As a part of Internet of Everything (IoE), satellite communication networks have the problem of the low emitter identification performance due to limited signal samples. To address this issue, this article develops a novel ground-satellite generative learning (GSGL) framework that deploys the SEI model trained at the ground station to the low earth orbit (LEO) satellite for identification. In particular, we exploit the embedded temporal information losslessly by employing gramian angular field (GAF) to preserve the original features of signals, thereby highlighting radio frequency fingerprint (RFF) discriminability among devices. To overcome the limitation of insufficient samples, a hybrid discriminative mechanism driven generative learning is proposed to effectively generate high-fidelity GAF representations, and then the original and generated GAF representations are fed into a residual structure network for extracting RFFs. Considering channel states and generated deviation, we introduce a lightweight combination attention mechanism to reinforce the intra-class cohesion of RFFs by refining the quality of features. Simulation results demonstrate that the proposed framework can enhance inter-class separation and intra-class cohesion of RFFs to obtain excellent identification performance under the scenarios of insufficient samples. Furthermore, the proposed framework can achieve 31.56% accuracy improvement over state-of-the-art methods at 5 signal samples per category.

Currently, Unmanned Aerial Vehicles (UAV) groups can quickly build a multi-hop transmission network, which have been widely utilized in emergency communication scenarios to perform search and rescue, environmental monitoring, personnel positioning, rapid networking, etc. In such emergency rescue situations, strict demands on real-time communication, security, and minimal resource consumption become paramount. Higher requirements for security, bandwidth, and real-time performance necessitate a secure and lightweight data transmission protocol. Additionally, due to the lack of personnel supervision in these scenarios, the probability of malicious nodes increases. Therefore, it is essential to quickly and proximally block malicious nodes’ data to prevent it from affecting subsequent network propagation, and to accurately identify the malicious nodes. To address these issues, in this paper, we propose a traceable, lightweight, and secure data transmission protocol for UAV multi-hop networks in emergency rescue scenarios. The proposed protocol can verify the integrity of data transmitted by a large number of nodes in real time, detect erroneous transmissions, and trace malicious users. Experimental results show that our protocol consistently outperforms the comparison schemes in terms of computational overhead. Moreover, in scenarios involving smaller groups (m=5) and fewer hops (n=4), it exhibits significantly lower communication bandwidth overhead than the reference methods. Security analysis using BAN logic and the formal verification tool Scyther indicates that the proposed scheme meets security requirements. Additionally, comparative analysis results demonstrate that the proposed scheme is highly effective and outperforms other related schemes under the unique constraints of emergency rescue scenarios, where rapid, secure decision-making and data transmission are critical.

ABSTRACT This paper investigates a buffer‐aided cooperative non‐orthogonal multiple access scheme for simultaneous wireless information and power transfer systems, where the near users are equipped with data and energy buffers. To improve the overall energy efficiency, the transmission mode of the considered system adaptively chooses the base station or near‐user transmission mode. Given the target quality of service and limited resources, the long‐term power consumption minimization is formulated by a mixed integer non‐linear programming problem, where the model selection, user scheduling, and power consumption are jointly optimized. To efficiently tackle such a challenging problem, we transform the original problem into an instantaneous non‐convex problem by employing the Lyapunov optimization framework. Then, using the successive convex approximation algorithms, we approximate the instantaneous non‐convex problem as a linear programming problem, where the Karush–Kuhn–Tucker solution of the power allocation can be obtained. The optimality and complexity of the proposed scheme are theoretically analyzed. The simulation results show that the proposed scheme can achieve a near‐optimal performance and significantly improves the energy efficiency compared with the conventional transmission schemes.

This paper designs a helper-assisted offloading strategy in non-orthogonal multiple access enabled mobile edge computing systems, in order to guarantee the quality of service of the energy/delay-sensitive user equipments (UEs). To achieve a tradeoff between the energy consumption and the delay, we introduce a performance metric called energy-delay tradeoff. Aiming at the maximal energy-delay tradeoff minimization, the joint optimization of user association, resource block (RB) assignment, power allocation, task assignment, and computation resource allocation is formulated as a non-convex problem with coupled continuous and 0-1 variables. To tackle this challenging problem, we decompose it as a two-level problem. For the inner-level problem, an iterative parametric convex approximation (IPCA) algorithm is proposed. Then, based on the solution obtained from the inner-level problem, we model the outer-level problem as a four-sided matching problem, and then propose a low-complexity four-sided UE-RB-helper-server matching (FS-URHSM) algorithm. Theoretical analysis demonstrates that the IPCA algorithm can converge to a stationary Karush-Kuhn-Tucker (KKT) point and the FS-URHSM algorithm is guaranteed to converge to a stable matching with polynomial complexity. Simulation results demonstrate the superior performance of proposed algorithms in terms of the energy consumption and the delay.

