Heterogeneous satellite communication systems consisting of geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellites have attracted significant attention due to their complementary advantages in wide coverage and low-latency transmission. In this paper, we investigate the joint data scheduling and precoding problem in multi-antenna GEO-LEO heterogeneous satellite systems. Jointly considering the dynamic network topology, time-varying channel conditions, stochastic traffic arrivals, and queue dynamics, we model the problem as a long-term average cache queue length minimization problem under quality of service (QoS) constraints. To tackle the formulated mixed-integer non-convex optimization problem, we decompose it into two subproblems, i.e., data scheduling subproblem and precoding subproblem, and design a nested iterative solution framework. To address the data scheduling subproblem, we formulate it as a Markov decision process (MDP) and put forward a proximal policy optimization (PPO)-based data scheduling algorithm. Given the state and action of the MDP, the joint optimization problem is reduced to a precoding subproblem, which can then be transformed into a weighted mean square error minimization problem, and is efficiently solved via the Lagrangian dual method. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Deep reinforcement learning has demonstrated outstanding performance in autonomous driving (AD). However, independent single vehicle training struggles to cope with complex traffic environments, while collaborative training across multiple vehicles causes security risk in data sharing. To address these challenges, this paper proposes a dual-layer blockchain-enabled federated reinforcement learning algorithm (DBFRL) for personalized AD. The proposed DBFRL algorithm constructs a FRL architecture based on a dual-layer blockchain to ensure data security during training. Practical driving behaviors data from the HighD dataset are used to classify driving styles into three categories: timid, normal and aggressive. Correspondingly, the personalized multi-objective reward functions are designed to reflect individual driving preferences. Then, the improved TD3 algorithm with different experience replay buffers and prioritized experience replay mechanism are using in the local model training. Furthermore, the reputation values of connected autonomous vehicles are introduced to ensure high-quality global model aggregation. The effectiveness of the DBFRL algorithm is validated on the CARLA simulator. Simulation results confirm that it significantly improves training performance and preserves data safety simultaneously.

The Internet of Things (IoT) employing symbiotic radio (SR) technology encounters challenges such as low throughput and susceptibility to double fading. To address these challenges, this paper integrates non-orthogonal multiple access (NOMA) with simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) technology in an SR system, introducing a novel transmission model termed STAR-RIS-assisted NOMA-SR with hybrid active-passive communication. The proposed model operates in three phases. In the first two phases, when the primary system’s licensed spectrum is occupied, the backscatter devices (BDs) utilize backscatter communication (BC) to establish a symbiotic relationship with the primary system. Specifically, in Phase 1, STAR-RIS enhances the energy harvesting (EH) of BDs via the reflection mode, while in Phase 2, it aids both the primary and secondary systems via the transmission mode. In Phase 3, when the licensed spectrum is idle, STAR-RIS facilitates the active communication (AC) of BDs via the transmission mode. To maximize the total throughput of BDs while guaranteeing the primary system’s target throughput, we formulate a non-convex optimization problem and develop a block coordinate descent (BCD)-based resource allocation scheme. The problem is decomposed into subproblems and solved using successive convex approximation (SCA), variable substitution, and semi-definite relaxation (SDR) to jointly optimize transmission time, beamforming, STAR-RIS reflection and transmission coefficients, as well as BDs’ power allocation and reflection coefficients. Numerical results show that the proposed scheme enhances the total throughput of BDs by 14.36%, 43.43%, 67.78%, and 439.69% compared to four baseline schemes.

Narrow Band (NB) Internet of Things (IoT) is a low bandwidth wireless communication technology. The satellite IoT combines satellite and IoT technology, which can effectively assist IoT devices in remote areas to communicate. Due to the time delay in data transmission in narrowband satellite IoT, we have introduced the Age of Information (AoI) metric. We design a two-stage resource management scheme, which estimates resources first and then schedules resources. Construct an optimization problem that minimizes the required satellite bandwidth resources by constraining the average AoI of devices in the scenario. On this basis, user scheduling and resource allocation are carried out. We use Lyapunov optimization to transform continuous time optimization problems into single slot problems. In the end, we adopted particle swarm optimization algorithm and Reinforcement Learning (RL) algorithm to solve the problem. The simulation proves that the proposed scheme has advantages in reducing AoI and improving bandwidth utilization.

Semantic communication reduces data transmission by transmitting task-relevant semantic information, offering a new approach for efficient transmission in resource-constrained scenarios. To address the issues of inefficiency and data loss caused by bandwidth constraints and dynamic channel variations in satellite communications, this paper proposes a semantic communication architecture based on the Transformer and Generative Adversarial Networks (GANs) called TransGAN. This architecture employs a Transformer to extract core semantic features from telemetry data, effectively compressing the data to reduce bandwidth usage, while the generator learns the distribution of core semantic features through adversarial training, enabling it to generate realistic samples even when semantic information is missing. Furthermore, TransGAN is designed with a robust adversarial optimization strategy to enhance recovery performance in high-noise or packet loss scenarios within dynamic channel environments. Experimental results show that TransGAN significantly outperforms existing methods under various SNR and rates of semantic loss, achieving efficient and reliable semantic recovery and providing a new technological pathway for satellite Telemetry data transmission.

