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.

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

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 the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access system, where the legitimate users send confidential signals to the base station by exploiting STAR-RIS to reconfigure the electromagnetic propagation environment proactively. Depending on the availability of the eavesdropping channel state information (CSI), both the full CSI and statistical CSI of the eavesdropper are considered. For the full eavesdropping CSI scenario, we adopt the adaptive-rate wiretap code scheme with the aim of maximizing minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. To proceed, we propose an alternating hybrid beamforming (AHB) algorithm to jointly optimize the receive beamforming, transmit power, and reflection/transmission coefficients. While for the statistical eavesdropping CSI scenario, the constant-rate wiretap code scheme is employed to minimize the maximum secrecy outage probability (SOP) subject to the quality-of-service requirements of legitimate users. Then, we derive the exact SOP expression under the constant-rate coding strategy and develop an extended AHB algorithm for the joint secrecy beamforming design. Simulation results demonstrate the effectiveness of the proposed scheme. Moreover, some useful guidance about the quantification of phase shift/amplitude and the deployment of STAR-RIS is provided.

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.

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.

In this paper, we investigate secure communication for an untrusted relay network, in which a source equipped with NS antennas communicates to M users with the help of an untrusted relay. To protect the data confidentially while concurrently relying on the untrusted relays, joint antenna and user selection scheme has been proposed with the aid of cooperative jamming. Furthermore, the impacts of both the number of antennas and the number of users on system performance are studied. Both the secrecy outage probability (SOP) and ergodic secrecy rate (ESR) are derived in closed form, and the secrecy diversity order of the considered networks is proved to be min(NS, M/2). Compared with the traditional single antenna and single source-destination scenario, both SOP and ESR can achieve a great improvement owing to the mutual effects of antenna diversity and user diversity. In addition, numerical results are conducted to demonstrate the validity of the proposed scheme.

As a flexible network sharing mechanism, wireless network virtualization (WNV) can significantly improve the resource utilization and reduce the overall expense for the next generation wireless networks. Considering the users' common interests on popular contents, we introduce wireless multicast into wireless virtual networks. To serve the users who have requests on the same content via multicast transmission, both the virtual resources and content can be shared simultaneously. Thus, the virtual resources and backhaul bandwidth can be further released. In this paper, we propose a novel WNV based multicast system. Subsequently, the multicast group forming and sub-channel allocation strategies are jointly investigated in considered frameworks. Then we formulate the strategies as an integer nonlinear programming problem. Furthermore, we propose a heuristic algorithm with the aid of cutting-plane method to tackle the problem. Finally, Simulation results verify that our proposed scheme can significantly enhance the total utility of wireless virtual networks with lower backhaul usage and the proposed heuristic algorithm can achieve a perfect performance.

This paper studies the secure transmission for downlink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system in which imperfect channel state information (CSI) of the eavesdropper links is considered. We propose the novel robust beamforming strategies for the direct transmission NOMA (DT NOMA) and cooperative jamming NOMA (CJ NOMA) with a helper. We formulate our problem as the worst-case sum power minimization subject to secrecy rate constraint. The semidefinite relaxation (SDR) method is firstly applied to relax the quadratic terms and rank-one optimality is proved. Then an iterative algorithm based on successive convex approximation (SCA) is proposed to transform the nonconvex problem into convex approximations. Simulation results show that both the proposed NOMA schemes outperform the orthogonal multiple scheme, and CJ NOMA scheme can achieve much better system performance gain than DT NOMA scheme.

Recently, there are increasing requirements of security in wireless communications, especially for the multiuser systems. In this study, the authors consider the security of two‐hop cooperative multicast networks with an untrusted relay. An energy harvesting destination aided cooperative jamming framework is proposed to protect information from being intercepted by the untrusted relay. For the multiple destinations, a maximum interference jammer selection (MIJS) scheme is proposed to further improve physical layer security. Then the ergodic secrecy rate (ESR) for MIJS scheme is investigated and both the lower bound of ESR and the approximation of ESR are obtained. Finally, the effect of both the energy arrival rate and the number of destination nodes on ESR has been shown via numerical simulations. It can be revealed that as the number of destinations increases, the ESR is determined with two common behaviours regarding the multicast nature and energy arrival rate: (i) multicast nature forces the secrecy rate to decrease with the increasing number of destinations and (ii) the probability that a jammer exists increases with the increasing number of destinations but limited to one.