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.

This paper investigates covert communication in a hybrid-functional (HF) reconfigurable intelligent surface (RIS) assisted symbiotic radio (SR) system, where each element of RIS can adaptively switch between passive reflection and covert signal modulation mode. Since covert communication fundamentally relies on low transmission power to ensure undetectability, we try to minimize the transmit power while ensuring the covert constraint, and the joint optimization of active beamforming (BF) at access point, passive BF and mode selection at HF-RIS can be formulated as a mixed-integer fractional programming (MIFP) problem. By decomposing the original problem into the continuous beamformer design and the discrete mode selection problems, we further develop a binary search-assisted alternative beamformer optimization algorithm to obtain a stable solution of the MIFP problem. Numerical results demonstrate the superiority of the adaptive RIS mode selection covert-enhanced design over existing benchmark schemes and reveal useful insights to guide RIS mode selection design for different SR paradigms.

This paper considers the cost minimization problem in a heterogeneous mobile edge computing network, where the macro base stations are scattered over the ground to ensure the service coverage, and the small base stations are clustered around the user hotspots to improve the users’ quality of service. Specifically, the network-wide communication and computation performance are analyzed using stochastic geometry and queueing theory. Taking the obtained performance metrics as constraints, a cost minimization problem is formulated. To solve the formulated problem, we propose a globally optimal algorithm, whose optimality and complexity are theoretically analyzed. Then, to further reduce the computational complexity, the problem is reformulated as the difference of convex programming problems, which can be addressed by the concave-convex procedure. Numerical simulations verify the accuracy of the theoretical results and illustrate the superiority of the proposed algorithms.

This paper investigates covert communications in an integrated sensing and communications system, where a dual-functional base station (called Alice) covertly transmits signals to a covert user (called Bob) while sensing multiple targets, with one of them acting as a potential watcher (called Willie) and maliciously eavesdropping on legitimate communications. To shelter the covert communications, Alice transmits additional dual-functional artificial noise (DFAN) with a varying power not only to create uncertainty at Willie’s signal reception to confuse Willie but also to sense the targets simultaneously. Based on this framework, the weighted sum of the sensing beampattern means square error (MSE) and cross correlation is minimized by jointly optimizing the covert communications and DFAN signals subject to the minimum covert rate requirement. The robust design considers both cases of imperfect Willie’s CSI (WCSI) and statistical WCSI. Under the worst-case assumption that Willie can adaptively adjust the detection threshold to achieve the best detection performance, the minimum detection error probability (DEP) at Willie is analytically derived in the closed-form expression. The formulated covertness constrained optimization problems are tackled by a feasibility-checking based difference-of-convex relaxation (DC) algorithm utilizing the S-procedure, Bernstein-type inequality, and the DC method. Simulation results validate the feasibility of the proposed scheme and demonstrate the covertness performance gains achieved by our proposed design over various benchmarks.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted wireless powered mobile edge computing (MEC) system, where an access point (AP) integrated with an MEC server first transmits energy signals to charge multiple energy-constrained devices in the downlink (DL), and then the devices utilize the harvested energy to perform MEC offloading in the uplink (UL) assisted by an RIS. Specifically, three multiple access protocols for MEC offloading, namely pure non-orthogonal multiple access (NOMA), pure orthogonal multiple access, and hybrid NOMA, are proposed to exploit dynamic RIS beamforming and energy recycling among devices to enhance the efficiencies of energy harvesting and MEC offloading. For each of the three protocols, a joint resource allocation framework of the AP/devices transmit power, the RIS phase shifts, and the DL/UL time allocation is formulated to minimize the energy consumption at the AP. Because of those highly coupled optimization variables, the formulated optimization problems are first shown to be non-convex, and then the intrinsic structure of the problems is exploited to devise computationally-efficient algorithms and solve them iteratively. Numverical results are provided to demonstrate the performance improvement of our proposed designs compared to various benchmark schemes, and reveal the practical significance of the considered multiple access protocols with dynamic RIS beamforming and energy recycling for spectral and energy efficient MEC offloading.

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled non-orthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, a beam-steering scheme is proposed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. Numerical results validate that the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme.

A novel near-field transmission framework is proposed for dynamic metasurface antenna (DMA)-enabled nonorthogonal multiple access (NOMA) networks. The base station (BS) exploits the hybrid beamforming to communicate with multiple near users (NUs) and far users (FUs) using the NOMA principle. Based on this framework, two novel beamforming schemes are proposed. 1) For the case of the grouped users distributed in the same direction, a beam-steering scheme is developed. The metric of beam pattern error (BPE) is introduced for the characterization of the gap between the hybrid beamformers and the desired ideal beamformers, where a two-layer algorithm is proposed to minimize BPE by optimizing hybrid beamformers. Then, the optimal power allocation strategy is obtained to maximize the sum achievable rate of the network. 2) For the case of users randomly distributed, a beam-splitting scheme is proposed, where two subbeamformers are extracted from the single beamformer to serve different users in the same group. An alternating optimization (AO) algorithm is proposed for hybrid beamformer optimization, and the optimal power allocation is also derived. Numerical results validate that: 1) the proposed beamforming schemes exhibit superior performance compared with the existing imperfect-resolution-based beamforming scheme and 2) the communication rate of the proposed transmission framework is sensitive to the imperfect distance knowledge of NUs but not to that of FUs.

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging the energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing the analog beamformer, the baseband digital information/energy beamformers, and the number of dedicated energy beams. To tackle the uncertain number of dedicated energy beams, a semidefinite relaxation-based rank-one solution construction method is proposed to obtain the optimal baseband digital beamformers under the fixed analog precoder. Based on the structure of the optimal baseband digital beamformers, it is proved that no dedicated energy beam is required in the near-field SWIPT. To further exploit this insight, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer, and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, to reduce the high computational complexity caused by the large number of antennas, a low-complexity two-stage algorithm is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve near-optimal performance and 2) in contrast to the far-field SWIPT, a single near-field beamformer can focus the energy on multiple locations.

