio frequency (RF) links is becoming an important enabler for the Internet of Things (IoT). However, investigating the performance of the ISTN remains several challenges, i.e., the high mobility and long propagation delays of S2G links, and the highly correlated line-of-sight S2G channels. To address these challenges, we propose a hybrid RF/FSO cooperative satellite-terrestrial communication system that integrates the space time block code with cooperative transmission to enhance the coverage probability and communication reliability of satellite downlink transmission. We model the inter-satellite FSO channels by considering pointing and tracking errors, and the S2G RF channels using the shadowed-Rician fading model. Subsequently, we derive the probability density function and cumulative distribution function for both RF/FSO signal-to-noise ratio (SNR) under channel estimation errors and the sum of two RF SNRs from the same distribution family. Finally, for the proposed system, closed-form expressions of the outage probability (OP) and the upper bound for the average bit error probability (BEP) are derived. The proposed system outperforms SISO and MISO systems by reducing average BEP, outage probability, and robustness to channel estimation errors.

Low Earth Orbit (LEO) satellites contribute greatly to the remote Internet-of-Things (IoT) system in terms of wide-area coverage, massive access capacity and low processing latency. However, the inter-satellite handover (ISH) occurs frequently and consumes a large amount of signaling resources due to both the high-speed mobility of the LEO satellite and the massive number of IoT devices. In order to improve the resource utility and guarantee the service continuity, we propose a group handover strategy based on a multi-layer graph (MLG) model, which enables handover scheduling for multiple device groups across multiple time slots. Specifically, we first design a clustering feature (CF) tree based device clustering algorithm to reduce the handover signaling overhead. The proposed algorithm removes the requirement to preset the number of groups and thus avoids the influence of inappropriate grouping. Afterwards, we propose an inter-satellite group handover scheduling algorithm, where the MLG model is utilized to jointly schedule the resources of multiple satellites to serve multiple device groups over multiple slots. The MLG model transforms the large-scale ISH problem into a dynamic network-flows problem. Simulation results demonstrate that the proposed strategy achieves superior performance in terms of the handover rate, the signaling overhead, the handover success probability, and the average handover gain, compared with several benchmarks.

In this work, we propose a multi-aircraft cooperative handover scheme for satellite-to-aircraft communication systems. Specifically, considering the dual characteristics of aircraft resource demands and three satellite states (normal, congested, and failed), multiple aircraft collaborate to make handover decisions while maintaining network stability and avoiding congestion. We formulate the cooperative handover problem as a multi-objective optimization problem to minimize communication latency and network congestion while maximizing connection stability. To solve this problem, we first model the handover scheme into the Markov decision process to facilitate seamless satellite-aircraft handover. Then we develop a multi-agent deep deterministic policy gradient (MADDPG) algorithm with centralized training and decentralized execution architecture. Due to the time-varying nature of the action space and the constraint that action selection is limited to currently visible and undamaged satellites, we implement an action mask approach to effectively filter out illegal actions instead of using conventional negative reward methods. The simulation results demonstrate that the proposed framework effectively reduces handover frequency, minimizes communication latency, and achieves better network load balancing, validating its feasibility and effectiveness in satellite-toaircraft communication systems.

Low earth orbit (LEO) satellite communication systems have become the indispensable part of sixth generation (6 G) communications. However, since the LEO and geostationary earth orbit (GEO) satellite systems will inevitably share limited frequency resources, the communication signal from LEO satellites have the possibility to cause harmful interference to the GEO systems. To address this issue, this paper proposes an adaptive beamforming strategy to mitigate the interference while improving the system spectral efficiency (SE). In specific, we formulate the problem as the nonlinear mixed integer programming (NMIP) optimization problem, and apply the weighted minimum mean square error (WMMSE) and alternative optimization algorithm to obtain the closed-form solutions. Furthermore, to reduce the high complexity of beamforming when the LEO system serves a massive number of ground users (GU), we propose a channelaware user clustering scheme utilizing the channel correlation between GUs so that all GUs within the same cluster share the same precoding vector. Extensive simulations show that the proposed scheme effectively mitigates the interference.

Satellite Edge Computing (SEC) augments the computational capability of Low Earth Orbit (LEO) satellite networks to support latency-sensitive and computation-intensive services. However, limited onboard resources, dynamic network topology, and the lack of trust among heterogeneous nodes hinder efficient task offloading and collaborative processing. To address these challenges, we propose a blockchain-enabled SEC framework that integrates task offloading, resource allocation, and an incentive mechanism via smart contracts, ensuring trusted and autonomous cooperation. We further develop a distributed optimization algorithm based on Successive Convex Approximation and Distributed Alternating Direction Method of Multipliers (SCA-DADMM), enabling decentralized decision-making with only neighbor-level communication. Simulation results show that the proposed approach achieves up to 32.96% higher system revenue and 15.98% lower task latency compared to baseline methods under varying bandwidth and computing resource conditions, demonstrating its potential to enhance both efficiency and trust in resource-constrained satellite edge environments.

