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Unmanned Aerial Vehicles (UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation (6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly, serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus, UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical challenge. In this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

Accurate network synchronization is crucial to orchestrate distributed infrastructures in Industrial Internet of Things (IIoT) systems for accomplishing network-wide tight temporal collaboration. Traditional clock synchronization can be achieved with extensive exchanges of explicit timestamps for estimating clock offsets, which becomes impractical due to high overhead with the expansion of the network scale. The performance of conventional synchronization will also be dramatically deteriorated due to various uncertainties of IIoT networks. In this article, we propose a passive network synchronization scheme based on concurrent passive observations to calibrate the distributed clocks in IIoT systems while significantly reducing the explicit interactions and network resource consumption during synchronization. By processing the physical phenomena observed concurrently by a group of selected IIoT devices, the local clock offsets of the passive observing devices can be efficiently estimated according to the common time reference linked to the event observed. Multiple relay nodes are further coordinated by the cloud center to disseminate the reference time information throughout the IIoT system. Simulation results demonstrate that by utilizing a series of concurrent observations with efficient coordination, the proposed scheme can achieve accurate and reliable network synchronization for large-scale IIoT systems with significantly reduced network overhead.

Conventional authentication techniques based on cryptography and computational hardness are facing growing challenges for deployment in resource-constrained Internet-of-Things (IoT) devices. The dramatically increased security overhead and latency from the inherent computational processing make these conventional static security techniques undesirable for emerging machine communications. In this paper, we propose a novel lightweight continuous authentication scheme for identifying multiple resource-constrained IoT devices via their pre-arranged pseudo-random access time sequences. A transmitter will be authenticated as legitimate if and only if its access time sequential order is matched with a pre-agreed unique pseudo-random binary sequence (PRBS) between itself and the base station. The seed for generating the PRBS between each transceiver pair is acquired by exploiting the channel reciprocity, which is time-varying and difficult for a third party to predict. Hence, the proposed scheme provides seamless protection for legitimate communications by refreshing the seeds adaptively without incurring long latency, complex computation, and high communication overhead. Our results show that the proposed scheme achieves high entropy and low bit mismatch rate. Finally, we demonstrate the superiority of our scheme over the existing schemes in quantization performance, authentication performance, and computation cost.

The fifth-generation (5G) wireless communication networks are expected to play an essential role in the transformation of vertical industries. Among many exciting applications to be enabled by 5G, logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything (V2X) communications. In this paper, a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks. The proposed framework includes three layers: a perception and execution layer, a logistics layer, and a configuration layer. In addition to the collaboration among these three layers, this study addresses the collaboration among devices, edge servers, and cloud services. For effective logistics in industrial parks, task collaboration is achieved through four functions: environmental perception and map construction, task allocation, path planning, and vehicle movement. To dynamically coordinate these functions, device-edge-cloud collaboration, which is supported by 5G slices and V2X communication technology, is applied. Then, the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks. Finally, a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

Doppler suppression techniques with angle-domain Doppler compensation target reducing the time fluctuations of time-varying channel for high-mobility massive MIMO communications. However, the existing Doppler suppression solutions suffer from high peak-to-average power ratio (PAPR). In this letter, we propose a PAPR reduction scheme for Doppler suppression, where an optimization problem is formulated to minimize the Doppler spread under a constraint of tolerable PAPR level. We further develop a suboptimal solution which decomposes the original joint optimization problem into two steps. First, an iterative Fourier technique is employed and the idea of basis expansion model (BEM) is proposed to reduce the computational complexity. Then, the antenna weighting vector is optimized which takes into consideration both Doppler spread and PAPR. Simulation results are provided to demonstrate that the proposed scheme can substantially outperform the existing schemes.

Tight cooperation among distributively connected equipment and infrastructures of an Industrial-Internet-of-Things (IIoT) system hinges on low latency data exchange and accurate time synchronization within sophisticated networks. However, the temperature-induced clock drift in connected industry facilities constitutes a fundamental challenge for conventional synchronization techniques due to dynamic industrial environments. Furthermore, the variation of packet delivery latency in IIoT networks hinders the reliability of time information exchange, leading to deteriorated clock synchronization performance in terms of synchronization accuracy and network resource consumption. In this article, a digital-twin-enabled model-based scheme is proposed to achieve an intelligent clock synchronization for reducing resource consumption associated with distributed synchronization in fast-changing IIoT environments. By leveraging the digital-twin-enabled clock models at remote locations, required interactions among distributed IIoT facilities to achieve synchronization is dramatically reduced. The virtual clock modeling in advance of the clock calibrations helps to characterize each clock so that its behavior under dynamic operating environments is predictable, which is beneficial to avoiding excessive synchronization-related timestamp exchange. An edge-cloud collaborative architecture is also developed to enhance the overall system efficiency during the development of remote digital-twin models. Simulation results demonstrate that the proposed scheme can create an accurate virtual model remotely for each local clock according to the information gathered. Meanwhile, a significant enhancement on the clock accuracy is accomplished with dramatically reduced communication resource consumption in networks with different packet delay variations.

The detection of anomalous Internet-of-Things (IoT) sensor data is extremely important in many industrial applications due to the catastrophic consequences of the faulty or unreliable sensor data. Good anomalous data detection performance with high detection efficiency is indeed a dilemma since it is difficult to derive an explicit detection function to characterize the relationships between the anomalous values and the detection indicator. To overcome this difficulty, the location information of the IoT sensors is exploited in this study to characterize and reconstruct the relationships among the sensor data based on a second-order nonlinear polynomial graph filter (NPGF). The analysis of the sensor data reconstruction model is then conducted in the frequency domain based on the 2-D inverse graph Fourier transform (GFT), and the reconstruction error function for the sensor data is analytically derived based on the second-order GFT coefficients. It is shown that the detection efficiency can be greatly improved if the input graph signal is designed to be bandlimited. The anomalous sensor data detection is then conducted in the frequency domain as high-frequency components are more sensitive to the deviation values. An NPGF-based frequency-domain algorithm is proposed for the anomalous sensor data detection, which is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the detection performance and efficiency improvement of the proposed algorithm in anomaly detection.