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.

This paper investigates a mobile edge computing (MEC) network assisted by an unmanned aerial vehicle (UAV), where the moving UAV and a fixed ground base station cooperatively provide MEC services for multiple ground users. To evaluate the quality of service under this architecture, we first derive the successful edge computing probability (SECP) to evaluate the service reliability. Given a target SECP requirement, we formulate a service coverage maximization problem by optimizing the UAV altitude and task offloading probability. The problem is hard to solve due to the coupled UAV altitude and task offloading probability in the derived SECP expression. To address this challenge, we first explore some interesting properties of the formulated problem, and then use these properties to develop a golden-section search based method to solve the formulated optimization problem. Numerical results are used to verify the theoretical analysis of our system and demonstrate the efficiency of the proposed scheme.

This paper investigates a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. Three efficient operating protocols for the S-IOS, namely time switching, power splitting, and mode switching, are proposed to enable the dual-functionality of energy harvesting and information transmission. For each protocol, we design a joint optimization framework of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the network sum rate. Despite the challenging non-convex optimization problems with highly coupled and/or integer optimization variables, we develop computationally-efficient algorithms to solve them in an iterative manner, which exploit the intrinsic structure of the problems and employ the penalty-based method and the successive convex approximation. Numerical results confirm the efficiency of our developed optimization algorithms, demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications, and quantify the performance advantage of the proposed designs over the baseline schemes.

We investigate joint power allocation and decoding order selection (PA-DOS) aimed at enhancing the outage performance of non-orthogonal multiple access (NOMA) systems. By considering the diverse target rates of users, new important properties of NOMA are revealed: When users’ target rates satisfy certain conditions, the channel state information (CSI) is not required by PA-DOS to minimize the system outage probability, and different users’ outage probabilities can be minimized simultaneously; When such conditions are not satisfied, the opposite situation occurs. Following these properties, two PA-DOS strategies are designed regarding distinct user priorities, which ensure the minimization of the system outage probability and the user outage probability of the high-priority user. Especially, these strategies do not require CSI or only require one-bit CSI feedback depending on users’ target rates. Analytical and numerical results are provided to demonstrate that the proposed strategies significantly outperform the existing strategies in terms of both system and user outage performance. Furthermore, the results show that although under some target rates the minimum user outage probabilities cannot be simultaneously achieved, they can be closely approached at the same time in the high-signal-to-noise-ratio regime by the proposed strategies.

A simultaneously transmitting and reflecting surface (STARS) enabled two-phase integrated sensing and communications (ISAC) framework is proposed, where a novel bi-directional sensing-STARS architecture is devised to facilitate the full-space communication and sensing in a time-switching manner. Based on the proposed framework, a joint optimization problem is formulated, where the Cram$\acute {\text {e}}\text{r}$-Rao bound (CRB) for estimating the 2-dimension direction-of-arrival of the sensing target is minimized. Two cases are considered for sensing performance enhancement. 1) For the two-user case with the fixed number of sensors, an alternating optimization algorithm is proposed. In particular, the maximum number of deployable sensors is obtained in the closed-form expressions, where the maximum number of sensors is revealed to be only relevant to the QoS requirements of communications. 2) For the multi-user case with the variable number of sensors, an extended CRB (ECRB) metric is proposed to characterize the impact of the number of sensors on the sensing performance. A generic decoupling approach is proposed to convexify the non-convex ECRB expression. Based on this, a novel penalty-based double-loop (PDL) algorithm is proposed. Simulation results reveal that 1) the proposed PDL algorithm achieves a near-optimal performance with consideration of sensor deployment; 2) it is preferable to deploy more passive elements than sensors in terms of achieving optimal sensing performance.

A novel dynamic hybrid beamforming architecture is proposed to achieve the spatial multiplexing-power consumption tradeoff for near-field multiple-input multiple-output (MIMO) networks, where a switch module is integrated between the baseband digital and analog phase-shift module to control the number of activated RF chains. Based on this architecture, an optimization problem is formulated that maximizes the sum of achievable rates while minimizing the hardware power consumption. Both continuous and discrete phase shifters are considered. 1) For continuous phase shifters, a wavenumber-domain weighted minimum mean-square error (WD-WMMSE) algorithm is proposed, which exploits the sparsity of WD near-field channels to achieve the low-dimensional beamformer design. 2) For discrete phase shifters, a penalty-based layered iterative (PLI) algorithm is proposed. The closed-form analog and baseband digital beamformers are derived in each iteration. Simulation results demonstrate that: 1) the proposed dynamic beamforming architecture outperforms the conventional fixed hybrid beamforming architecture in terms of spatial multiplexing-power consumption tradeoff, and 2) the proposed algorithms achieve better performance than the other baseline schemes.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks due to its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The proposed scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. We formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based methods are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner but also it improves the delay performance and maintains the delay stability.

We investigate a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. We design a joint optimization problem of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the sum rate of the overall network. A computationally-efficient algorithm is developed to solve the problem. Numerical results demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications.

We investigate a reconfigurable intelligent surface (RIS)-aided wireless powered mobile edge computing (MEC) network, where a new RIS-aided hybrid non-orthogonal multiple access (NOMA) offloading scheme is proposed to improve the efficiency of both MEC offloading and energy harvesting. A joint optimization framework for the transmit power, time allocation, and RIS phase shifts is developed to minimize the overall energy consumption of the network, subject to the energy causality and MEC offloading constraints. Despite of the optimization problem's non-convexity with highly coupled optimization variables, we devise a computationally-efficient algorithm to solve it iteratively. Simulation results are also provided to facilitate the performance evaluation of the RIS-aided wireless powered MEC, quantify the value of the RIS for energy efficient MEC offloading, and validate the performance gains of the proposed hybrid NOMA offloading over various baseline schemes.