The emerging architecture in next-generation mobile networks leverages the coexistence of low Earth orbit (LEO) and geostationary Earth orbit (GEO) satellites within heterogeneous networks. This setup not only enables seamless coverage but also enhances user data rates. However, due to the scarcity of resources in such heterogeneous satellite coexistence networks, efficiently allocating onboard resources, particularly spectrum resources, poses a significant challenge. In the context of satellite communication systems under dynamic heterogeneous multi-system coexistence environments, we design a cross-system joint optimization framework and propose an online resource allocation scheme based on beam-hopping (BH) to coordinate inter-system transmissions. This work further considers the uncertainty of beam service durations and formulates a resource optimization problem. We decompose this problem into two sub-problems: inter-cell resource allocation and intra-cell resource allocation. For inter-cell resource allocation, we adopt the Deep Deterministic Policy Gradient (DDPG) algorithm to provide a solution. Intra-cell resource allocation is addressed using traditional convex optimization methods. Simulation results validate that the proposed algorithm ensures fairness in LEO satellite data processing while effectively improving overall system performance.

Low-orbit satellite Internet of Things (LEO-IoT) has become a key technology for connecting remote terminals due to its wide-area coverage capability. However, the dynamic network topology, resource constraints, and service burstiness pose serious challenges to quality of service (QoS) assurance for delay-sensitive applications. In this paper, we propose an innovative approach to address traffic shaping, resource scheduling and multipath load balancing in LEO-IoT systems by fusing network calculus theory with deep reinforcement learning (DRL) techniques. We propose a QoS-oriented dynamic load balancing strategy to alleviate the local congestion and throughput degradation problems caused by time-varying topology and uneven traffic distribution. We establish an upper bound model for inter-satellite link delay based on network calculus, and determine the worst delay bound for multi-hop links through the least-additive convolution operation of the hopping-beam satellite arrival curves and the inter-satellite link service curves. On this basis, we design a load balancing algorithm for joint beam scheduling and traffic allocation, which dynamically optimizes the beam dwell time and coordinates the multipath weight allocation. In addition, we implement a DRL framework based on proximal policy optimization. Simulation results show that the proposed strategy achieves significant improvements in performance over traditional methods in LEO-IoT environments.

With the widespread adoption of edge computing, service migration is critical for meeting real-time computing demands and ensuring service continuity. However, the dynamic and uncertain nature of edge computing-enabled networks, characterized by fluctuating topologies, bandwidth, and resources, significantly complicates migration decisions. Existing strategies rely on precise analytical models and reward functions but struggle with generalization and adaptability. This paper proposes a novel service migration strategy driven by active inference for edge computing-enabled networks. Unlike traditional approaches, it eliminates the need for explicit reward functions, instead leveraging a cognitive optimization mechanism where decisions are guided by minimizing free energy. This allows the system to maintain efficient service migration across a wider range of edge scenarios, with enhanced generalization and flexibility. Simulation results show that the proposed strategy outperforms existing approaches by reducing latency and improving adaptability to varying environments, highlighting its superiority in service migration for edge computing-enabled networks.

In this work, we explore the task execution problem within unmanned aerial vehicle (UAV)-assisted low Earth orbit (LEO) satellite offloading networks. We define a system cost function that includes both energy consumption and task dropping cost, and formulate the joint power allocation, task offloading and scheduling, and UAV flight trajectory planning problem as a constrained system cost minimization problem. Given that the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we decompose the problem into four subproblems, i.e., IoT device task transmission subproblem, UAV trajectory design subproblem, power allocation subproblem, and task offloading and computing scheduling sub-problem. We propose an iterative algorithm for the first three subproblems and a heuristic for task offloading and computing scheduling subproblem. The simulation results reveal that our proposed method achieves superior performance compared to the existing algorithm.

Multiagent reinforcement learning (MARL) has emerged as a foundational approach for addressing diverse, intelligent control tasks in various scenarios like the Internet of Vehicles, Internet of Things, and unmanned aerial vehicles. However, the widely assumed existence of a central node for centralized, federated learning-assisted MARL might be impractical in highly dynamic environments. This can lead to excessive communication overhead, potentially overwhelming the system. To address these challenges, we design a novel communication-efficient, fully distributed algorithm for collaborative MARL under the frameworks of soft actor-critic (SAC) and decentralized federated learning (DFL), named regulated segment mixture-based multiagent SAC (RSM-MASAC). In particular, RSM-MASAC enhances multiagent collaboration and prioritizes higher communication efficiency in dynamic systems by incorporating the concept of segmented aggregation in DFL and augmenting multiple model replicas from received neighboring policy segments, which are subsequently employed as reconstructed referential policies for mixing. Distinctively diverging from traditional reinforcement learning (RL) approaches, RSM-MASAC introduces new bounds under the framework of maximum entropy reinforcement learning (MERL). Correspondingly, it adopts a theory-guided mixture metric to regulate the selection of contributive referential policies, thus guaranteeing soft policy improvement during the communication-assisted mixing phase. Finally, the extensive simulations in mixed-autonomy traffic control scenarios verify the effectiveness and superiority of our algorithm.