The ever-growing wireless applications and their diverse Quality of Service (QoS) requirements bring the challenge of tailored QoS provisioning with limited radio resources in future cellular networks. While resource constraint is ubiquitous, different communication equipment in cellular networks could experience very different constraints in the multi-dimensional resource domains. To achieve stringent yet diverse QoS with limited resources, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is proposed in this paper to exploit disparate resource constraints among heterogeneous equipment for 5G beyond and 6G networks. With the assist of real-time data analysis, real-time QoS requirements, and resource availability of the related equipment are first determined in the proposed MD-IMA. Based on this, multiple access (MA) scheme is then intelligently adapted accordingly for each equipment in multi-dimensional resource domain to maximize the overall system requirement with operational constraints. The resource allocation in the MD-IMA system is further formulated as an optimization problem. To solve this non-convexity optimization of high computational complexity, the overall optimization is divided into several sub-problems and a joint optimization algorithm is adopted. Simulation results demonstrate the system energy efficiency performance gain of proposed MD-IMA over traditional MA is around 15% - 18%.

The mechanism of vehicle dynamics steering bifurcation has almost been confirmed. But the present steering bifurcation mechanism cannot explain the bifurcation phenomena caused by the driving torque. As a result, the vehicle coupled bifurcation analysis of the steering angle and driving torque has not been studied. Based on the five degrees of freedom (5DOF) vehicle system dynamics model with driving torque involved, the vehicle dynamics equilibriums under different driving torque and driving mode were searched by a hybrid method in this paper. The hybrid method combined the real-coded Genetic Algorithm with Quasi-Newton gradient method. According to the definition of static bifurcation of nonlinear systems, the equilibrium bifurcation of 5DOF vehicle system was confirmed. Then, the 5DOF vehicle system model was transformed into autonomous equation with the front wheel steering angle as intermediate variable. From the two aspects of constant steering angle amplitude and constant driving torque, the bifurcation diagrams of different driving mode were calculated. The vehicle coupled bifurcation characteristics of steering angle and driving torque were analyzed. The results show that the values of the driving torque will directly affect the bifurcation characteristics of vehicle dynamics system. The coupled feature of the front wheel steering angle and driving torque effect on vehicle bifurcation is obvious.

While frequency-domain algorithms have been demonstrated to be powerful for conventional nonlinear signal processing, there is still not much progress in literature dedicated to nonlinear graph signal processing in frequency domain. The difficulties come from the irregular structure of graph signals and the lack of a straightforward description for the nonlinear relationship between input and output graph signals. In this paper, a distributed nonlinear polynomial graph filter (NPGF) is proposed to characterize the nonlinear input-output relationship by employing polynomial-type nonlinearity based on Hadamard product. Then, the nonlinear output graph signal is reformulated based on the multi-dimensional inverse graph Fourier transform (GFT), where the m -dimensional GFT coefficients are defined as explicit functions of input graph spectrum and filter structural factors (filter parameters, graph shift matrix, nonlinear degree, and graph shift order). The effects of filter structural factors on the output graph spectrum are further evaluated by proposing graph frequency response (GFR) coefficients, which inherently characterize the irregular structural behaviors of a distributed NPGF in frequency domain. The nonlinear output graph spectrum is then developed based on GFR vectors by mapping the GFR coefficients in the tensor formulation to the output GFT coefficients in the vector expression. This can greatly improve the efficiency in calculating the output graph signal if the input signal only involves a few frequency components. Finally, algorithms are proposed to design the filter parameters for the desired output signal. Numerical studies are presented to illustrate and validate the proposed frequency-domain framework for nonlinear graph signal processing.

Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues. However, finding the right parameters for an AQM scheme is very challenging due to the dynamics of the IP networks. In addition, this problem becomes even more complex in inter-domain scenarios where several organizations interconnect each other with the limitation of not sharing raw and private data. As a result, existing AQM schemes have not been widely employed despite their advantages. Therefore, we present a solution that tackles the challenges of tuning the AQM parameters for inter-domain congestion control scenarios where the network management goes beyond an organization’s domain. We then introduce the Federated Intelligence for AQM (FIAQM) architecture, which enhances the existing AQM schemes by leveraging the Federated Learning approach. The proposed FIAQM framework is capable of dynamically adjusting the AQM parameters in a multi-domain setting, which is hard to achieve with the conventional AQM solutions working alone. To this end, FIAQM uses an artificial neural network, trained in a federated manner, to predict beyond-own-domain congestion and an intelligent AQM parameter tuner. The evaluation results show that FIAQM can effectively improve the performance of the inter-domain connections by reducing the congestion on their links while preserving the network data private within each participating domain.

While the unmanned aerial vehicles (UAVs) swarm travels under a dynamic environment, the cluster head (CH) switching is unavoidable due to the mitigation of mobility, quality of service, and energy consumption. If an attacker becomes the new CH, the entire swarm will be controlled and the sensitive data will be leaked. Unlike the other mobile networks with constant network connectivity, the authentication in the UAV swarm suffers from intermittent connection with the ground station under a hostile environment or spectrum constraint condition. Hence, this paper proposes a novel CH safeguarding mechanism enabled by edge intelligence utilizing a situational-aware authentication scheme. This low-latency mechanism provides extra security at the CH selection and switching without cloud server support. By adopting the unique cross-layer attributes, the system security is significantly improved based on the extracted multi-dimensional information. The Linear Discriminant Analysis (LDA) algorithm fuses the authentication decision accurately by projecting the high dimensional estimations into a low dimensional space for maximum separability by only keeping the necessary attributes. A situation-aware cross-layer attribute selection algorithm is developed to select a minimum number of attributes so that the time required for attribute estimation and computation overhead of authentication can be reduced. The simulation results demonstrate that our scheme performs better under a dynamic environment compared with the physical layer authentication scheme and some existing state-of-the-art authentication techniques.