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.

This paper investigates computing task offloading from an Internet-of-Thing (IoT) device with limited transmit power to a mobile-edge computing (MEC) server located beyond the communication range of the IoT device. We propose an opportunistic cooperative offloading (OCO) strategy that recruits the IoT device’s nearby spatially random idle-state users as helpers and opportunistically schedules one of them to partially execute the latency-critical task, and forwards the rest portion of the task to the MEC server. For the OCO strategy, we investigate the helper scheduling under three cases of system information availability, i.e., the global, partial, and distance information cases, and develop an offloading-outage optimal scheduling scheme for each case. For each scheduling scheme, an approximate expression is derived for the offloading-outage probability, with which the achieved diversity order is also theoretically evaluated. Simulation results verify our performance analysis for the OCO strategy using helper scheduling and show its achieved offloading-outage/energy consumption reduction over multi-helper cooperative offloading that fully uses all helpers for cooperation. In addition, the advantages of the proposed helper scheduling schemes over existing scheduling schemes are also demonstrated by simulations.

In this paper, we propose a new simultaneous information relaying and jamming (SIRJ) scheme based on a dual-functional reconfigurable intelligent surface (RIS) to achieve secure non-orthogonal multiple access communications. Specifically, the RIS elements are split into two groups, where elements in one group perform signal reflection to enhance the legitimate reception quality while elements in the other group generate artificial jamming to interfere with the eavesdropper (Eve). Based on different channel state information (CSI) availabilities of Eve, the system sum-rate is maximized by jointly optimizing the transmit beamforming of the base station, reflect beamforming of the RIS, and mode selection of each RIS element, subject to a maximum tolerable information leakage to Eve. For the case with perfect Eve’s CSI, a penalty based alternating algorithm is proposed to deal with the challenging multivariate coupled and mixed integer non-convex optimization problem. For the case with imperfect Eve’s CSI, we consider the infinite number of secrecy constraints, for which the traditional$\mathcal {S}$-procedure cannot be directly applied. To tackle this challenge, we devise an efficient transformation that fits the$\mathcal {S}$-procedure to the problem and propose a robust secure beamforming design. Simulation results demonstrate the performance advantage of the proposed SIRJ scheme over the existing baseline schemes.

To enhance the outage performance of downlink non-orthogonal multiple access (NOMA) systems, we investigate opportunistic user pair scheduling (UPS) as well as the joint power allocation and decoding order selection (PA-DOS) for the scheduled user pair. Unlike most existing literature where the UPS and PA-DOS schemes are solely driven by channel conditions, we consider the impact of both the channel conditions and users’ target rates in the scheme design, since these two factors jointly determine the occurrence of transmission outages. Specifically, a two-stage rate-aware scheme for joint UPS and PA-DOS is proposed, which is proved to achieve the minimum system outage probability and guarantee fair access opportunities among users. For performance evaluation, the exact system outage probability achieved by the proposed scheme is derived in closed form, based on which a high-signal-to-noise-ratio asymptotic result is further derived for meaningful insights. Our analytical results reveal a new finding: The diversity order achieved by our proposed scheme, which is also the maximum achievable diversity order of the considered NOMA system, varies with the users’ target rates. Simulations confirm our findings and demonstrate that the proposed scheme can achieve a significant outage performance gain over the existing benchmark schemes.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

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 security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a base station (BS) serves users securely with the assistance of distributed IRSs. Considering that eavesdropper’s instantaneous channel state information (CSI) is challenging to acquire in practice, we utilize secrecy outage probability (SOP) as the security metric. A problem of maximizing the minimum secrecy rate among users, by jointly optimizing transmit beamforming at the BS and phase shifts of the IRSs, is formulated. For a special case with a single-antenna BS, we derive the closed-form SOP expressions and propose a novelring-penaltybased successive convex approximation (SCA) algorithm to design transmit power and phase shifts jointly. For a general multi-antenna BS case, we develop a Bernstein-type inequality based alternating optimization (AO) algorithm to solve the challenging problem. Numerical results demonstrate the advantages of the proposed algorithms over the baseline schemes. The results also show that: 1) the maximum secrecy rate is achieved when distributed IRSs share the reflecting elements equally; and 2) the distributed IRS deployment does not always outperform the centralized IRS deployment, due to the tradeoff between the number of IRSs and the reflecting elements equipped at each IRS.

We investigate covert wireless communication in a multi-antenna relay network, where the relay transmits its own covert message to the destination when assisting the source’s information delivery, and the source acts as a warden to detect this covert transmission. Based on whether the channel state information of the relay-destination link is available at the source or not, we propose two relay beamforming schemes, namely random beamforming and maximum-ratio transmission (MRT) beamforming schemes, to guarantee the reception reliability at the destination while deliberately introducing uncertainty to the source to degrade its detection. Under the worst-case covert communication scenario where the source is capable of optimizing its detection threshold, analytical expressions for the minimum detection error probability achieved by each of the proposed schemes are derived to evaluate the detection limits of the source. By utilizing the above analytical results as the covertness constraint, an optimization problem of transmit power allocation for each scheme is formulated and solved to maximize the covert rate. The impact of imperfect channel state information on the covert communication performance is also examined. Simulation results are performed to confirm the accuracy of the derived analytical results and quantify the communication covertness enhancement of the proposed schemes. Our results also show that the MRT beamforming scheme offers a higher covert rate than that of the random beamforming scheme, especially when the covertness constraint becomes loose and/or the number of antennas at the relay increases.