In complex jamming environments with multi-user spectrum sharing, existing distributed anti-jamming strategies are constrained by significant communication overhead, limited efficiency in knowledge dissemination, and low collaborative effectiveness. To address these challenges, a distributed anti-jamming strategy based on local knowledge diffusion and differential weighted fusion mechanisms (LKD-DWF-M) is proposed. In this strategy, a local knowledge diffusion mechanism is introduced to facilitate knowledge sharing among communication nodes, enabling each node to gain a comprehensive understanding of its neighbors’ behavior. Subsequently, a knowledge contribution measurement method based on mutual information is proposed, and a differential weighted fusion (DWF) mechanism is designed to effectively integrate the policy and value parameters of neighboring nodes. This integration enables accurate global value estimation while optimizing individual anti-jamming strategies. Additionally, the existence of the Nash equilibrium (NE) for each node’s policy and value parameters is theoretically established using Kakutani’s fixed-point theorem. Furthermore, through the construction of a Lyapunov function, it is demonstrated that the proposed strategy can stabilize and converge to the NE in the long-term jamming counteraction process. Simulation results indicate that, in comparison to anti-jamming strategies employing global knowledge diffusion and differential weighted fusion mechanism (GKD-DWF-M), global knowledge diffusion and average fusion (GKD-AF-M), and local knowledge diffusion and average fusion (LKD-AF-M), the proposed distributed anti-jamming strategy achieves respective improvements of 4%, 17%, and 20% in system normalized throughput under statistical jamming (SJ). Under dynamic sweeping jamming (DSJ), the system normalized throughput improves by 8%, 11%, and 11.5%, respectively; under intelligent comb jamming (ICJ), it increases by 10%, 10.5%, and 19%, respectively; and under intelligent block jamming (IBJ), it increases by 5%, 16%, and 21%, respectively. Moreover, the proposed strategy exhibits superior convergence speed compared to other strategies. When the jammer alternates between SJ, DSJ, ICJ, and IBJ, the proposed distributed anti-jamming strategy responds quickly, demonstrating robustness in dynamic jamming environments.

Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), which promotes connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data to protect data privacy. In this paper, consider the limitation of model accuracy and communication overhead in FL, as well as further verification in the real scenarios, we propose a directed acyclic graph (DAG) blockchain-based IoV system that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-assisted asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model accuracy, which utilizes mutual distillation method to train a teacher-student model simultaneously. Moreover, a policy network will first be pre-trained by an expert data augmentation strategy through the DAFML algorithm via the behavior cloning, and be re-trained through the proposed proximal policy optimization (PPO) algorithm based autonomous driving framework. Finally, simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy, distillation ratio and autonomous driving decision.

Collaborative resource allocation is crucial for reducing overhead and enhancing resource utilization in edge computing-enabled networks. To ensure a satisfactory user experience, we recognize the importance of considering information uncertainty in resource allocation. Therefore, we explore information uncertainty in edge computing-enabled networks, especially within the complex environment of resource coupling. However, existing methods lack a comprehensive and robust solution for coordinating wireless, transport, and computing resource under this information uncertainty. This paper addresses this gap by proposing a joint optimization of access point (AP) selection, computing node association, and traffic engineering, aiming to maximize network utility under the uncertain conditions of wireless status and application QoS requirements. The constraints under these uncertainties are modeled as chance constraints, complicating the problem's solvability. We adopt the Bernstein approximation to establish convex conservative approximations of the chance constraints. Given the problem's substantial size and computational complexity, the alternating direction method of multipliers is employed to solve the approximated problem in a distributed manner. We further derive the closed solutions of the corresponding sub-problems. Extensive simulations validate the superiority of our proposed scheme, demonstrating its ability to achieve a good trade-off between meeting user requirements and optimizing resource utilization.

In this study, we investigate minimization of energy consumption in multi-UAV assisted networks. We formulate an energy minimization optimization problem with UAV trajectory design, content fetching, power allocation and content placement constraints. The problem is a mixed integer nonlinear programming (MINLP); therefore, we convert the formulated problem into semi-Markov decision process (SMDP). To tackle this SMDP optimization challenge, we introduce an option-based hierarchical deep reinforcement learning (OHDRL) approach. We designate UAV trajectory planning and power allocation as the low level action space, and content placement and content fetching as the high level option space. Through simulations, we demonstrate the effectiveness of the proposed OHDRL method.

In future space-ground integrated networks, a satellite-based core network can reduce frequent signaling interactions between satellites and ground stations, thereby enhancing network architecture and supporting global communications. Users can achieve end-to-end communication through the satellitebased User Plane Function (UPF). However, the high dynamics of Low Earth Orbit (LEO) satellites result in frequent inter-satellite handovers, significantly affecting user service continuity. Existing satellite handover strategies are overly simplistic and fail to ensure the Quality of Service (QoS). Additionally, ground users compete for satellite links based on limited observations, leading to network congestion. This paper proposes a loadbalanced, distributed, multi-agent deep reinforcement learning method for satellite handover. We formulate a combinatorial optimization problem to maximize the total utility of user-satellite associations across various service types. Each user acts based on local information and engages in distributed matching with satellites. Simulation results indicate that our method ensures QoS for various service types, optimizes load balancing, and outperforms basic handover strategies in terms of handover success rate and frequency.