The space-air–ground integrated network (SAGIN) integrates satellites, unmanned aerial vehicles (UAVs), and terrestrial remote clouds to provide seamless network access and high-volume computing services for remote Internet of Things (IoT) devices, thus alleviating geographic and resource constraints. Existing methods typically focus on the network dynamics while overlooking the comprehensive consideration of device dynamics, namely, the time-varying task performance weights, task sizes, and task processing demands. Moreover, the centralized learning-based offloading schemes often lead to substantial signaling overhead. To bridge these gaps, this article proposes a distributed dynamic task offloading mechanism with game-theoretic multiagent stochastic learning (MASL). Technically, a stochastic game is formulated with each device as a player minimizing its weighted sum cost of latency and energy. We prove the existence of Nash equilibrium (NE) for our proposed game and propose a multiagent entropy-enhanced stochastic learning (MESL) algorithm in a fully distributed manner with no information exchange among IoT devices. By introducing the entropy of decision probability for each device, MESL increases decision dimensions, accelerates convergence, and facilitates optimal strategy achievement. Experimental results show that the MESL algorithm significantly reduces the overall cost and greatly enhances the convergence speed in dynamic SAGIN environments compared to existing algorithms.

To fulfill the requirements of human lunar exploration programs and establish bases on the moon in the distant future, lunar sensors (LSs) will inevitably produce a significant amount of data. It is necessary to construct the Earth-Moon metaverse in order to obtain and utilize lunar information more effectively. Due to the long communication distance between Earth and Moon as well as the lack of communication resources, a hybrid Earth-Moon metaverse network with channel model and transmission model is designed. To ensure the efficiency and stability of transmission in the network, LSs transmit lunar data to Earth clients (ECs) through the active and passive intelligent reflecting surface (IRS) deployed at relay satellites. Then, we propose a Stackelberg game model to describe the adversarial relationship between the satellites, LSs and ECs, and optimal strategies are obtained by solving the Nash equilibrium to maximize their utility. Simulations demonstrate that the network can effectively shorten the transmission delay and improve the utility of ECs and satellites.

In this paper, we investigate the coverage path planning (CPP) scheme for autonomous underwater vehicles (AUVs) cooperative environment detection in integrated underwater acoustic communication and detection networks (UCDNs), where multiple AUVs detect unexplored oceanic environments and avoid obstacles. Firstly, we present the detection range prediction model related to oceanic environmental parameters and propose the detection and communication scheme in UCDNs. Secondly, to conduct the cooperative environment detection mission, we formulate the CPP problem as a mixed combinatorial and sequential quadratic optimization problem to maximize the coverage ratio and minimize the path length of AUVs. To solve this problem, we investigate the multi-agent proximal policy optimization (MAPPO)-based CPP scheme. In specific, the CPP problem is modeled as a partially observable Markov decision process (POMDP). Since the path planning of the AUVs is not only related to the local information but also the other AUVs' information, the information should be shared among AUVs based on the UCDNs. Furthermore, we introduce the MAPPO-based algorithm under the centralized training with decentralized execution (CTDE) architecture. Extensive simulations are carried out to demonstrate the strength of the proposed scheme.

Emerging Internet of Things (loT) applications, such as autonomous driving and environment monitoring, require fresh and timely information. For these applications, satellite edge computing (SEC) can provide low-latency services for user equipments (UEs) that lack direct access to terrestrial infrastructures. In this work, we investigate the problem of task offloading and resource scheduling of SEC for freshness-aware services in a satellite-terrestrial integrated network (STIN). In the STIN, tasks generated by UEs need to be processed promptly by either the satellite onboard or the remote cloud computing center. To capture the freshness of information, we formulate the above problem as a mixed integer non-linear dynamic programming problem. We further propose a freshness-aware task offloading and resource scheduling algorithm (FATORSA) to minimize the freshness of information by decomposing the above problem into two sub-problems. Firstly, we use a convex optimization algorithm to solve the sub-problem of communication resource scheduling under given satellite computation resource. We then convert the sub-problem of task offloading and computation resource allocation into a model-free Markov Decision Process (MDP), and solve it by a deep reinforcement learning method based on Proximal Policy Optimization (PPO). Simulation re-sults show that FATORSA reduces the freshness of information effectively and outperforms benchmarks.