Nowadays the deployment of drones/UAVs is not just limited to military and defense establishments; they are also widely deployed in geo-dispersed applications (environmental monitoring, rescue operation monitoring, road and traffic surveillance, natural disaster monitoring, soil and crop analysis, and consumer product delivery). Drones have become the instigators and enablers of global mechanization. For example, the influx of precocious drones is recognizable looking at the high angled photography of peculiar landscapes. Likewise, multiple drones coordinate and collaborate with each other to form a web of flying computing resources which can sense, analyze, and transmit data to remote cloud servers and storage spaces. Even more, the popularity of next generation communication technologies has opened a new vertical in the form of 5G-en-abled flying automation using drones. This new dimension for drone communication has attracted the wide attention of the academia, research and industrial communities. 5G-enabled flying automation empowers the astronomically large numbers of connected things in a drone-enabled smart ecosystem to communicate among themselves and thereafter decisively handle the progressively intricate traffic conditions.

Although the use of coordinated direct and relay transmission (CDRT) in non-orthogonal multiple access (NOMA) can extend the coverage, its duplicated transmission reduces the spectrum efficiency (SE) of NOMA. To improve the SE, we propose a spectrum-efficient scheme for NOMA-based CDRT over Nakagami- $m$ fading channels. In this scheme, the base station (BS) connects with a cell-center user (CCU) directly while communicating with a cell-edge user (CEU) via a relay and the CCU. Then, the relay and the CCU use network coding to process and retransmit the signals sent by the BS first and the CEU later. Finally, the BS and the relay simultaneously broadcast downlink signals. We derive the closed-form expressions for the average SE, the user fairness index and the energy efficiency (EE) as well as the asymptotic average SE using both perfect and imperfect successive interference cancellation (SIC). Simulations verify the correctness of our theoretical analysis and the superiority of the proposed scheme in SE and EE.

Unmanned aerial vehicle (UAV) can be combined with non-orthogonal multiple access (NOMA) to achieve better performance. However, jointly optimizing the location, transmit power and decoding order for NOMA-UAV networks remains difficult, due to the change of decoding order as a result of UAV mobility. In this letter, a low-complexity scheme is proposed to maximize the sum rate of NOMA-UAV networks via updating decoding order, which can be decomposed into two steps. First, the joint location and power optimization can be divided into two non-convex sub-problems, which are further approximated via successive convex optimization. Then, the decoding order is updated according to the optimized UAV location. An iterative algorithm is proposed to execute the two steps alternately. In addition, the asymptotic performance is analyzed. Simulation results demonstrate the effectiveness of the proposed scheme.\ud

Accurately estimating network traffic from the partial measurements plays a crucial role in network management. However, the potential anomaly existing in real networks usually makes this goal difficult to achieve. Existing network traffic estimation methods generally impute network traffic independent of anomaly detection, which incurs significant performance degradation with network anomaly. To address this issue in the realistic network scenario, we propose a novel anomaly-aware network traffic estimation method to recover network traffic data concurrently with network anomaly detection. Specifically, by exploiting the inherent spatio-temporal characteristics, we first formulate the network traffic estimation as a low-rank tensor completion problem. Then, an outlier-robust tensor completion (OrTC) model is constructed by introducing both L2,1-norm regularization and ${\mathrm {L}}_{F}$ -norm regularization, which can not only well fit the intrinsic low-rank property of real traffic data, but also is robust against both the dense noise and the sparse anomaly. Furthermore, an effective optimization algorithm OrTC-AM is designed to solve the non-convex and non-smooth OrTC model based on the popular alternating minimization method. Finally, the extensive experiments performed on the public dataset demonstrate that our proposed OrTC-AM method outperforms the previously widely used network traffic estimation methods.

Big data analytics has been rapidly integrated into Intelligent Transportation System (ITS), empowering diverse applications such as real-time traffic prediction and management. However, incomplete traffic time-series data during the data analysis are nearly inevitable due to the constraints of data collection or packet loss in the communication process. Existing tensor-based completion methods fail to perform well in consecutively missing (CM) cases where a sub-series of the traffic time series is completely missing. Moreover, their high computational complexity prevents the road network-level implementation. Therefore, to tackle these problems, a batch-oriented traffic data completion method for large-scale road networks is proposed in this paper. In order to preserve multi-way natures of traffic data, we first model the traffic data by tensors so that traffic data completion becomes a tensor completion problem. Several types of tensor structures are adopted in order to analyze their impacts on traffic data completion. Thereafter, a Content-Aware traffic data completion method is further developed based on the Generative Adversarial Net (CA-GAN). More specifically, the formulated traffic tensors are interpreted as samples from a high-dimensional traffic distribution and a GAN is then proposed to learn this distribution. By considering effects of traffic data from different days and links, a weighted loss function is employed to search for the raw traffic tensor in the obtained traffic distribution. Afterwards, the estimated raw traffic tensor is used to complete the missing data. Visualization results demonstrate that our GANs can produce verisimilar traffic patterns with different time spans. Finally, our experiments on real traffic datasets show that the proposed CA-GAN method can effectively batch incomplete traffic tensors in CM cases with fast recovery process.