Low Earth Orbit (LEO) satellite systems provide geographically unrestricted services to ground users. However, the conflict between existing resource allocation schemes and the variability of inter-beam traffic is becoming increasingly prominent. To address this issue, this paper proposes a resource allocation strategy for beam-hopping satellite networks based on Rate-Splitting Multiple Access (RSMA) technology, aiming to reduce co-channel interference while improving system resource utilization. First, by analyzing the resource allocation challenges faced by beam-hopping satellite networks, including low spectrum utilization and co-channel interference, the background and motivation for the proposed strategy are provided. Next, RSMA technology is introduced, dividing user messages into common and private parts, and a corresponding resource allocation algorithm is designed to enhance spectrum utilization and reduce co-channel interference. Through the construction of a system model and simulation experiments, the effectiveness and performance advantages of the proposed strategy are verified. This study provides new ideas and methods for resource allocation in beam-hopping satellite networks, which is significant for improving system performance and service quality.

Unmanned aerial vehicles (UAVs) are expected to act as aerial relays which forwards data packets for ground users (GUs) leveraging their advantages of low cost, high flexibility and maneuverability. One challenging problem in UAV-assisted cellular systems is how to design the efficient UAV deployment, GU association and resource allocation strategy which achieves system performance optimization. In this paper, we address the data transmission problem in a UAV-assisted cellular system with the knowledge of statistical GU positions. Stressing the energy consumption of base station (BS) and UAVs, and the cost of UAVs, we formulate the joint UAV deployment, GU association and power allocation problem as a constrained system cost minimization problem. To solve the formulated problem, we decouple it into three subproblems, i.e., UAV deployment, GU association and power allocation subproblem. Then, the UAV deployment subproblem is modeled as a Markov decision process (MDP), and an embedded multi-agent double deep $\mathbf{Q}$ network (DDQN) algorithm is proposed. Specifically, given the state and action of the MDP, we formulate and solve the power allocation subproblem and determine the transmit power of the UAVs by applying the Lagrange dual method-based algorithm. The GU association subproblem is then tackled by utilizing a proposed Kuhn-Munkres (K-M) algorithm-based scheme. Based on the obtained power allocation and GU association strategy, the reward of the MDP can be computed and the UAV deployment strategy is determined which maximizes the long-term average reward. Simulation results demonstrate the effectiveness of the proposed algorithms.

The uncertain factors such as network status, measurement errors and quality of service (QoS) requirements of applications make it challenging to guarantee the performance of edge computing-enabled networks through resource allocation schemes modeled on accurate information. This paper investigates the impact of information uncertainty on resource allocation in edge computing-enabled networks. We model the resource constraints as chance constraints and jointly optimize wireless access point (AP) selection, computing node association, and traffic engineering to maximize the network utility. Since the problem contains uncertainty parameters and binary variables, it is intractable to solve. Therefore, we utilize the Bernstein approximation to derive convex conservative approximations for chance constraints. To address the unrealistic nature of the problem due to its large size and computing complexity, we employ the alternating direction method of multiplier to iterate wireless AP selection, computing node association, and bandwidth allocation in a distributed manner. Additionally, we use the convex optimization method to solve the corresponding sub-problems. Simulations are conducted to demonstrate that our proposed resource allocation scheme can satisfy more requirements and save more resources than other schemes.

The dynamic edge computing-enabled networks contain various resources, and network parameters and system models are subject to uncertainty. Despite this, there is still a lack of comprehensive online solutions for coordinating wireless, transport, and computing resources. This paper investigates the use of online convex optimization for resource allocation in edge computing-enabled networks with time-varying cost and time-varying constraint functions. Taking into account the uncertainty of wireless status, quality of service requirements, and cost function, the goal is to minimize the long-term cost by optimizing the selection of access points, association of computing nodes, allocation of computing resources, and bandwidth allocation. To address the proposed online resource allocation problem, the modified online saddle-point algorithm is employed and dynamic regret and accumulative constraint violation are defined to measure the performance of the algorithm. To reduce the computational complexity of the projection in the modified online saddle point algorithm, the projection is reformulated as quadratic programs, which can be solved efficiently by convex optimization. Finally, the effectiveness and superiority of the proposed solution are demonstrated through simulation analysis.

In this work, we investigate content delivery and target sensing problem in unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks where UAVs are allowed storing user-requested contents, delivering the content to users and performing target sensing as well. To jointly address the performance of content transmission and target sensing, we define utility function and formulate the UAV deployment, communication and sensing precoder design problem as a constrained utility maximization problem. As the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we transform it into two subproblems, namely, user grouping and UAV deployment subproblem, and communication and sensing precoder design subproblem, and solve the two subproblems by using an alternate iteration-based algorithm. Specifically, we first design a mean-shift-based user grouping strategy which divides users into different groups and then propose a UAV deployment strategy based on successive convex approximation (SCA)-based iterative algorithm and the first order Taylor expansion method. To solve communication and sensing precoder design subproblem, we propose a two-layer penalty-based SCA algorithm. Simulation results demonstrate the effectiveness of the proposed algorithms.