Full-duplex (FD) system has the potential to double throughput, enabling to transmit and receive information simultaneously via self-interference (SI) suppression. In this paper, we focus on the problem of optimally allocating the transmission power for multi-user in the full-duplex communication mode. Firstly, we study the achievable bidirectional rate of full-duplex link as a function of signal over noise ratios (SNRs) and self-interference over noise ratios (INRs). We find that whether full-duplex is better than half-duplex is closely related to transmission power. Then, from the perspectives of SNR/INR and communication range, we divide the whole operation region into three parts: the full-duplex region, the half-duplex region and the gain-loss region. Aiming to maximize the sum-rate for multi-user which is hardly to be solved with explicit expressions, we propose an iterative algorithm to find the optimal transmission power. Simulation results show that compared with the average power allocation, our proposed algorithm has better performance.

In this paper, we propose an imaging scheme for unmanned aerial vehicle (UAV)-Enabled integrated sensing and communication (ISAC), where the UAV serves as a flexible communication auxiliary and a versatile sensing platform with the cooperation of a ground base station (GBS). To guarantee the performance of both sensing and communication, the proposed imaging scheme is based on the orthogonal frequency division modulation (OFDM) ISAC waveform and bistatic communication-assisted sensing strategy, which can be divided into three steps. Firstly, the UAV transmits OFDM ISAC signal, which contains the UAV position information to enable the communication-assisted sensing strategy. Secondly, the GBS receives the line-of-sight (LoS) ISAC signal from the UAV and the reflected ISAC signal from targets, in which the bistatic sensing architecture is designed to process data frequently and bypass the self-interference problem. Thirdly, the GBS preprocesses the received signal and reconstructs the image based on polar format algorithm (PFA) with the knowledge of UAV positions to relax the constraint of UAV trajectory. Numerical simulations are carried out to validate and evaluate the proposed UAV-enabled ISAC imaging scheme.

Traffic classification is indispensable for the Internet of Things (IoT) in intrusion detection and resource management. Deep-learning (DL)-based strategies are the key tools for traffic classification due to high accuracy but still have some challenges: 1) it is hard to deploy complex DL models on resource-constraint IoT devices and 2) performance is limited because of the ignorance of the similarity between IoT traffic. To address these issues, we propose lightweight but accurate models for traffic classification. First, we adopt a network-in-network basic model to reduce model size. Second, the basic model is trained with self-distilled response, feature map, and similarity among traffic types to enable its identification accuracy. Next, redundant filters are removed from the basic model to achieve compressed architectures. Then, a teacher model updating scheme with knowledge distillation is proposed to train compressed models without compromising performance. Experimental results demonstrate that compared to the state-of-the-art deep packet model, the compressed model can achieve the highest accuracy, deal with imbalanced traffic, and reduce nearly 99% of computation overhead in two encrypted traffic classification scenarios, thus, emphasizing its efficiency.

Traffic classification task is to identify different types of Internet traffic or applications. Classical traffic classification methods have the limitation that they need to predefine the features. The emergence of new applications reduces their accuracy due to the inaccurate feature design. The deep learning-based methods could extract features from raw traffic data and achieve high accuracy, but accordingly, leading to more complex models and heavier computations. The above deep models are hard to be deployed on edge nodes or resource-limited IoT devices. Therefore, in this paper, we adopt novel compressed models based on a two-step distillation approach for traffic classification. To address the trade-off problem between classification accuracy and model complexity, we first design lightweight models and then propose a novel training procedure to enhance their classification accuracy. Specifically, the response, relationship, and feature map-based knowledge of different traffic are distilled to train the small models. Experiment results demonstrate that compared to the state-of-the-art model, the minimum model using the proposed method can achieve higher accuracy, F1scores, and reduce nearly 99.7% computation overhead, thus verifying the effectiveness of our method.

Cooperative communication is a promising technology to improve the performance of resource-limited or cost-sensitive single antenna wireless equipment. Moreover, full duplex (FD) could theoretically double the spectral efficiency of a cooperative communication system comparing to the conventional half duplex (HD) communication. Although researchers have studied FD cooperative communication with two source nodes, and proposed an Alamouti-style space-time code, there is not much works on multiple source nodes scenarios yet. This article presents our results on the FD cooperative communication with multiple source nodes. We provide two orthogonal space-time block codes (OSTBC) for both three and four source nodes scenarios. The proposed codes skillfully share data in as less time slots as possible by taking advantages of the FD communication. Consequently, spectral efficiency of the FD cooperation is improved. Furthermore, the proposed codes provide full diversity in addition to a simple ML decoding. Finally, computer simulations verify the performance of the proposed OSTBC design.