Abstract With the large‐scale deployment of communication and computation units, the conventional radio access network has been gradually transformed into a sophisticated Cyber‐Physical System. Since the access network is put forward higher expectations for ultra‐reliable and low‐latency services, Short‐Packet Communications (SPC) based on Finite Blocklength Codes can be adopted to further decrease network latency and enhance communication reliability. Considering the interaction among the service provider, the network operator, and users, a three‐stage Stackelberg game is constructed to dynamically arrange communication and computation resources. In this paper, the tripartite interaction problem is modelled as a generalized Nash equilibrium problem, and the reverse induction method is employed to achieve the sub‐game equilibrium. To solve the problem of maximizing tripartite utilities, the authors propose an iterative algorithm for jointly managing resource allocation and tripartite pricing. Simulation results show that compared with the benchmark, the proposed scheme can effectively improve the utilities of users, the service provider, and the network operator and achieve a win–win situation.

Abstract This paper investigates low‐latency offloading strategy in a non‐orthogonal multiple access aided mobile edge computing (NOMA‐MEC) system consisting of K edge servers, one mobile user and one cloud server. An intelligent edge server selection strategy (IESSS) based on Markov decision process (MDP) is proposed to select an edge server, in order to reduce the task completion latency of this system. When an edge server is selected by the proposed IESSS, a joint optimization problem of power allocation and task scheduling factors is formulated to minimize the task completion latency of the hybrid NOMA‐MEC system. To solve the formulated non‐convex optimization problem with coupled variables, a low‐complexity adaptive power‐task resource allocation iterative (APTRAI) algorithm is proposed. Simulation results demonstrate the advantages of the proposed IESSS and verify the convergence and time complexity of the proposed APTRAI algorithm.

Abstract This paper proposes a novel multi‐phase coordinated direct and user‐assisted transmission (MP‐CDUAT) strategy for a non‐orthogonal multiple access system consisting of a base station (BS), K near users (NUs) and a far user (FU). In the first phase, the BS directly serves an opportunistically scheduled NU. For the rest phases, an NU scheduling scheme is developed to jointly select two NUs in each phase, where one receives desired message from the BS and the other one serves as a relay to help FU's receiving. To evaluate the performance of the proposed MP‐CDUAT strategy, the analytical expression of the ergodic capacity (EC) for both NU and FU is derived. With the derived results, the EC scaling of NU and FU are, respectively, derived in the high ρ regime, where the EC scaling of FU increases with the transmission phase, while the EC scaling of NU remains the same. Finally, the numerical and simulation results show that (a) our proposed strategy can improve the performance of FU without affecting the capacity scaling of served NUs; (b) the performance gain achieved by the proposed strategy is more prominent with the increasing number of the transmission phases.

Abstract With the rapid development of Internet‐of‐Things (IoT) and mobile devices, the IoT applications become more computation‐intensive and latency‐sensitive, which bring severe challenges to the resource‐limited devices. Mobile Edge Computing has served as a key promising method to enhance the network's computing capability by enabling resource‐constrained devices to offload tasks to the edge servers. A major challenge, which has been overlooked by most existing works on task offloading, is the dependencies among tasks and subtasks. In this paper, the subtask offloading with logical dependency for IoT applications is focused on. Specifically, subtask dependent graphs are employed to explore the dependency of subtasks and consider the priority of task scheduling. Further, an offloading scheme is put forward for minimizing both task latency and energy consumption of the device with dependency guarantees for all IoT tasks in multi‐server edge networks. Finaly, the simulation results demonstrate that the overall reduction rate is around 14% and relatively stable can effectively reduce task latency in multi‐server edge networks.