Network availability and service continuity are major concerns for network operators to provide reliable communication services for Internet of Things (IoT), which are particularly challenging to achieve in virtualized network slicing environment where network services are exposed to the failure risks of both software (virtual network function (VNF) instances) and hardware (physical nodes). In general, the redundancy-based VNF backup solutions are used to improve the reliability of virtualized network slices. However, backup VNFs require the same amount of resources as the primary VNFs, which will result in high-resource cost. In this article, we propose a joint VNF partition and hybrid backup scheme for VNF orchestration, backup and mapping, whose aim is to construct the reliability-enhanced and delay-guaranteed network slices at minimum cost. Specifically, the VNF partition method divides a single VNF into multiple thinner VNFs with lower processing capacity and is expected to enhance the reliability of network slices with less additional resources. The hybrid backup scheme includes both onsite and offsite backup forms. Then, considering the time-varying network environment and IoT service requirements, we formulate the VNF orchestration, backup and mapping as a dynamic mixed integer linear programming (DMILP) problem, and model the dynamic problem as a Markov decision process (MDP). In view of the large action space of the formulated MDP, we propose a multiagent deep reinforcement learning (DRL) approach with an action space reduction strategy to achieve the dynamic VNF orchestration, backup and mapping solution. Simulation results demonstrate that the proposed joint VNF partition and hybrid backup scheme can obtain superior delay and reliability performance with low-network cost.

Multibeam satellite communication systems have been received widespread attention due to their high throughput and efficient resource utilization. In this article, we investigate the beam illumination and resource allocation problem in multibeam satellite communication systems. By jointly considering user position and service characteristics, an optics-based initial user grouping algorithm is proposed. To enhance beam coverage performance, a minimum circle algorithm is proposed to optimally design satellite beam positions and coverage radius. Given the obtained user grouping strategy, we address the difference between random user service demands and service provisioning capability of the system, and define system cost function. The joint beam illumination, subchannel, and power allocation problem is formulated as a system cost function minimization problem. To solve the formulated optimization problem, we introduce aggregate nodes to describe the characteristics of user groups, and address the beam illumination and power allocation problem of user groups. The problem is modeled as a mixed-space Markov decision process (MDP), and a parameterized deep Q-network-based joint beam illumination and power allocation algorithm is proposed. Based on the obtained resource allocation strategy for user groups, we then design user-oriented subchannel and power allocation strategy. To this end, we model the optimization problem as an MDP and propose a double deep Q-network (DDQN) algorithm-based algorithm. To address the concern that the DDQN algorithm may reach a local optimum, proximal policy optimization algorithms with discrete action space and continuous action space are proposed. Simulation results validate the effectiveness of the proposed algorithms.

This paper proposes a deep learning (DL)-based time series (TS) anomaly detection method (DLTS) for the low earth orbit (LEO) satellite network slicing scenario, aiming to address the virtual link anomalies induced by software and hardware abnormalities. Initially, the time series anomalous variations of each resource utilization in satellite network slicing are categorized into three types based on the utilization of computing, storage, and network resources of virtual nodes. Thereafter, the anomaly detection problem is formulated as a classification problem, and the time series are transformed into images using the Gramian Angular Field (GAF) for model input. Lastly, we propose a design principle for a time-constrained deep neural network architecture to mitigate training time, and design a DL model architecture to classify the TS transformation images of resource utilization for each virtual node. This aligns with the objective of the satellite network slicing scenario. Additionally, a new evaluation metric is introduced. Experimental results underscore the shorter training time of the proposed model, and affirm its efficacy, demonstrated through accuracy, F1 score, and the newly proposed evaluation metric.

Anticipated to substantially enhance communication efficiency, semantic communication emerges as a novel communication paradigm. Considering the constraints of wireless resources, it becomes crucial to design resource allocation schemes that ensure efficient data transmission in a semantic communication system. This paper proposes a resource allocation scheme that maximizes the energy efficiency of the entire semantic network, ensuring the performance of semantic tasks within the confines of limited wireless resources. Specifically, we begin by defining the energy efficiency measurement metrics in semantic communication systems and subsequently optimize it through the joint allocation of semantic symbols, bandwidth, and power. This problem is formulated as an optimization problem. Given the absence of a mathematical closed-form expression for semantic similarity, an effective solution to the problem is proposed via a whale optimization algorithm with a penalty strategy, targeting joint semantic symbols assignment and resource allocation. Simulation results substantiate the effectiveness and feasibility of the proposed scheme.

The emerging architecture in the next generation of mobile networks leverages the coexistence of Low Earth Orbit (LEO) and Geostationary Orbit (GEO) satellites in a heterogeneous network. This setup not only offers seamless coverage but also enhances user rates. Nevertheless, the efficient allocation of onboard resources, particularly spectrum resources, poses a significant challenge due to their scarcity in such heterogeneous satellite coexistence networks. A practical solution is found in the use of beam hopping (BH) technology. This technology enables multi-beam satellites to serve users using fewer beams than traditional spot-beam systems. This paper proposes a resource allocation strategy for the heterogeneous LEO-GEO coexistence satellite network. We formulate this resource allocation strategy as a joint optimization problem. Due to the complexity of the system arising from the coupling of multiple variables, we break down the original problem into two manageable sub-problems. The first addresses user association, subcarrier, and power allocation and employs a standard convex optimization algorithm for a solution. The second tackles the illuminated beam selection issue, with a genetic algorithm (GA) providing a solution. The effectiveness of our proposed scheme is established through simulation experiments, demonstrating clear performance gains.

Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dynamic scenarios, and the excessive communication overheads possibly overwhelm the IoV system. Therefore, in this paper, we design a communication efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the communication overheads in a fully distributed architecture. In particular, RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by incorporating the idea of segment mixture and augmenting multiple model replicas from received neighboring policy segments. Afterwards, RSM-MAPPO adopts a theory-guided metric to regulate the selection of contributive replicas to guarantee the policy improvement. Finally, extensive simulations in a mixed-autonomy traffic control scenario verify the effectiveness of the RSM-MAPPO algorithm.

Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-preserving distributed machine learning in the Internet of Things (IoT). However, the communication overhead caused by partial model aggregations will increase the model training latency. In this article, a multilayer blockchain-enabled hierarchical FL (HFL) network is proposed for low-latency model training while ensuring data security. Meanwhile, we theoretically analyze the bottleneck of the model accuracy with the total data distance due to the imbalanced data distribution. Moreover, the mathematical expression of the model error with respect to IoT devices (IDs) association and local data distribution is provided, then the upper bound of the model error is represented by the total data distance. To further improve the learning performance, the distance-aware HFL (DAHFL) algorithm is investigated, which optimizes ID association strategy based on dual-distance, and allocates computing and communication resources alternatively. Finally, the working process of the blockchain-enabled HFL system is exhibited by the blockchain simulation platform and the efficiency of the proposed DAHFL algorithm is demonstrated by the simulation results.

Virtualized network slicing allows a multitude of logical networks to be created on a common substrate infrastructure to support diverse services. A virtualized network slice is a logical combination of multiple virtual network functions, which run on virtual machines (VMs) as software applications by virtualization techniques. As the performance of network slices hinges on the normal running of VMs, detecting and analyzing anomalies in VMs are critical. Based on the three-tier management framework of virtualized network slicing, we first develop a federated learning (FL) based three-tier distributed VM anomaly detection framework, which enables distributed network slice managers to collaboratively train a global VM anomaly detection model while keeping metrics data locally. The high-dimensional, imbalanced, and distributed data features in virtualized network slicing scenarios invalidate the existing anomaly detection models. Considering the powerful ability of generative adversarial network (GAN) in capturing the distribution from complex data, we design a new multi-discriminator Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) model to learn the normal data distribution from high-dimensional resource metrics datasets that are spread on multiple VM monitors. The multi-discriminator BiWGAN-GP model can be trained over distributed data sources, which avoids high communication and computation overhead caused by the centralized collection and processing of local data. We define an anomaly score as the discriminant criterion to quantify the deviation of new metrics data from the learned normal distribution to detect abnormal behaviors arising in VMs. The efficiency and effectiveness of the proposed collaborative anomaly detection algorithm are validated through extensive experimental evaluation on a real-world dataset.

Network slicing has become an essential technology for the future network. Obviously, it will play an important role in satellite networks as well. To address that quality of service (QoS) may be severely affected by embedding satellite virtual networks (SVNs), we propose a method for SVN admission control that can effectively guarantee the QoS of network slices by admitting SVNs embedded in the physical satellite networks. Specifically, firstly, we propose a two-stage SVN embedding mechanism that decouples short-term resource allocation from long-term admission control and resource leasing. Then, we consider the case of uncertain system capacity due to the highly dynamic nature of the satellite networks topology, and model the admission control problem as a robust optimization problem. The robust problem is transformed into a convex counterpart by using the Bernstein approximation. Finally, we solve the resource allocation problem by converting it into a convex problem. The simulation results show the effectiveness of the proposed method.

The satellite-ground integrated network has been proposed to be one of the key technologies in the next-generation wireless communication network. However, in the network with low orbit satellites (LEO) as relay nodes to serve the backhaul, both the wireless access links and the backhaul links are changing rapidly. In this paper, we propose to deploy the software-defined network and edge caching to extend the concept of content prefetch from users to networks, which compensates for the poor transmission conditions in the satellite-ground mobile network. Specifically, an optimization problem is proposed for the prefetching scheme based on the current and predicted network state (e.g., the cache state of the user and the base station), and movement patterns of users and satellites. The problem is a mixed integer nonlinear problem, which then is transformed into convex to be solved effectively. The simulation results show that the proposed prefetching scheme can improve the users’ experience.

With the development of satellite communications, satellites have been equipped with edge computing capability and edge caching capability, and these advancements can further drive the development of video transmission mechanisms. In this paper, we propose to utilize in-network caching and computing of software-defined space-air-ground integrated networks to improve the quality of video experience for users. The optimization problem can be viewed as a coupling of three parts, namely, the video resolution adaptation problem, the computing resource scheduling problem, and the bandwidth provision problem. To achieve the solution of the problem effectively in practice, we deploy the alternating direction method of multipliers to decouple the three sets of variables. Numerical results demonstrate the effectiveness of the proposed scheme.