In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, we propose new IRS-assisted downlink and uplink NOMA schemes to hide the existence of Bob’s covert transmission from a warden (Willie), which cost-effectively exploits the phase-shift uncertainty of the IRS and the non-orthogonal signal transmission of Roy as the cover medium without requiring additional uncertainty sources. Assuming the worst-case covert communication scenario where Willie can optimally adjust the detection threshold for his detector, we derive an analytical expression for the minimum average detection error probability of Willie achieved by each of the proposed schemes. To further enhance the covert communication performance, we propose to maximize the covert rates of Bob by jointly optimizing the transmit power and the IRS reflect beamforming, subject to given requirements on the covertness against Willie and the quality-of-service at Roy. Simulation results demonstrate the covertness advantage of the proposed schemes and confirm the accuracy of the derived analytical results. Interestingly, it is found that covert communication is impossible without using IRS or NOMA for the considered setup while the proposed schemes can always guarantee positive covert rates.

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered to be a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large transmission delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

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.

High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing to improve intelligent transportation systems (ITS). HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities, which is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

This paper investigates the security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a distributed IRS enabled NOMA transmission framework is proposed to serve users securely in the presence of a passive eavesdropper. Considering that the instantaneous channel state information (CSI) of the eavesdropper is challenging to acquire in practice, we utilize the secrecy outage probability (SOP) as the security metric. A problem by jointly optimizing the transmit power at the base station (BS) and reflection phase shifts at IRSs, subject to the successive interference cancellation (SIC) decoding constraints and SOP constraints, is formulated to maximize the minimum secrecy rate among legitimate users. To tackle the non-convex problem, we first derive the exact SOP in closed-form expressions and then propose a novel ring-penalty based successive convex approximation (SCA) algorithm to design power allocation and phase shifts jointly. Numerical results validate the convergence and the secrecy superiority of proposed scheme over the baseline schemes.

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.

To cope with the unprecedented surge in demand for data computing for the applications, the promising concept of multiaccess edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for mobile devices (MDs). Since enormous workloads need to be migrated, and MDs always remain resource-constrained, data offloading from devices to the MEC server will inevitably require more efficient transmission designs. The integration of nonorthogonal multiple access (NOMA) technique with MEC has been shown to provide applications with lower latency and higher energy efficiency. However, the existing designs of this type have mainly focused on the transmission technique, which is still insufficient. To further advance offloading performance, in this work, we propose an application-driven NOMA-enabled computation offloading scheme by exploring the characteristics of applications, where the common data of the application is offloaded through multidevice cooperation. Under the premise of successfully offloading the common data, we formulate the problem as the maximization of individual offloading throughput, where the time allocation and power control are jointly optimized. By using the successive convex approximation (SCA) method, the formulated problem can be iteratively solved. Simulation results demonstrate the convergence of our method and the effectiveness of the proposed scheme.

Multihypothesis-based compressed video sensing scheme attracts wide attention in the research of resource-constrained video application scenarios. However, high-accuracy weight prediction of hypotheses is always challenging especially for the high-motion sequences. To solve this problem, this paper proposes a novel multihypothesis-based distributed compressed video sensing (NMH-DCVS) system. The new multihypothesis system contains two components: hypotheses acquisition, and weight prediction. First, to acquire more high-quality hypotheses, a new hypotheses acquisition scheme is proposed by constructing the search window based on the temporal, and spatial correlation of the image blocks, respectively. The optimal matching block can be quickly determined. Second, to improve the accuracy of the multihypothesis weight prediction, a new residual transforming preprocessing-based weight prediction algorithm is proposed by transforming the original hypothesis set to residual hypothesis set. The influence of the quality fluctuation of the hypotheses on prediction accuracy is effectively suppressed. Moreover, the improved hypotheses further improve the sparsity of the residual hypothesis set, leading to the additional improvement of the accuracy of the proposed residual-based weight prediction algorithm. Experiment results show that compared with the state-of-the-art methods reported in the literature, the proposed new multihypothesis system significantly improves the decoding performance both in objective, and subjective quality.

In this paper, we propose an innovative framework to improve the physical layer security of cognitive radio nonorthogonal multiple access (CR-NOMA) networks. Specifically, we consider a cooperative spectrum-sharing mechanism, where a cognitive transmitter serves as a relay and assists primary/cognitive transmissions using the NOMA principle in the presence of a passive eavesdropper. Technically, we propose a new cooperative jamming scheme, where the primary/cognitive receivers and the primary transmitter are recruited as jammers to transmit artificial noise in order to intentionally confuse eavesdropper. Under a practical assumption that only the statistical channel state information of eavesdropper is available, the secrecy outage probability (SOP) is used as the performance metric. An optimization problem of maximizing the minimum confidential information rate among the primary and cognitive receivers is formulated. We devise a successive convex approximation based power allocation algorithm to efficiently solve the non-convex optimization problem. Our simulation results indicate that the proposed scheme outperforms the orthogonal multiple access based schemes in terms of minimum confidential information rate.

For multihypothesis-based compressed video sensing schemes, the low accuracy of weight prediction and degradation of recovery quality for high-motion videos are open challenges. To solve this problem, this paper proposes a new multihypothesis prediction scheme. To efficiently get high-quality hypotheses, a new hypotheses acquiring method is proposed by building the search window based on the temporal and spatial correlation. To improve the accuracy of weight prediction, a residual transforming preprocessing for weight prediction is proposed. By converting the original hypotheses to residual hypotheses, the influence of quality fluctuation of hypotheses on the recovery quality is suppressed effectively. The sparsity and accuracy of the prediction model are improved efficiently. Simulation results show that a significant improvement in recovery quality is obtained in the proposed scheme compared to the state-of-the-art systems.

In this paper, we investigate a NOMA assisted multi-antenna two-way relay system, where users apply NOMA to support bidirectional superposition transmission with the aid of multiple relays. Specifically, we propose a multiple-access broadcast NOMA strategy together with a joint antenna-and-relay selection scheme to enhance its transmission reliability. Analytical closed-form expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed strategy with the joint antenna-and-relay selection scheme. Based on the analytical result, we further optimize the power allocation to reduce the outage probability. Our numerical results show that the proposed mechanism significantly outperforms existing benchmark strategies in terms of the outage probability.