Satellite mobile edge computing (SMEC) enhanced satellite-terrestrial integrated networks (STIN) have attracted intensive attention to obtain seamless coverage and provide on-demand computation services. However, the cooperative task execution among low earth orbit (LEO) satellites is largely ignored in the SMEC-STIN. In this paper, we explore a hybrid cloud and edge computing architecture of the SMEC-STIN with coordinated task processing among neighboring LEO satellites. We investigate the computation offloading and resource allocation strategies to minimize the long-term cost in terms of a trade-off between task execution latency and energy consumption. We formulate the optimization problem as a Markov decision process and design a proximal policy optimization based deep reinforcement learning method to approximate the optimal solution with robust training stability and low storage demand. Simulation results validate the effectiveness of our proposed method.

Low Earth Orbit (LEO) satellites have become great extensions of terrestrial networks to enable the global coverage and ubiquitous communications. However, the LEO system needs to handle the problem of frequently inter-satellite handover where the conventional single-user based handover will incur significant signaling overhead and increase the probability of handover failure, especially in massive user terminals (UTs) scenarios. Thus, we propose a novel group handover strategy for LEO satellite networks which mainly includes a user grouping algorithm and a handover scheduling method. Specifically, we first use the hierarchical clustering to group the UTs according to the network load and the properties of UTs. We next design a division restricted satellite selection algorithm based on a network-flows model, where the user group is scheduled as a whole in the first priority. Simulation results validate the great advantages of the proposed strategy in the signaling overhead reduction and the improvement in terms of the successful handover possibility, the quality of experience (QoE) of UTs and the load balance during the inter-satellite handover.

Adopting full-duplex (FD) technique in the underlay cognitive radio network (CRN), the secondary user can sense the activity of the primary user (PU) and transmit data simultaneously to enhance spectrum reuse efficiency. However, the sensing accuracy degrades due to the self-interference compared with the discrete sensing and transmission half-duplex network. We employ deep reinforcement learning models to analyze and solve the power control problem with the objective to minimize the adjustment steps in FD underlay CRNs. The proposed power control algorithm can help the secondary transmitter search an optimal transmit power, which satisfies pre-defined quality of service (QoS) requirements while retaining interference to the PU under a threshold. Simulation results show that compared to the benchmark, our method can achieve lower average number of transactions, and reduce more than 53.3% and 98.1% of the computing time and the storage resources, which verifies the effectiveness of our scheme.

Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.

Optical wireless communication (OWC) meets the demands of the future six-generation mobile network (6G) as it operates at several hundreds of Terahertz and has the potential to enable data rate in the order of Tbps. However, most beam-steering OWC technologies require high-accuracy positioning and high-speed control. Resonant beam communication (RBCom), as one kind of non-positioning OWC technologies, has been proposed for high-rate mobile communications. The mobility of RBCom relies on its self-alignment characteristic where no positioning is required. In a previous study, an external-cavity second-harmonic-generation (SHG) RBCom system has been proposed for eliminating the echo interference inside the resonator. However, its energy conversion efficiency and complexity are of concern. In this paper, we propose an intra-cavity SHG RBCom system to simplify the system design and improve the energy conversion efficiency. We elaborate the system structure and establish an analytical model. Numerical results show that the energy consumption of the proposed intra-cavity design is reduced to reach the same level of channel capacity at the receiver compared with the external-cavity one.

Delay line based uniform circular antenna array (UCA) is commonly used for orbital angular momentum (OAM) generation. It is known that delay line is preferred for Multiple-input Multiple-output(MIMO) beamforming than the ideal phase shifter(PS), as the latter causes the problem of beam squint. Different from traditional MIMO beamforming, we theoretically prove in this paper that ideal PS is preferred for UCA based OAM communications, while delay line causes vortex distortion. Specifically, through the derivation of baseband equivalent model, we demonstrate that vortex distortion of delay line is a time-varying process, which is affected by OAM mode and signal bandwidth. To verify our analysis, we first simulate instantaneous distortion, and then simulate isolation and spectrum efficiency in different OAM mode and bandwidth configurations. The simulation results indicate that for UCA based OAM commu-nication systems using delay line, higher OAM mode and higher bandwidth greatly limit mode isolation and spectrum efficiency. Our research also applies to delay line based customized OAM antennas, such as helicoidal parabolic antenna, traveling wave circular loop antenna and so on. The research result is of great significance to OAM based wireless communications.