In Low Earth Orbit (LEO) satellites, which run in polar orbit, the area of overlap among beams becomes wider as the latitude of satellites increases, which leads to intolerable interference and extra energy consumption. To minimize the onboard power with QoS requirements, we propose an energy optimization model with considering power allocation, user association and dynamic beam ON/OFF operation jointly. Moreover, the frequent beam ON/OFF operations lead to the large number of user handovers, so handover cost is also considered in the model. The original problem is decomposed into two levels due to the high coupling of variables and the successive convex approximation is employed. A low complexity greedy ON/OFF iteration is proposed to adapt to dynamic topology of LEO. Simulation results show that the proposed scheme can effectively reduce the system energy consumption.

Secure information transmission and energy efficiency (EE) optimization are very important for simultaneous wireless information and power transfer (SWIPT)-aided heterogeneous networks. However, most of the existing works consider perfect channel state information (CSI) and linear energy harvesting (EH) models, which are too ideal in practical systems. In this article, we focus on the EE-based robust optimization with imperfect CSI and nonlinear EH models in a SWIPT-aided two-tier heterogeneous macro-femto network with multiple eavesdroppers. In particular, we formulate a robust beamforming problem by jointly optimizing the beamforming vectors of the macro base station (BS) and femto BSs, the power splitting (PS) factors of energy receivers, and the artificial noise vectors of BSs, under multiple constraints including the quality of service requirement of each user, the minimum harvested energy, the maximum transmit power, and the PS factor. Although the formulated robust optimization problem is nonconvex, an EE-based iterative algorithm is developed to obtain the solutions. Simulation results demonstrate the proposed algorithm is superior to other algorithms in terms of EE and security.

Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users who without licensed spectrum in the network can suffer from long executing time, which is not acceptable for latency-sensitive and computation- intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user, i.e., the quality of service of the PU can be guaranteed, and within the available computation resources of the MEC server, the primary user and secondary users with different channel gains both can offload tasks to the MEC server through non-orthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the non-convexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near-optimal solution. Simulation results show that the proposed scheme can effectively reduce the overall computing latency for the secondary network.

Recent advances of computing, caching, and communication (3C) can have significant impacts on mobile social networks (MSNs). MSNs can leverage these new paradigms to provide a new mechanism for users to share resources (e.g., information, computation-based services). In this paper, we exploit the intrinsic nature of social networks, i.e., the trust formed through social relationships among users, to enable users to share resources under the framework of 3C. Specifically, we consider the mobile edge computing (MEC), in-network caching and device-to-device (D2D) communications. When considering the trust-based MSNs with MEC, caching and D2D, we apply a novel big data deep reinforcement learning (DRL) approach to automatically make a decision for optimally allocating the network resources. The decision is made purely through observing the network's states, rather than any handcrafted or explicit control rules, which makes it adaptive to variable network conditions. Google TensorFlow is used to implement the proposed deep Q-learning approach. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.

The exponential growth of data traffic volume dominates the demand for the next generation mobile networks (5G). The consistent and satisfied quality of experience (QoE) is one of the leading challenges of provisioning services in 5G mobile networks. Thus, in this paper, we propose a novel adaptive prefetching scheme to compensate the undesired transmission conditions in the 5G mobile network by extending the content prefetching concept from the users to the network. Specifically, an optimization problem is proposed for a prefetching scheme that adaptively retrieves users' data to access nodes and (or) user equipments (UEs) before the actual requests according to the network status, QoE status, predicted data rates, and mobility patterns of users. For the sake of tractability, the prefetching problem is transferred to a convex problem that can be solved efficiently. Accordingly, to implement the proposed schemes in the 5G network, system interactions among entities in the network are designed to realize prefetching-related functions. A signaling protocol to support the adaptive prefetching scheme is also presented. Simulation results show that an adaptive prefetching scheme can improve the network performance significantly.

Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have significant effects on video services in next generation mobile networks. In this paper, we jointly consider SDMNs, in-network caching, and MEC to enhance the video service in next generation mobile networks. We use a new video experience evaluation standard called U-video mean opinion score (vMOS), which is a more advanced measurement of the video quality based on the well-known vMOS. With the objective of maximizing the mean U-vMOS, an optimization problem is formulated. Due to the coupling of video data rate, computing resource, and traffic engineering (bandwidth provisioning and paths selection), the problem becomes intractable in practice. Thus, we utilize a dual-decomposition method to decouple those three sets of variables. By this decoupling, video rate adaptation is performed at users with network assistants. End nodes can schedule computing resource independently. Traffic engineering is performed by the software-defined networking controller and base stations. Furthermore, to address the challenges of dynamic change of network status and the drawbacks caused by the frequent exchange of information, we design a decentralized algorithm based on alternating direction method of multipliers to solve the traffic engineering problem. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

Both caching and interference alignment (IA) are promising techniques for future wireless networks. Nevertheless, most of existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, we propose a novel big data reinforcement learning approach in this paper. Deep reinforcement learning is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function. Deep reinforcement learning is used in this paper to obtain the optimal lA user selection policy in cache-enabled opportunistic lA wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme.