The proliferation of the IoT in connected society is rapidly expanding into vertical industry sectors due to the ever-increasing ties amongst businesses and economies. As the number of IoT nodes utilized in a network increases, decentralized network infrastructure, and security provisioning mechanisms, primarily enabled by blockchain-based technologies, become more beneficial. However, blockchain-based IoT networks experience transaction throughput degradation due to the platform's cryptographically-based security features, where negotiating with ledger maintainers for faster processing is a must. Existing transaction processing schemes are mainly geared towards digital currency applications. In overcoming these challenges, a novel feeless transaction processing algorithm is proposed for non-cryptocurrency blockchain-based IoT networks. The proposed algorithm enables ledger maintainers in achieving desired processing throughputs for select transactions found in a miner's transaction pool. A utility function is designed to select transactions from miners’ transaction pools to form blocks that add a desired operational value for achieving pre-determined production outputs over blockchain-based networks. Furthermore, the proposed scheme will utilize an aging process to increase the likelihood of selecting transactions with larger miners’ transaction pool residence times. The simulation and implementation results show that the proposed methodologies increase the processing throughput of desired transactions while preventing transaction processing starvation.

Utilizing the idle computing resources from the distributed Internet of Things devices can sustainably increase the computational capacity and thereby effectively alleviate the pressure on resource-constrained devices, which is referred to as collaborative computing. However, extra computing consumption potentially impacts the local computation tasks of collaborative computing devices. Hence, it is essential to design an efficient incentive mechanism for computational resources sharing. Specifically, we consider the collaborative computing system where a user offloads the computation-intensive and latency-sensitive tasks to multiple idle computing devices (ICDs) by a centralized computing sharing platform (CSP). We first propose a computational latency-based pricing mechanism from the perspective of the quality-of-experience performance; then, a game-theoretic computing task allocation approach is developed among the CSP and multiple ICDs to maximize all participants’ profit. The CSP first determines the optimal task partition dynamically upon the tasks’ arrival; then, the ICDs derive the optimal central processing unit-cycle frequency correspondingly. Simulation results demonstrate that the overall computational latency of our proposed mechanism is significantly decreased, and achieves by at least 13.5 percent improvement compared with the existing schemes. Meanwhile, the profit of all participants is maximum in collaborative computing, which is improved by 41.5 and 27.9 percent for the CSP and the ICDs, respectively.

Due to abundant spectrum resources, communications in the millimeter-wave (mm-wave) bands have been widely considered as the key enabler for next-generation wireless systems, including future high-speed railway (HSR) wireless systems, to dramatically increase data rates. To overcome the hostile propagation properties of mm-wave bands, antenna arrays, which enable directional transmissions with extended transmission distance, become essential techniques. Nevertheless, the high cost of antenna arrays and the limited battery lifetime of user equipment are slowing down the practical deployment of mm-wave communications. Without these limitations, an HSR scenario is better suited for mm-wave communications. However, the point-like coverage of mmwave directional beams cannot provide robust access for high-mobility scenarios.

Due to the urgent and unpredictable nature of disaster relief, emergency management systems (EMS) faces an enormous challenge of real-time data analysis without the complete information from emergency communication networks (ECNs). In this letter, we propose an incomplete information based two-tier game model (IITG) to realize collaborative computing at the edge of ECNs, which incentivizes idle computing devices (ICDs) to share computation resources through maximizing utilities of EMS and ICDs. Furthermore, we develop a near-optimal IITG algorithm (N-IITG) to seek the unique Bayesian Nash equilibrium. Simulation results reveal that N-IITG outperforms the existing incomplete information based methods in terms of computation latency and participants utilities.

The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.

Understanding the chemical composition of gases trapped in rocks is important in a wide variety of earth science studies and gases are often extracted from rock samples and analyzed. Gas chromatography (GC) and mass spectrometry (MS) are the most frequently used analytical techniques for measuring gases extracted from rock samples, but these techniques have rarely been coupled except for gas chromatography-mass spectrometry (GC-MS) or Continuous flow isotope ratio mass spectrometry (CF-IRMS). A thermal conductivity detector (TCD) is most frequently used in GC because of its ability to detect all gases, but has limited sensitivity and thus cannot accurately detect trace gases in some rock samples. To resolve this issue, we built a new device that tests gases extracted from rock samples by GC and MS together, and employs a pulsed discharge detector (PDD) with GC analyses rather than a TCD because PDD is capable of detecting most gases and exhibits higher sensitivity than that of TCD. In this device, a rock sample can be heated or crushed in vacuum to release gases. A diaphragm gauge is then used to measure the pressure of the gases released from the rock samples. Four kinds of samples were tested using this new device: serpentinized peridotites from Inner Mongolia, volcanic rocks from Wudalianchi, shale rocks from Sichuan basin and reservoir rocks from Songliao basin. The chemical contents of H2, CH4, H2O, CO, N2, C2H4, C2H6, O2, H2S, Ar, CO2, C3H8, COS and SO2 could be measured simultaneously.

Accurate clock synchronization in the industr-ial-Internet-of-Things systems forms the cornerstone of distributed interaction and coordination among various infrastructures and machines in an industrial environment. However, due to the widespread use of wireless networks in industrial applications, constraints inherent to wireless networks including uncertain propagation delays, random packets losses, and unguaranteed communication resources are unavoidable, leading to dramatically increased clock synchronization error and unreliable or even outdated information. Meanwhile, time information transmissions are vulnerable to suffer from malicious attacks, causing unreliable timestamps and insecure synchronization. In this article, we proposed a distributed clock synchronization protocol based on an intelligent clustering algorithm to achieve accurate, secure, and packet-efficient clock synchronization. The varying rate of skew of every clock is collected and utilized for cluster formation as well as malicious node detection. According to established clusters, various synchronization frequencies are assigned, which can avoid excessive network access contention, reduce overall communication resource consumption, and improve synchronization accuracy. Meanwhile, a two-tier fault detection algorithm consists of outlier detection and second-order regressive model prediction is applied to determine potential malicious nodes. The simulation results demonstrate that the proposed protocol overwhelms simultaneous synchronization protocols in terms of synchronization performance and faulty node detection.