In order to alleviate network congestion and reduce request delay, device‐to‐device caching technology will be an important part of modern communication networks. People always browse the Internet for things they are interested in. However, different people have different points of interest. Therefore, how to choose a suitable cache node when people share content is a challenge. In this study, an optimal cache node selection algorithm based on virtual delay is proposed, where the multi‐armed bandit model is used to obtain optimised cache decision based on the interest differences of users. Each candidate user may become a cache node, and the algorithm selects the user who minimises the overall delay as the node. When multiple candidate users exist, it is necessary to cooperate between multiple candidate users whose cache space is limited in order to maximise the cache hit rate. However, since each candidate user acts as a cache node has a defferent service efficiency for different requests from surrounding users, it is necessary to effectively distinguish cooperative cache users. This study proposes a master–slave node cooperative cache model based on the optimal cache node selection. The experimental results confirm that the proposed schemes achieve lower overall delay performance.

The image denoising model based on non‐local self‐similarity prior (NSS) has received extensive attention in recent years because of the repeated structure of natural image patches. Similar patches collected by exploiting NSS prior are sparse, which can be used to estimate potential lowrank subspace. Meanwhile, the modelling of natural images, such as Gaussian mixture models (GMMs), has been successful in all aspects of computer vision by reducing the patterns of image patches. However, the version of its geometric transformation (e.g. rotational transformation) cannot be matched directly by using distance. How to further reduce the patterns of the patches by geometric prior and accelerate rotational matching through parallel calculation is an issue that needs to be solved. In this study, an external guided rotational matching denoising framework is proposed. The proposed framework combines non‐local, sparse and low‐rank image priors and we design a parallel computing scheme. They demonstrate the performance improvement of the proposed algorithm on images with strong rotational properties and the comparison with traditional state‐of‐the‐art denoising methods. The scalability and effectiveness of the new framework are verified by simulation experiments in public and real datasets.

We investigate a secrecy-enhancing design for cooperative downlink and uplink non-orthogonal multiple access (NOMA) transmissions with an untrusted relay. A source uses the NOMA principle to have downlink and uplink transmissions with a near user and a far user, and the communications between the source and the far user are aided by an untrusted relay. To minimize information leakage at the untrusted relay and achieve secure NOMA communications, adaptive downlink and uplink cooperative jamming schemes are proposed, where the far user in downlink and the near user in uplink adaptively emit a jamming signal to the untrusted relay to impair its eavesdropping capability. Both downlink and uplink jamming power are optimized to fully exploit the benefits of the proposed schemes for security enhancement. Then, for each scheme, we quantify the secrecy performance by deriving the ergodic secrecy sum rate lower bound and its scaling law. Finally, computer simulation is used to demonstrate the effectiveness of the proposed adaptive downlink and uplink cooperative jamming schemes and verify the accuracy of the derived analytical results.

In this paper, we investigate a non-orthogonal multiple access (NOMA) assisted multi-antenna two-way relay system, where multi-antenna users apply NOMA to support bidirectional superposition transmission via multiple multi-antenna relays. Specifically, we propose two cooperative strategies, namely multiple-access broadcast NOMA and time division broadcast NOMA. For each of the two cooperative strategies, we devise a joint antenna-and-relay selection scheme to enhance the transmission reliability. Analytical expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed cooperative strategies with the corresponding joint antenna-and-relay selection schemes. To further reduce the outage probability, we use the derived analytical results as objective functions to optimize the transmit power allocation under both cooperative strategies. Finally, extensive simulations are carried out to validate the accuracy of the derived analytical results. Our simulation results indicate that the proposed strategies significantly outperform existing benchmark strategies in terms of outage probability and diversity order.

This paper designs a novel opportunistic adaptive non-orthogonal multiple access (OA-NOMA) strategy, where a base station (BS) employs NOMA to serve a near user (NU)-far user (FU) pair opportunistically scheduled from M NUs and K FUs. In particular, the NOMA transmission to the scheduled NU-FU pair adaptively operates in one of two modes: Direct NOMA mode, in which the BS directly serves the scheduled NU-FU pair with using NOMA; Cooperative NOMA mode, in which the scheduled NU receives the messages intended by both scheduled users from the BS, and then forwards the message intended by the scheduled FU. For the OA-NOMA strategy, a scheduling candidate acquisition method and a probabilistic user pair scheduling scheme are proposed to guarantee the transmission reliability and improve the scheduling fairness, respectively. To evaluate the scheduling fairness, we develop a max-min fairness criterion and show that the OA-NOMA strategy approximately achieves max-min fairness. The reliability of the OA-NOMA strategy is also evaluated in terms of outage probability and diversity order. For the outage probability, we derive an approximate expression and numerically verify its tightness. For the diversity order, we show that the proposed OA-NOMA strategy achieves a diversity order of M.

We study secure communications with an untrusted relay for a cooperative non-orthogonal multiple access (NOMA) system, where a base station (BS) serves a near user (NU) and a far user (FU) by the NOMA principle, and transmission between BS and FU is aided by an untrusted relay. We propose an adaptive jamming scheme to enhance transmission security, in which FU is asked to adaptively emit a jamming signal to confuse the untrusted relay. The transmission rate for jamming is designed to ensure that only NU can decode the jamming signal correctly, and the jamming power is optimized for maximizing the secrecy sum rate. We analyze the security performance of the proposed scheme and derive the ergodic secrecy sum rate and its scaling law. Simulation results are presented to validate the effectiveness of the proposed adaptive jamming scheme.