As a supplement of cloud computing, fog computing has attracted wide attention due to its lower latency in data offloading. At present, most researches can only offload data through fixed nodes or can not effectively reduce the offloading delay of different types of data. And it is still a great challenge to develop an effective mechanism for offloading fog nodes selection. Due to the consumption of bandwidth, storage capacity, power and other resources in the process of data transmission, we must formulate a pricing strategy to ensure the revenue of fog nodes. Game theory is a widely adopted method to analyze the pricing strategy between clients and fog nodes. Therefore, this paper uses Stackelberg game to model the interaction between clients and fog nodes. And put forward the best strategy of clients and fog nodes by seeking their Nash equilibrium. Finally, the simulation results show that this mechanism can effectively reduce the offloading delay of clients and improve the revenue of fog nodes.

Recent progress in self-interference cancellation technology has demonstrated the in-band full-duplex wireless communication. However, the inter-client interference caused by the simultaneous co-channel uplink and downlink transmission in the asymmetric full-duplex communication can decrease the network throughput. The conventional solutions to this problem are finding the hidden stations by frequent inter-client channel probing, which caused a lot of signaling overhead. In this paper, we propose an asymmetric full-duplex medium access control (MAC) protocol using the beam divergence feature of the orbital angular momentum (OAM) waves. The noteworthy features of our protocol consist of two aspects. Firstly, the protocol avoids the inter-client interference through the divergence feature of the OAM wave, which does not need frequent inter-client channel probing and reduces the signaling overhead compared with the conventional solutions. Secondly, asymmetric full-duplex communication can be established in high probability with the appropriate multiplexing of OAM modes. Through the simulation results, the throughput of the asymmetric full-duplex network outperforms the other proposed full-duplex MAC protocol and half-duplex network.

Accurate traffic classification is critical for network QoS provisioning and cyberspace security. Recently, classifying different traffic using convolutional neural networks (CNN) has achieved high accuracy. However, these large CNN models have millions of parameters, which are not suitable for edge computing hardware deployment. In this work, we propose a compressed network in network (NIN) model for traffic identification. A stepwise pruning and knowledge distillation (KD) is designed for training the compressed model, which aims at reducing storage and computing resources. Our method is validated with the public ISCX VPN-nonVPN traffic dataset. Experimental results show that without degrading classification accuracy, our minimum model can save more than 50% of the number of parameters and 30% of the computation time comparing with the uncompressed NIN model. The test set average F1score of 0.9805 of the minimum model is higher than that of the state-of-the-art model, which is a CNN model.

With the rapid growth of terminal equipments, the data traffic in the network has grown exponentially. In order to relieve the pressure of cloud computing on link delay, congestion and energy consumption, the promising fog computing is proposed. The fog network consists of several fog clusters. We consider a fog cluster in which a fog controller (FC) aims to schedule the idle fog nodes (FNs) to serve the task node (TN) while guaranteeing the quality of service (QoS) requirements of the TN. We design an ascending-bid auction mechanism to achieve this goal. In this mechanism, the FC is the auctioneer with the reward prices as its strategy and the FNs play the role of bidders with the task sizes as their strategies. The FC uses the bid prices to motivate the FNs to process more data for the TN. The utility function of FNs is proposed, considering the payment from the FC, the cost of task computational delay and energy consumption. The FNs determine the data sizes to be processed by maximizing their utilities. Numerical simulations indicate the satisfactory performance and verify the theoretical analysis, thereby our proposed mechanism results in a win-win solution under the condition of meeting the QoS.

A reverse transmission mechanism for smart grid is introduced, in which the transmission robustness of ad-hoc net-segment is guaranteed. The goal is to address the deadlock issue caused by the broken down of the targeted tower , and to reduce transmission power consumption. Performance analysis of the new mechanism is presented, in which the configuration of different number of electrical towers is discussed. Upon examination of the simulation results, we conclude that the proposed mechanism provides lower latency and power consumption compared to the traditional transmission mechanism, and guarantees the transmission robustness for the smart grid.

In this paper, we propose a new and rather simple grouped-subcarrier allocation algorithms with proportional fairness among users in downlink OFDM transmission. The proposed algorithm tries to minimize the required transmit power while satisfying the rate requirement and BER constraint of each user. Subcarrier and power allocation are performed in two steps. We are mainly focusing on the subcarrier allocation step. First subcarriers are grouped to reduce the computational complexity of the allocation algorithm. Then we employ proportional constraints in terms of rate ratios to assure each user to achieve the target data rate in advance. Simulation results show that this low complexity resource allocation scheme achieves better performance than the fixed frequency division approach and shows comparable performance compared to previously derived suboptimal resource distribution schemes. It is also shown that with rate constraints the capacity is distributed more fairly and rationally among users