Detecting the existence of anomaly and localizing the faulty sensors in the Internet-of-Things (IoT) systems are extremely critical, since the incorrect data could lead to catastrophic consequences in many vertical industry applications. The difficulties of such problems come from deriving an explicit error function for each sensor in IoT, and the data continuity in the temporal domain would also be seriously challenged. To overcome these difficulties, the irregular spatial information of the IoT sensors is utilized by constructing an adjacency matrix using the distances among different sensors, and the nonlinear polynomial graph filter (NPGF) is employed to characterize the relationships among the collected sensor data. The NPGF provides a more accurate model for reconstructing the sensor data by taking the data nonlinear relationships into account. The error functions at each sensor for newly detection data are theoretically derived, and it is demonstrated that the error at the anomalous sensor performs differently from that of the normal sensors if the adjacency matrix is designed appropriately. The proposed NPGF-based algorithm is illustrated and validated with a real-world data set for temperature monitoring. The simulation results demonstrate the superior performance of our scheme in both anomaly detection and faulty sensor localization when compared with existing algorithms, such as the graph frequency algorithm and oversampling PCA (OS-PCA) method, especially for the case of small sensor data deviations.

With the explosive growth of data traffic over wireless access networks, the use of the millimeter- wave band becomes inevitable due to the spectrum scarcity in lower frequencies and the availability of large chunks of underutilized spectrum in this band. The rapid evolution of wireless technologies and co-existence of legacy and emerging wireless infrastructures are leading to complex network operation scenarios. For instance, to achieve the orchestration of co-existed networks and a large number of devices, dynamic operations of conventional distributed WLANs will become extremely challenging. Various technologies such as dense network deployment, dual-band cooperation, C-RAN, and AI, which have been developed for cellular networks, could be adopted to achieve intelligent and efficient WLAN operations. To meet the future traffic requirements of evolving WLAN, this article first outlines the architecture of future WLAN and the corresponding challenges. A centralized control architecture, named WLAN C-RAN, is proposed to achieve orchestrated coordination and improved network throughput. Critical technologies of WLAN C-RAN, for example, dual-band protocol stack and resource management schemes are also developed, and the simulation results demonstrate the improvements on system capacity. Finally, we present some potential challenges and feasible solutions for the future WLAN.

High quality multimedia streaming over the Internet has proliferated in modern society due to the ever-increasing ties amongst people and businesses, thereby occupying the majority of all exchanged data traffic. The Internet is the primary multimedia exchange medium that is being used beyond its intended design. Using Hypertext Transfer Protocol/Transmission Control Protocol (HTTP/TCP), multimedia can be delivered to virtually all devices connected to the Internet. HTTP-based streaming suffers from the increasing overhead generated as the stream length, quality, and non-deterministic path conditions vary. In this paper, a novel cross-layer signalling reduction scheme is proposed to alleviate resource consumption in networks exchanging multimedia. The proposed scheme is a steganographic-based protocol translator that encodes information within multimedia payloads prior to packet flight to reduce the size of exchanged data. The encoded data is used by node pairs to replace bulky protocols, such as TCP, with lightweight protocols, such as User Datagram Protocol (UDP). In addition to the protocol translator, a routing scheme is given to be used in place of the inflated networking protocols to further reduce the header footprint. A utility function is developed to find the optimal overhead savings where simulations are conducted to verify the designs. Using virtual machines, the proposed translator is implemented where a multimedia file is exchanged and subject to encoding. The simulations and implementation show that the proposed methodologies will decrease the amount of signalling needed while increasing the overall network capacity. Furthermore, the proposed methods are capable of successfully extending existing signalling reduction methods.

In this paper, we propose a novel hierarchical radio-frequency identification (RFID) tag-recognition method based on blind source separation (BSS), graph-based automatic modulation classification (AMC), and direct-sequence spread-spectrum (DSSS). In our proposed method, RFID tags can be modulated using different modulation schemes according to different scenarios (e.g., different users or different tag devices). For each modulation scheme, the direct-sequence spread-spectrum strategy is employed to allow simultaneous transmissions of multiple commands. In the signal separation phase, BSS is employed to separate different transmitted signals. Then in the first hierarchy of the recognition phase, different modulation types are adopted to distinguish different users, the graph-based AMC is built upon the periodicity of the modulated signals: the cyclic spectrum of the received signal is established; the graph representation is then constructed according to the cyclic spectrum. Ultimately, robust features are extracted from the graph representation. In the second hierarchy of the recognition phase, the DSSS scheme is utilized to differentiate the control or sensed data carried by individual tags; the signature sequence set with low cross-correlations can be generated from Kasami sequences. In the third hierarchy of the recognition phase, the information data are thus spread by these signature sequences. In our proposed new RFID framework, multiple tags can transmit signals simultaneously in the same frequency band where each tag signal can still be separated and identified and its carried information can be recovered. Monte Carlo simulation results demonstrate the promising performance of our proposed new RFID scheme.

In this correspondence, we study the outage performance for the simultaneous wireless information and power transfer (SWIPT) full-duplex relay system. The energy-constrained relay node is subject to the co-channel interference (CCI). Although the interference deteriorates the system performance, it can also provide additional energy for harvesting at the relay. Hence, in order to quantify the impact of CCI for the energy harvesting system, a closed-form and asymptotic expression of the system outage performance is derived for the power splitting (PS) based relay. Moreover, with the aid of the approximated outage analysis, we obtain a simple expression for the optimal PS ratio. Simulation results are provided to validate the accuracy of our analysis.