It is challenging for resource-constrained wireless terminals to realize the high-efficiency video upload. This paper proposes a compressed sensing based high-efficiency video upload (CS-HEVU) system for the terminal-to-cloud network. The main contributions are as follows. First, to develop higher transmission efficiency, a new encoder sampling scheme is proposed by utilizing skip block based residual compressed sensing sampling. The redundant image blocks are effectively removed in encoding. Second, to improve the decoding quality, a local secondary reconstruction based multi-reference frames cross recovery algorithm is proposed in decoder. The user experience is improved by reducing the quality fluctuation of non-key frames. Compared with the state-of-the-art systems reported in literature, simulation results show that a significant improvement in encoding efficiency and decoding performance is obtained in the proposed scheme.

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.

Spectrum harvesting is needed for large-scale wireless networks to access underutilized spectrum and support multiple heterogeneous users. Cooperative spectrum harvesting (CSH) allows for improved co-channel existence and intra-/inter-cell interference mitigation, which dramatically improves spectral efficiency. However, good performance metrics to quantify CSH schemes are not available. For example, existing metrics such as data rate, error/outage probability, and multiplexing/diversity gains may not clearly distinguish large signal-to-interference-plus-noise ratio (SINR) scenarios and sum-rate performance for multiple links. To overcome these limitations, we propose two new metrics called spectrum access level (SAL) and user participation level (UPL). The advantages of these metrics are: 1) achieving distinct upper bounds for multiple links; 2) upper bounds being evaluated directly by basic CSH system model; and 3) determining the performance at any power level of CSH schemes even if they are not interference exempt. Moreover, a novel CSH system model is conceived to achieve satisfying spectrum access ability based on SAL and UPL, and an interference-exempt scheme is designed to achieve relevant upper bounds. Numerical results verify the efficiency of SAL and UPL, and the spectrum access ability of proposed system model with interference-exempt scheme.

This paper proposes a novel design of cooperative non-orthogonal layered multicast multiple access in a heterogeneous network, where the information is encoded into the messages of high priority (HP) and low priority (LP). Two types of multicast users coexist in the network: 1) regular users (RUs), which are located far away from the base station (BS) and expect to decode only the HP message (due to the weak channels), and 2) advanced users (AUs), which are located close to the BS and expect to decode both HP and LP messages. To improve the reliability of layered multicast, we consider that the successful AUs (those AUs who successfully decode the HP and LP messages) serve as potential relays to assist other AUs/RUs. Based on this idea, two novel cooperation strategies are proposed for different cases of channel information availability. For each proposed strategy, we derive closed-form exact outage probabilities of AUs and RUs and then further analyze their diversity orders. Moreover, considering that the layered multicast is outage-constrained, we theoretically evaluate the energy consumption of both strategies and demonstrate their energy saving gains over the direct non-orthogonal multiple access for layered multicast. Finally, our theoretical analysis is verified by numerical results, and the advantages of the proposed strategies are also demonstrated.

We propose a novel non-orthogonal multiple access (NOMA)-inspired jamming and relaying scheme to enhance the physical layer security of untrusted relay networks. Particularly, during the first phase, with the application of the downlink NOMA principle, the source sends a superimposed version of a desired signal and a jamming signal, where the jamming signal is designed to deliberately confuse the untrusted relay by exploiting the beamforming design and adapting the transmission rate at the source. During the second phase, relying on the uplink NOMA principle, the untrusted relay forwards its received signals and, simultaneously, the source transmits a new desired signal that cannot be wiretapped by the untrusted relay to maximize the secrecy sum rate. Scenarios of single-antenna and multiple-antenna relaying are considered, and the impact of different antenna configurations at the source and the untrusted relay on the secrecy performance is investigated. Analytical expressions of an ergodic secrecy sum rate (ESSR) lower bound and an asymptotic ESSR scaling law are derived to evaluate the secrecy performance of the proposed NOMA-inspired jamming and relaying scheme. Computer simulations are presented to validate the accuracy of the derived analytical results, and demonstrate the significant ESSR improvement of the proposed scheme over the conventional orthogonal multiple access-based relaying schemes.

With the rapid popularization of mobile intelligent terminals, mobile video and cloud services applications are widely used in people's lives. However, the resource-constrained characteristic of the terminals and the enormous amount of video information make the efficient terminal-to-cloud data upload a challenge. To solve the problem, this paper proposes an efficient compressed sensing-based high-efficiency video upload system for the terminal-to-cloud upload network. The system contains two main new components. First, to effectively remove the inter-frame redundant information, an encoder sampling scheme with high efficiency is developed by applying the skip block-based residual compressed sensing sampling technology. For the time-varying channel state, the encoder can adaptively allocate the sampling rate for different video frames by the proposed adaptive sampling scheme. Second, a local secondary reconstruction-based multi-reference frames cross-recovery algorithm is developed at the decoder. It further improves the reconstruction quality and reduces the quality fluctuation of the recovered video frames to improve the user experience. Compared with the state-of-the-art reference systems reported in the literature, the proposed system achieves the high-efficiency and high-quality terminal-to-cloud transmission.

In this paper, we propose a novel successive two-way relaying (STWR) system that uses a pair of conventional half-duplex (HD) relays to mimic a full-duplex two-way relay (FD-TWR). Although classical FD-TWR is spectral efficient and expands cell coverage, the proposed STWR utilizes the existing HD infrastructure to boost the FD implementation and offers bi-directional data exchange and low-complexity residual self-interference (RSI) mitigation. To formulate STWR, we develop a unified signal model to facilitate the mitigation of the generalized self-interference (GSI). GSI consists of back-propagating interference due to two-way relaying, RSI of FD sources and inter-relay interference caused by the pairs of HD relays. Because the GSI channel matrix has a distinct row linearity, we propose an efficient digital approach to remove the GSI and design two low-complexity algorithms. These algorithms avoid RSI channel estimation, full-rank matrix, and complex matrix computation. Our analysis and simulations show that: 1) the proposed STWR achieves the multiplexing gain of the true FD-TWR; 2) the distance between the two HD relays should be optimized to achieve the highest spectral efficiency; and 3) the STWR system with two algorithms can achieve a diversity order of one or two, respectively. Therefore, the STWR concept achieves a flexible tradeoff between performance and complexity, potentially enabling large-scale relay deployments.