This paper investigates a joint resource allocation for ultra-reliable and low-latency radio access networks (URLLRANs) with edge computing. Compared with conventional networks, URLLRANs have more restrictive latency and reliability requirements, and always feature short packet communications. It is a challenging work to provide edge computing services in URLLRANs, since the processing and transmission delay as well as packet loss during computation and communications should all be taken into considerations. Along these lines, to specify the trade-off between latency and reliability, this paper defines computation rates and transmission rates for short packets. Different from the existing work, the proposal takes effective information as well as energy consumption as performance metrics based on the definition. The packet request rates, computation latency, service rates, communication power, blocklength, and transmission information amounts are jointly optimized to reduce energy consumption and meanwhile generate more effective information for both the computation system and the communication system. To solve the NP-hard problem, the locally optimal solution and global optimal solution are both derived. Simulation results validate the performance advantage of the proposal and also indicate that the locally optimal solution can greatly reduce the computation complexity with only a small performance loss when compared with the global optimal solution.

With the increasing popularity of smart services and applications, massive amounts of data generated in the cyber-physical-social smart system (CPS3) have become a valuable commodity. To make data tradable in CPS3 and guarantee the data security, this paper proposes a blockchain-based data market model to enable data trading between the CPS3 operator and CPS3 users. The data trading mechanisms are formulated as two kinds of non-cooperative games, namely, independent optimization problem and competition-enhanced optimization problem. We analyze the game equilibrium of the two problems and validate the existence and uniqueness of the Nash equilibrium. Numerical results demonstrate the performance advantage of the proposed scheme.

This paper develops a multi-helper non-orthogonal multiple access (NOMA)-enabled mobile edge computing (MEC) system, in order to support massive connectivity. To achieve a tradeoff between the energy consumption and delay, we introduce a novel performance metric, called energy-delay tradeoff, which is defined as the weighted sum of energy consumption and delay. The joint optimization of helper clustering, power allocation and task assignment is formulated as a mixed integer nonlinear programming problem with the aim of minimizing the energy-delay tradeoff. The formulated problem with coupled and 0-1 variables belongs to the NP-hard problem, which cannot be directly solved within polynomial time. To efficiently solve such a challenging problem, we first decouple it into a power allocation and task assignment (PATA) subproblem. Then, with the solution obtained from the PATA subproblem, we equivalently reformulate the original problem as a discrete helper clustering (DHC) problem. For the PATA subproblem, a successive convex approximation (SCA)-based algorithm is proposed. Then, based on the solution obtained from the PATA subproblem, we design a low-complexity matching-based clustering (MBC) algorithm to solve the DHC problem. Simulation results are provided to demonstrate the effectiveness of our proposed algorithm in the compromise of energy consumption and delay.

Abstract This study investigates the physical‐layer security of a terrestrial communication system in the presence of potential unmanned aerial vehicle (UAV) eavesdroppers (UEDs). Due to the line‐of‐sight channels of ground‐to‐air links and the broadcast characteristics of wireless channels, the UEDs have a better chance to eavesdrop terrestrial communication systems compared with the conventional ground eavesdroppers. It indicates that the emerging of UEDs brings new challenges to secure transmissions in terrestrial communication systems. In this study, a UAV is recruited to wisely jam the UEDs by adopting a dynamic three‐dimensional (3D) trajectory, in order to protect the legitimate transmission. To maximally improve the average secrecy rate, the joint optimization of the 3D trajectory of the UAV jammer and legitimate user scheduling as a non‐convex mixed‐integer optimization problem is formulated. To efficiently solve such a challenging problem, a novel enhanced genetic algorithm (EGA), where a combined encoding method and a fitness‐based crossover method are developed to improve the convergence performance, is proposed. Simulation results demonstrate the superior performance of the proposed EGA in terms of the average secrecy rate.

Abstract This paper investigates the reliable computation offloading in non‐orthogonal multiple access enabled mobile edge computing networks, where the short‐packet technique is adopted to meet the stringent latency requirements of delay‐sensitive computing services. To characterize the reliability of computation offloading with finite blocklength coding, a novel analytical framework is developed to approximate the average overall block error probability (AOBEP) by using a double‐case linearization. With the derived approximate expression for AOBEP, the joint optimization of computation workload and transmit power is further investigated, in order to minimize the AOBEP under the computation/communication delay constraints. To tackle the non‐convexity of the formulated problem, the formulated problem is first decomposed into the computation workload problem and the transmit power allocation problem. For the computing workload problem, a closed‐form solution is derived. Then, by applying the derived workload allocation, the power allocation is transformed into the convex form through some approximations and solved by the successive convex approximation technique. Finally, simulation results are provided to demonstrate the reliability enhancement of computation offloading and reveal the relationship between the workload/power allocation and the communication/computation delay.