In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a location-based $k$ -means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods.

With the emergence of 4k/8k video, the throughput requirement of video delivery will keep grow to tens of Gbps. Other new high-throughput and low-latency video applications including augmented reality (AR), virtual reality (VR), and online gaming, are also proliferating. Due to the related stringent requirements, supporting these applications over wireless local area network (WLAN) is far beyond the capabilities of the new WLAN standard – IEEE 802.11ax. To meet these emerging demands, the IEEE 802.11 will release a new amendment standard IEEE 802.11be – Extremely High Throughput (EHT), also known as Wireless-Fidelity (Wi-Fi) 7. This article provides the comprehensive survey on the key medium access control (MAC) layer techniques and physical layer (PHY) techniques being discussed in the EHT task group, including the channelization and tone plan, multiple resource units (multi-RU) support, 4096 quadrature amplitude modulation (4096-QAM), preamble designs, multiple link operations (e.g., multi-link aggregation and channel access), multiple input multiple output (MIMO) enhancement, multiple access point (multi-AP) coordination (e.g., multi-AP joint transmission), enhanced link adaptation and retransmission protocols (e.g., hybrid automatic repeat request (HARQ)). This survey covers both the critical technologies being discussed in EHT standard and the related latest progresses from worldwide research. Besides, the potential developments beyond EHT are discussed to provide some possible future research directions for WLAN.

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this article, we propose a joint computation offloading and multiuser scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multiuser resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semidistributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. The simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

Short-term traffic prediction (STTP) is one of the most critical capabilities in Intelligent Transportation Systems (ITS), which can be used to support driving decisions, alleviate traffic congestion and improve transportation efficiency. However, STTP of large-scale road networks remains challenging due to the difficulties of effectively modeling the diverse traffic patterns by high-dimensional time series. Therefore, this paper proposes a framework that involves a deep clustering method for STTP in large-scale road networks. The deep clustering method is employed to supervise the representation learning in a visualized way from the large unlabeled dataset. More specifically, to fully exploit the traffic periodicity, the raw series is first divided into a number of sub-series for triplet generation. The convolutional neural networks (CNNs) with triplet loss are utilized to extract the features of shape by transforming the series into visual images. The shape-based representations are then used to cluster road segments into groups. Thereafter, a model sharing strategy is further proposed to build recurrent NNs-based predictions through group-based models (GBMs). GBM is built for a type of traffic patterns, instead of one road segment exclusively or all road segments uniformly. Our framework can not only significantly reduce the number of prediction models, but also improve their generalization by virtue of being trained on more diverse examples. Furthermore, the proposed framework over a selected road network in Beijing is evaluated. Experiment results show that the deep clustering method can effectively cluster the road segments and GBM can achieve comparable prediction accuracy against the IBM with less number of prediction models.

The machine-to-machine (M2M) communication is an emerging technology that is widely utilized in a vast number of industrial Internet-of-Things (IIoT) applications. Due to the diversity of IIoT applications, provisioning of heterogeneous delay requirements of delay-sensitive machine type devices (MTDs) while optimizing the access efficiency of delay-tolerate MTDs becomes a critical challenge for M2M communications. To address this issue, a multigroup analytical framework for massive random access of M2M communications in IIoT is proposed in this article. Specifically, we consider delay-sensitive MTDs and delay-tolerate MTDs coexist in the network, and those MTDs are divided into multiple groups according to their delay requirements. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the throughput and the mean access delay of each group are characterized. It is found that for each group, the mean access delay decreases as the throughput increases and is minimized when the throughput is maximized. To achieve the maximum throughput of delay-tolerate MTDs under delay constraints of delay-sensitive MTDs, the backoff parameters of delay-sensitive MTDs should be tuned according to the delay constraints while that of delay-tolerate MTDs should be tuned further according to the aggregate packet arrival rate and the number of MTDs in each group. It is further demonstrated that the optimal tuning of backoff parameters is robust against the burstiness of input traffic. The analysis sheds important light on the access design of M2M communications in IIoT with delay constraints.

Recent literature has demonstrated the improved data discovery and delivery efficiency gained through applying named data networking (NDN) to a variety of information-centric Internet of things (IoT) applications. However, from a data security perspective, the development of NDN-IoT raises several new authentication challenges. In particular, NDN-IoT authentication may require per-packet-level signatures, thus imposing intolerably high computational and time costs on the resource-poor IoT end devices. This paper proposes an effective solution by seamlessly integrating the lightweight and unforgeable physical-layer identity (PHY-ID) into the existing NDN signature scheme for the mobile edge computing (MEC)-enabled NDN-IoT networks. The PHY-ID generation exploits the inherent signal-level device-specific radio-frequency imperfections of IoT devices, including the in-phase/quadrature-phase imbalance, and thereby avoids adding any implementation complexity to the constrained IoT devices. We derive the offline maximum entropy-based quantization rule and propose an online two-step authentication scheme to improve the accuracy of the authentication decision making. Consequently, a cooperative MEC device can securely execute the costly signing task on behalf of the authenticated IoT device in an optimal manner. The evaluation results demonstrate 1) elevated authentication time efficiency, 2) robustness to several impersonation attacks including the replay attack and the computation-based spoofing attack, and 3) increased differentiation rate and correct authentication probability by applying our integration design in MEC-enabled NDN-IoT networks.

Benefiting from the widely deployed LTE infrastructures, 5G wireless networks are becoming a critical enabler for the emerging V2X communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and the design of an ML algorithm, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML technologies, the QoS of V2X services is expected to be dramatically enhanced.