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.

This paper proposes a novel design of cooperative non-orthogonal multiple access (NOMA) for layered multicast, where the information is encoded into the messages of high-priority (HP) and low-priority (LP). Two types of multicast users coexist in the system: 1) regular users (RUs), which locate far away from the base-station (BS) and demand only the HP message; 2) advanced users (AUs), which locate close to the BS and demand both HP and LP messages. To improve the reliability of layered multicast, an opportunistic cooperative NOMA multicast strategy is proposed, in which one successful AU is selected to forward both HP and LP messages. For the proposed strategy, we derive closed-form exact outage probabilities of AUs and RUs. By further carrying out the asymptotic analysis, the achieved diversity orders are shown to be not less than the number of AUs, i.e., full diversity is achieved. Finally, numerical results verify the theoretical analysis and demonstrate the superiority of the proposed strategy.

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.

Recently, there has been a surge of increased interest in using physical layer security to safeguard the fifth generation (5G) mobile networks. In this paper, we study the problem of secure communications in untrusted relay networks. A new nonorthogonal relaying protocol is proposed, aiming at maximizing the secrecy rate. Specifically, we allow the source and relay to transmit signals simultaneously over non-orthogonal channels, and apply successive interference cancellation at the destination to separate the multiplexed signals. We also propose two transmit antenna selection schemes to further improve security. To evaluate the secrecy performance, closed-form expressions for ergodic secrecy rate (ESR) and ESR scaling law are derived. The results show that the proposed non-orthogonal relaying protocol yields a considerable ESR improvement than conventional orthogonal relaying, despite the presence of co-channel interference. It is also revealed that the ESR scales with the average signal-to-noise ratio γ̅ and the number of available transmit antennas Ntaccording to log (√γ̅ log Nt), thus achieving superior secrecy performance in the untrusted relay networks.

This paper proposes a new optimal time-switching relaying protocol for wireless-powered decode-and- forward relay networks. The relay has no embedded power supply, and it has to harvest energy from radio-frequency signals transmitted by the source. In specific, we focus on the time-switching mechanism where a fraction of block time is used for energy harvesting (EH) and the remaining is used for information transmission (IT). Equipped with a rechargeable battery, the relay can accumulate the harvested energy and choose a proper transmit power. For system throughput maximization, we propose an optimal time-switching relaying (TSR) protocol to optimally determine the EH time and the relay power, according to the channel state and the battery state. Interestingly, we prove that the optimal EH time is a discrete two-state variable, i.e., the whole block time is used for either IT or EH. With the proposed TSR relaying protocol, the analytical expression for the system throughput is derived. Simulations validate the analysis and demonstrate that a significant throughput improvement can be achieved.

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.

The inherent broadcast characteristics of wireless medium make wireless data transmission difficult to be shielded from unintended recipients. As such, secure communication over wireless channels becomes a critical issue in the design of wireless networks. In this study, the authors study cooperative jamming and power optimisation in untrusted relay networks to improve physical layer security. By exploiting the direct link, a source‐based jamming (SBJ) scheme is proposed to hinder the untrusted relay from intercepting the confidential message. Considering the total power budget, a power allocation strategy is also proposed to optimally determine the power of source and untrusted relay as well as the power of information and jamming signals. Furthermore, to evaluate the secrecy performance of the proposed SBJ scheme with the power allocation strategy, tight lower bound of ergodic secrecy capacity and asymptotic secrecy outage probability are derived in closed form. Simulation verifies the advantages of the proposed SBJ scheme and the power allocation strategy.

Non-orthogonal multiple access (NOMA) is emerging as a promising, yet challenging, multiple access technology to improve spectrum utilization for the fifth generation (5G) wireless networks. In this paper, the application of NOMA to multicast cognitive radio networks (termed as MCR-NOMA) is investigated. A dynamic cooperative MCR-NOMA scheme is proposed, where the multicast secondary users serve as relays to improve the performance of both primary and secondary networks. Based on the available channel state information (CSI), three different secondary user scheduling strategies for the cooperative MCR-NOMA scheme are presented. To evaluate the system performance, we derive the closed-form expressions of the outage probability and diversity order for both networks. Furthermore, we introduce a new metric, referred to as mutual outage probability to characterize the cooperation benefit compared to non-cooperative MCR-NOMA scheme. Simulation results demonstrate significant performance gains are obtained for both networks, thanks to the use of our proposed cooperative MCR-NOMA scheme. It is also demonstrated that higher spatial diversity order can be achieved by opportunistically utilizing the CSI available for the secondary user scheduling.

Optimal relay selection is investigated for secure cooperative communications against an adaptive eavesdropper that can perform eavesdropping if the eavesdropping link has good channel quality or perform jamming otherwise. A number of decode-and-forward relays are available for legitimate communications, among which one relay can be selected to help. For legitimate communications, three cases for availability of the eavesdropping channel information are considered: full channel knowledge, partial channel knowledge, and statistical channel knowledge. An optimal relay selection scheme is proposed for each case. For the first and third cases, exact secrecy outage probability expressions in closed form are derived, and for the second case, an approximate secrecy outage probability is derived, which is tight in the high main-to-eavesdropper ratio regime. Moreover, secrecy diversity order for the proposed relay selection scheme in each case is also derived, which is shown to be a full secrecy diversity. Finally, numerical results are given to verify the theoretical analysis derived in this paper.