Software-defined networking and mobile edge caching are promising technologies in next generation mobile networks. This paper proposes to jointly optimize bandwidth provisioning and caching strategies in software-defined mobile networks considering the quality of services and limitations of resources. The proposed scheme aims at maximizing gains from dynamically managing caches and efficiently utilizing network resources of the backhaul, spectrum, and cache. Specifically, we model the proposed scheme as a dynamic caching and bandwidth provisioning problem to obtain a sub-optimal solution by using convex optimization. The relaxation method and dual-decomposition method are adopted to solve this problem with low computational complexity. Simulation results are presented to show that the latency is decreased and the utilization of caches is improved in the proposed scheme.

Mobile edge computing (MEC) has risen as a promising technology to augment computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues, we formulate an optimization problem in wireless cellular networks with mobile edge computing, taking into consideration computation offloading decision, physical spectrum resource allocation, MEC computation resource allocation, and content caching strategy. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers (ADMM) based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.

Recent advances in networking, caching and computing have significant impacts on the developments of vehicular networks. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results are presented to show the effectiveness of the proposed scheme.

Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have great effects on video services in next generation mobile networks. In this paper, we jointly consider SDMNs, in- network caching, and MEC to enhance the video service in next generation mobile networks. With the objective of maximizing the mean measurement of video quality, an optimization problem is formulated. Due to the coupling of video data rate, computing resource, and traffic engineering (bandwidth provisioning and paths selection), the problem becomes intractable in practice. Thus, we utilize dual-decomposition method to decouple those three sets of variables. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

Software-defined networking and in-network caching are promising technologies in the next generation wireless networks. In this paper, we propose enhancing the quality of experience (QoE)-aware wireless edge caching with bandwidth provisioning in software-defined wireless networks (SDWNs). Specifically, we design a novel mechanism to jointly provide proactive caching, bandwidth provisioning, and adaptive video streaming. The caches are requested to retrieve data in advance dynamically according to the behaviors of users, the current traffic, and the resource status. Then, we formulate a novel optimization problem regarding the QoE-aware bandwidth provisioning in SDWNs with jointly considering in-network caching strategy. The caching problem is decoupled from the bandwidth provisioning problem by deploying the dual-decomposition method. Additionally, we relax the binary variables to real numbers so that those two problems are formulated as a linear problem and a convex problem, respectively, which can be solved efficiently. Simulation results are presented to show that the latency is decreased and the utilization of caches is improved in the proposed scheme.

Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.

Software-defined networking (SDN) and in-network caching are promising technologies in next generation wireless networks. In this paper, motivated by the flow control in SDN, we propose an approach to solve bandwidth provisioning problem in software-defined mobile networks (SDMNs) with jointly considering in-network cache under uncertain flow rate. We present a flow control problem supporting bandwidth provisioning while providing optimum forwarding strategies and resource allocation. Moreover, due to the centralized control mechanism, the information collected by the SDN controller may not be real- time or accurate. To deal with this uncertainty and fluctuation of flows rate, chance constraints are used to pose bandwidth provisioning. Specifically, with recent advances in robust optimization and approximation techniques, we formulate the flow control problem as a robust optimization problem and transform it to a convex problem, which can be solved efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.

Wireless network virtualization is a promising technology in next generation wireless networks. In this paper, motivated by the experience of user equipment (UE) admission control in traditional wireless networks, we propose a novel concept of mobile virtual network (MVN) admission control for wireless virtualization. By limiting the number of MVNs embedded in the physical network, MVN admission control can effectively guarantee quality of service (QoS) experienced by users of MVNs and maximize the utilization of the physical networks at the same time. Specifically, we propose a two-stage MVN embedding mechanism that can decouple short-term physical resource allocation from long-term admission control and resource leasing. With recent advances in robust optimization, we formulate the MVN admission control problem as a robust optimization problem. Both the long-term admission control and short- term resource allocation problems are transformed to convex problems, which can be solved efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.

Wireless network virtualization and information-centric networking (ICN) are two promising technologies in next generation wireless networks. Traditionally, these two technologies have been addressed separately. In this paper, we show that jointly considering wireless network virtualization and ICN is necessary. Specifically, we propose an information-centric wireless network virtualization framework for enabling wireless network virtualization and ICN. Then, we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering not only the revenue earned by serving end users but also the cost of leasing infrastructure. In addition, with recent advances in distributed convex optimization, we develop an efficient distributed method based on alternating direction method of multipliers (ADMM)-based to solve virtual resource allocation and in-network caching scheme. Simulation results are presented to show the effectiveness of the proposed scheme.

We propose a framework for software defined device-to-device (D2D) communications in virtual wireless networks. With software defined D2D communications, the decisions such as radio resource management are made at a central controller as a piece of software. This work studies the resource sharing problem given imperfect network state information (NSI). We formulate the problem as a discrete stochastic optimization problem maximizing the network-wide sum utility, and develop discrete stochastic approximation (DSA) algorithms to address the stochastic optimization problem. Such algorithms can reduce the computation complexity compared with exhaustive search while achieving satisfactory performance. Extensive simulations show that users' welfare can benefit from both wireless network virtualization and software defined D2D communications.