This paper proposes a novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. With the proposed channel estimation and beamforming approach, we determine the number of cells $N_{c}$ that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has a maximum of $L$ uncorrelated multipath taps (or correlated multipath taps satisfying modest criteria which is valid in most practical scenarios), it is shown that $N_{c}=L$ cells can estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination. The proposed approach can also be applied for general correlated multipath taps, and achieves good performance for this scenario as well. In a typical long term evolution (LTE) channel environment having delay spread $T_{d}=4.69\,\,\mu \text{s}$ and channel bandwidth $B=5$ MHz, we have found that $L=36$ cells can use this band. In practice, $T_{d}$ is constant for a particular environment and carrier frequency, and hence $L$ increases as the bandwidth increases. All the analytical expressions have been validated, and the superiority of the proposed design over the existing ones is demonstrated using extensive numerical simulations both for correlated and uncorrelated channels. The proposed channel estimation and beamforming design is linear and simple to implement.

The 5G and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulties in pre-designing a precise authentication model, providing continuous protection, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/ unsupervised/reinforcement learning algorithms. In a nutshell, the machine-learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous, and situation-aware device validation under unknown network conditions and unpredictable dynamics.

The integration of millimeter-wave (mmWave) communications and massive multiple input multiple output (MIMO) techniques is a promising solution to dramatically increase the 5G network throughput. By using large antenna arrays, beamforming can be adopted to improve the 5G capacity by employing spatial domain resources. In a frequency division duplexing (FDD) based 5G mmWave MIMO system, beamforming operation requires timely downlink channel state information (CSI) feedback. However, the rapid channel variations caused by short wavelength of mmWave band, and the high-level feedback information required due to the large number of antennas in massive MIMO system lead to the significantly increased beamforming overhead. In this paper, by exploiting the higher angular stability of such channels, we propose an angle-based beamforming scheme to reduce the feedback frequency and the number of feedback bits. To facilitate this approach users are initially selected to reduce the intra-zone interference before beamforming. Besides, location related feedback, which is not affected by the number of antennas, is adopted to reduce overhead. The simulation results show that two proposed user selection algorithms can adapt to scenarios with diverse requirements, while the feedback overhead of proposed angle-based beamforming algorithm is sharply reduce compared with that of CSI-based beamforming algorithm.

IEEE 802.11ax, which is an emerging WLAN standard, aims at providing highly efficient communication in ultra-dense wireless networks. However, due to a large number of stations (STAs) in the ultra-dense device deployment scenarios, the potentially high packet collision rate significantly decreases the communication efficiency of WLAN. In this paper, we propose an adaptive STA grouping scheme to overcome this dense network challenge in IEEE 802.11ax by using Buffer State Report (BSR) based Two-stage Mechanism (BTM). In order to achieve the optimal efficiency of BSR delivery, we analyze the functional relationship between STA number in group and Resource Unit (RU) efficiency. Based on this analysis results, an adaptive STA grouping algorithm with variable group size is proposed to achieve efficient grouping in BTM. The numerical results demonstrate that the proposed adaptive BTM grouping algorithm significantly improves the BSR delivery efficiency and the throughput of overall system and each STA in the ultra-dense wireless network.

One of the challenges related to the investigation of vehicular networks is associated with predicting a network state regarding both short-term and long-term network evolutionary changes. This paper analyzes a case in which vehicles are located on a straight road, and the connectivity state between two consecutive cars is determined by the Markov chain model with two states. The transition probabilities of the considered model are explicitly expressed in terms of known parameters of the network using the Wang-Moayery model. Within the presented model, the network evolution is described in terms of determinative parameters, such as average link duration, average cluster lifetime, and a clusters' existence probability between two fixed moments of time. In support of the theoretically obtained probabilistic distributions, the results of numerical simulations are provided.

More network operators have recently provided content delivery network (CDN) services, where traffic engineering techniques, such as the base station (BS) association, are jointly employed with content delivery. This deployment attempts to reduce the network operating cost and enhance the quality of service (QoS) of end users. Toward this end, we study the BS association and file caching problem considering the spatial diversity of the file popularity and the realistic time-scale separation between the file caching and the BS association decisions in this paper. Our design aims to minimize the file delivery latency and operating power consumption in the cellular networks, where the tradeoff between these conflicting objectives is controlled by a single parameter. The short time-scale BS association problem is solved by using the convex optimization technique for a given file caching solution. However, the long time-scale file caching problem considering the varying BS association decisions taken at the short time-scale is difficult to tackle. To solve this file caching problem, we prove and leverage the submodularity property of the underlying objective function to develop a greedy content caching algorithm that guarantees a constant approximation ratio of the optimal objective value. Via simulations using real-world datasets, we show that the proposed algorithms outperform file caching and BS association algorithms that do not consider the spatial diversity of the file popularity in terms of the power consumption and delay performance in the geographically heterogeneous file popularity scenario.

Multimedia streams consume a significant chunk of the consumer Internet traffic exchanged and will continue to do so due to the ever-increasing connection among people, businesses, and industries. To cope with the deviation of the Internet's intended use, unreliable underlying infrastructure, and best effort protocols while leveraging existing technologies, Hypertext Transfer Protocol Adaptive Streaming is utilized by numerous multimedia services. Performance of HAS-based streaming services is limited by the growing control overhead generated by the Transmission Control Protocol/Internet Protocol (TCP/IP) stack as the stream length, multimedia fidelity, and network conditions vary. In this paper, a novel cross-layer steganographic-enabled signaling scheme is proposed to reduce service provider costs while improving multimedia session performance and maintaining expected Quality-of-Service (QoS). The proposed scheme is designed to encode control stream messages from any TCP/IP layer within payload messages to reduce the total amount of overhead exchanged, thereby decreasing resource utilization within source and intermediate nodes. Furthermore, the encoding scheme probes network conditions and session statistics for adaptive decision-making to enable real-time pliability of the proposed process. A utility function is developed to find the optimal cost savings where simulations are conducted to verify the designs. The proposed solution is then implemented using VideoLan Media Player transceivers residing in linux containers virtual machines, where a multimedia file is exchanged in the popular Advanced Video Coding (H.264) format. The results show a decrease in bandwidth and average queue waiting time costs of 4.71% and 29.61%, respectively, with a throughput increase of 5.77%.

Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multiaccess edge computing, roadside units (RSUs) could become cache-capable and offer fast caching services to moving vehicles for content providers. On the other hand, deep learning makes it possible to accurately estimate the behavior of vehicles, which enables effective proactive caching strategies. However, caching services considering both the mobility of vehicles and storage could incur increased latency and considerable cost due to the cache size needed in RSUs. In this paper, we model such a problem using Markov decision processes, and propose a heuristic ${Q}$ -learning solution together with vehicle movement predictions based on a long short-term memory network. The optimal caching strategy which minimizes the latency of caching services can be derived by our heuristic ${\varepsilon }_{{n}}$ -greedy training processes. Numerical results demonstrate that our proposed strategy can achieve better performance compared with several baselines under different prediction accuracies.

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size $\boldsymbol K$ -means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.

LDM, a non-orthogonal multiplexing technology, is one of the main innovations in ATSC 3.0, a next generation terrestrial TV broadcast system. This article gives a general overview of the LDM technology, its current applications in the ATSC 3.0 system, and its cost in terms of the required additional complexity. The transmission capacity benefit offered by LDM is explained by theoretical analysis and demonstrated by simulation results. An efficient implementation scheme is described with less than 15 percent complexity increase. New service coverage paradigms enabled by LDM are presented. Finally, more future innovative applications of LDM, along with their benefits and challenges, are introduced. These include using LDM for wireless in-band backhaul, combining LDM with scalable video coding, and the application of LDM in other future broadcasting and 4G/5G broadband systems.

The performance of orthogonal frequency division multiplexing (OFDM) based wireless vehicular communication systems is faced with the great challenge of impulsive noise (IN), which could limit the application of OFDM in ultra-reliable low-latency communication scenarios. In this paper, the challenge of IN elimination for OFDM-based wireless systems is efficiently overcome by the proposed sparse learning algorithms and probabilistic framework inspired by the emerging machine learning theories. For the first time, the sparse machine learning theory is introduced to IN recovery and elimination. Exploiting the measurement vector of IN observed from the reserved null sub-carriers as the input, a novel sparse machine learning based algorithm of sparse cross-entropy minimization is proposed, in which the probability distribution of the IN support is iteratively updated by minimizing the loss function, i.e. the cross-entropy. The proposed algorithm is able to effectively and efficiently learn the sparse pattern and converge to the accurate distribution of IN support. To facilitate an accelerated and even more efficient learning process, regularization is imposed on the loss function by adding a weighting parameter in favor of the accurate distribution. The computer simulation results confirm that the proposed scheme outperforms conventional methods while utilizing fewer spectrum resources over wireless vehicular channels.

Relay identification is necessary in many cooperative communication applications such as detecting the presence of malicious relays for communication security, selecting the intended relays for signal forwarding, and tracing a specific relay. However, this identification task becomes extremely challenging for amplify-and-forward (AF) relaying systems since AF relays usually have no capability of adopting traditional identification methods implemented above the physical layer. This paper proposes a physical-layer AF relay identification scheme based on the exploitation of the device-specific in-phase and quadrature-phase imbalance (IQI) feature. Given that IQI estimation is mandatory in most present receivers for compensation, it is cost-effective to make use of these estimation results for fingerprinting AF relays. A generalized likelihood ratio test-based fingerprint differentiation technique is adopted to detect the minor difference between two range-limited IQI fingerprints. Using this differentiation technique, a whitelist-based identification algorithm consisting of fingerprint registration, update, and identification is proposed. Furthermore, the optimal training signals that lead to the maximal detection probability are derived for the typical quadrature amplitude modulation and phase-shift keying modulation schemes. The simulation results validate our derivations and confirm that the proposed method can accurately identify AF relays.

Layered-division-multiplexing (LDM) is a physical-layer non-orthogonal multiplexing technology, which has been accepted as a baseline technology by the next generation terrestrial digital TV (DTV) broadcasting standard, advanced television systems committee (ATSC) 3.0. The typical scenario for an LDM-based ATSC 3.0 system is to deliver a robust high-definition TV (HDTV) service in the higher-power core-layer (CL) for mobile reception and a 4k ultra high definition TV or multiple enhanced full HDTV (1080p) services in the lower-power enhanced layer (EL) for fixed reception. So far, the capability of using the EL of an LDM system to deliver mobile services has not been investigated and well-understood. In this paper, we investigate the potential of using both layers of an LDM-based ATSC 3.0 system to delivery multiple mobile services that target different types of mobile devices. The mobile performance of the LDM system is then compared to its time-division-multiplexing and frequency-division-multiplexing counterparts, which will reveal the application scenarios for which using LDM would provide performance advantages. In addition, the availability of high-throughput fixed services carried in the LDM-EL to mobile receivers is also investigated by computer simulations. An advanced detection algorithm is proposed based on the LDM-CL inter-carrier-interference cancellation, which is shown to provide significantly better mobile performance. Finally, we show by simulation that using receive antenna diversity combining techniques can significantly improve the service detection performance of LDM mobile receivers, especially for services carried in the LDM EL.