This paper designs a novel non-orthogonal multiple access (NOMA) unicast-multicast system, where a number of unicast users (those who require different messages) and a group of multicast users (those who require identical message) share the same time/space/frequency resource. For the designed NOMA unicast-multicast system, an efficient two-phase cooperation strategy is proposed to improve the reliability of all users. In the first phase, the base station (BS) broadcasts a superposed message consisting of all users' information. In the second phase, a multicast user is selected to forward the information intended by unsuccessfully decoded unicast and/or multicast users. Moreover, the multicast user selection is investigated under two different power allocation (PA) approaches: 1) fixed PA (FPA), in which the PA coefficients for both the phases are predetermined, and 2) dynamic PA (DPA), in which the PA coefficients for the first phase are predetermined, while the PA coefficients for the second phase are dynamically determined based on instantaneous channel information. Under the FPA approach, a best user selection (BUS) scheme (called F-BUS) is proposed to minimize the outage probability. Under the DPA approach, the local optimal PA coefficients for the second phase are derived in closed form first. Based on the derived PA coefficients, a BUS scheme (called D-BUS) is then proposed for outage probability minimization. To verify the reliability of the proposed cooperation strategy with employing the BUS schemes, we theoretically analyze the outage probability as well as diversity orders. It is shown that the proposed cooperation strategy achieves diversity orders equal to the number of multicast users, indicating that the inherent diversity orders offered by the multicast users are fully exploited. Finally, simulation results are presented to validate the theoretical results and demonstrate the advantages of the proposed cooperation strategy and the BUS schemes.

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.

Successive relaying has recently emerged as a spectral-efficient technique for cooperative wireless communications. However, in scenarios with uncertain accuracy of instantaneous channel state information (CSI), the scope of conventional two-path successive relaying protocol shrinks. This is due to the challenges of cancelling inter-relay interference (IRI) and detecting signals efficiently without specific CSI. The two challenges motivate us to propose a novel double listening 3-path successive relaying (DL3PSR) protocol to mitigate the successive relaying's dependence on accurate instantaneous CSI. We overcome the first challenge by proposing a blind IRI cancellation technique, and its effectiveness is proven. After blind IRI cancellation, the challenge of efficient signal detection is overcome by robust cooperative non-coherent detection, which consists of exclusive OR (XOR) demodulation and joint decoding. Based on constellation mapping, XOR demodulation is designed to demodulate M-ary phase-shift keying symbols without instantaneous CSI. Moreover, joint forward and backward decoding strategy is proposed to improve the robustness of data detection by retrieving two independent versions of the original data. Furthermore, the detection threshold, bit error probability, and achievable rate of DL3PSR are theoretically obtained. Simulations verify that in scenarios without full knowledge of CSI, DL3PSR protocol outperforms conventional two-path successive relaying in bit error performance and that the rate of DL3PSR is close to that of the full-duplex relaying. Copyright © 2016 John Wiley & Sons, Ltd.

Owing to environmental, financial and quality of service (QoS) considerations, energy efficient wireless communications have been paid increasing attention under the background of limited energy resource. This study considers a spectrum sharing cognitive radio network, where the primary system leases its radio spectrum to the secondary system for a fraction of time in exchange for secondary users served as relays to assist the transmission of the primary traffic. Based on the cooperative spectrum leasing protocol, the authors formulate a joint optimisation of relay selection and power allocation under QoS requirements to improve energy efficiency (EE). By employing a greedy spectrum sharing (GSS) algorithm, the optimal relay selection, power and sharing time allocation are readily obtained. Monte–Carlo simulations are performed to demonstrate that significant EE improvement is achieved by the proposed GSS algorithm, and that the network performance is enhanced.

The high‐speed mobility of modern transportation worsens the situation of unstable direct links and imprecise channel estimation, which poses challenges on fast and reliable data delivery in vehicular ad hoc networks (VANETs). The goal of the proposed scheme is to provide high‐speed full‐duplex relaying connectivity for unstable direct links, and reliable information detection without precise channel state information (CSI) in VANETs. In the proposed scheme, full‐duplex relaying connectivity is provided by differential successive relays (DSRs) at roadside or inside other vehicles. To improve robustness of DSR without specific CSI, efficient blind interference cancellation is presented to mitigate inter‐relay interference. Meanwhile, reliable information detection without precise CSI is enabled by superposition coding with differential modulation. Furthermore, the advantages, applications of DSR and complexities for real‐world implementation are discussed, and the decision rule, bit error probability and ergodic capacity are theoretically obtained. Numerical results verify significant improvements in the performance of the proposed scheme when compared with conventional schemes with half‐duplex relays and frequent channel estimation in VANETs.

Cognitive radio networks (CRNs) are applied to solve spectrum scarcity. In this study, the authors propose an efficient power control game to improve its performance based on outage probability of primary user in a spectrum‐underlay CRN. The interference threshold deduced from outage probability and normalised signal to interference plus noise ratio are used to develop a novel non‐linear pricing function, which is a key element of obtaining Pareto improvement in non‐cooperative power control game. In addition, an efficient swarm intelligent algorithm originated from eco‐group activities is designed in detail to accelerate the convergence speed and improve the energy‐efficiency. Theoretical analysis and simulation results are presented to prove the effectiveness and superiority of the proposed power control game.

This paper presents a modulation identification system based on software radio platform. The signals have been conveyed to baseband by frequency conversion. Only three features have been chosen, which are kurtosis of the envelope, variance of phase histogram differential coefficients and valid area of power spectrum density. A fuzzy classifier is used for identification. It is proved by the results that the system performs well with a high percentage of correct identification.

It is important for digital modulation identification based on neural network to determine a suitable size of a network. This paper proposes that the number of nodes in hidden layer which is the core for network optimizing can be confirmed by K-L transform. The validity and robustness are verified by simulation. The percentage of correct identification (PCI) is almost the same before and after the network optimizing.