We study the energy efficiency problem for a fog computing system with multiple users and fog nodes. In order to handle various kinds of tasks at the same time, a mixed-task paradigm is proposed to combine both binary offloading and partial offloading. Then, a mixed-task resource allocation problem with the consideration of computation and communication resources is formulated as a mixed integer nonlinear programming problem (MINLP), which cannot be handled by the traditional relaxation algorithm due to the joint design of binary and partial offloading. To solve this problem efficiently, we first adopt a replacement-based method to transform the problem, and then design an augmented Lagrange method (ALM)-based resource allocation scheme. To further accelerate the solution procedure, a novel optimization technique, AMSGrad, is applied to the designed scheme. The performance of the proposed scheme is demonstrated by simulation results.

In this paper, we tackle the joint computation and communication resource allocation problem for mixed-task user-concerned fog computing. To deal with diverse kinds of computing tasks, we propose a mixed-task paradigm to support the co-existence of binary offloading and partial offloading. Considering the impact of users' satisfaction on fog computing service, we employ the user-weighted energy efficiency (UWEE) as the objective of resource allocation and develop a user-concerned mechanism (UCM) to sketch users' social features. Then, under the constraints of users' satisfaction, the resource allocation problem for UWEE maximization is formulated as a mixed integer nonlinear programming problem (MINLP), which cannot be properly solved by the traditional relaxation algorithms due to the co-existence of binary offloading and partial offloading. After transforming the problem with the replacement-based method, an augmented Lagrange method (ALM)-based resource allocation scheme is proposed to iteratively solve this joint optimization problem, in which AMSGrad is employed to accelerate the convergence. Simulation results demonstrate the superior performance of the ALM-based resource allocation scheme in terms of UWEE.

To promote future intelligent systems, a novel cyber-physical-social smart system (CPS3) powered by the Internet of Things is presented in this paper, where wireless network virtualization is adopted to enhance the diversity and the flexibility of the service operation and the system management. Based on the presented system, a robust energy-efficient resource allocation scheme is proposed to guarantee the outage probability requirements of controllers and actuators while realizing the maximization of the system energy efficiency. Different from the existing works, imperfect channel state information is studied for energy-efficient resource allocation in CPS3. To effectively handle the formulated optimization problem, the concept of virtual devices is introduced to equivalently reformulate the original problem. Afterward, the probabilistic mixed problem is approximately transformed into a nonprobabilistic problem though outage probability analyses. After the transformation, the optimization problem can be decomposed into power allocation and channel allocation, where an iterative algorithm for power allocation is adopted to maximize the system energy, and a heuristic greedy algorithm is presented to schedule sensors and actuators on different subchannels based on the obtained power allocation results. Simulation results demonstrate the convergency of the proposed algorithm and the advantages of the proposed scheme.

In this study, the authors focus on the downlink resource allocation to optimise energy‐efficiency (EE) of orthogonal frequency division multiplexing‐based cognitive heterogeneous network (HetNet), where network service providers operate multi‐radio access technologies. A more practical case is considered, in which imperfect channel state information (CSI) is available for wireless channels between secondary base stations and secondary users (SUs), primary users (PUs). Since imperfect CSI may result in outage in secondary networks and make collision occur in primary networks, a joint subcarrier and power allocation scheme for the cognitive HetNet is proposed to guarantee quality of service requirements for both SUs and PUs in probabilistic manners. Then the probabilistic EE optimisation scheme is approximated into a deterministic convex form, and the optimal solutions for the scheme are derived by double‐loop iteration method. Afterwards, the authors develop a low‐complexity algorithm in the presence of imperfect CSI with a small reduction of system performance. Simulation results are provided to show the impact of channel estimation error on the EE optimisation problem and to highlight the performance improvement from considering channel estimation error in the joint subcarrier and power allocation scheme for the cognitive HetNet system.