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Terahertz (THz) communications have naturally promising physical layer security (PLS) performance since narrow beams created by antenna arrays can effectively prevent eavesdroppers outside the beam from overhearing legitimate messages. However, eavesdroppers residing in the THz beam sector are still threatening even with the equipment of traditional multi-antenna techniques. This security challenge is referred to range security, since the range of the legitimate receiver and the eavesdropper differs. To address this problem, in this paper, a fundamental theoretical analysis on multiple-antenna techniques to provide range security is studied. Based on this, the frequency diverse array, as a candidate multiple-antenna technique, is proven ineffective in addressing the range security problem. Moreover, a widely-spaced array and beamforming design for THz range security is demonstrated. A non-constrained optimum approaching (NCOA) algorithm is proposed to achieve sub-optimal performance based on a THz-specific multiple-antenna array structure. Simulation results illustrate that our proposed widely-spaced antenna communication scheme can ensure a 6 bps/Hz secrecy rate when the transmit power is 10 dBm and the propagation distance is 10 m.

Graph Convolutional Networks (GCN) are a potent and adaptable tool for effectively processing and analyzing continuous spatial data. Despite the substantial potential of GCN in various domains, most existing spatial data prediction models are confined to defining weights solely based on distance. To overcome this limitation, this study proposes a novel approach to obtain the second-level embedding of Points of Interests (POIs) by employing Delaunay Triangulation (DT), Random Walk, and Skip-Gram model training. Subsequently, enhanced features are obtained through various aggregation strategies for regional embedding. The integrated grid data, including longitude and latitude coordinates, enhanced features, and target values, are then integrated. Finally, the GCN is utilized for training and fitting to achieve the final prediction target value. By considering the influence of weights on data prediction, this approach can more accurately reflect the distribution and relationships of data in the actual environment. Furthermore, we have experimentally validated the effectiveness of this approach, demonstrating that it significantly enhances the accuracy of spatial data prediction when compared to the original GCN model’s approach.

Terahertz (THz) communications with a frequency band 0.1 – 10 THz are envisioned as a promising solution to future high-speed wireless communication. Although with tens of gigahertz available bandwidth, THz signals suffer from severe free-spreading loss and molecular-absorption loss, which limit the wireless transmission distance. To compensate for the propagation loss, the ultra-massive multiple-input-multiple-output (UM-MIMO) can be applied to generate a high-gain directional beam by beamforming technologies. In this paper, a review of beamforming technologies for THz UM-MIMO systems is provided. Specifically, we first present the system model of THz UM-MIMO and identify its channel parameters and architecture types. Then, we illustrate the basic principles of beamforming via UM-MIMO and discuss the far-field and near-field assumptions in THz UM-MIMO. Moreover, an important beamforming strategy in THz band, i.e., beam training, is introduced wherein the beam training protocol and codebook design approaches are summarized. The intelligent-reflecting-surface (IRS)-assisted joint beamforming and multi-user beamforming in THz UM-MIMO systems are studied, respectively. The spatial-wideband effect and frequency-wideband effect in the THz beamforming are analyzed and the corresponding solutions are provided. Further, we present the corresponding fabrication techniques and illuminate the emerging applications benefiting from THz beamforming. Open challenges and future research directions on THz UM-MIMO systems are finally highlighted.

Customer churn prediction is a valuable task in many industries. In telecommunications it presents great challenges, given the high dimensionality of the data, and how difficult it is to identify underlying frustration signatures, which may represent an important driver regarding future churn behaviour. Here, we propose a novel Bayesian hierarchical joint model that is able to characterise customer profiles based on how many events take place within different television watching journeys, and how long it takes between events. The model drastically reduces the dimensionality of the data from thousands of observations per customer to 11 customer-level parameter estimates and random effects. We test our methodology using data from 40 BT customers (20 active and 20 who eventually cancelled their subscription) whose TV watching behaviours were recorded from October to December 2019, totalling approximately half a million observations. Employing different machine learning techniques using the parameter estimates and random effects from the Bayesian hierarchical model as features yielded up to 92% accuracy predicting churn, associated with 100% true positive rates and false positive rates as low as 14% on a validation set. Our proposed methodology represents an efficient way of reducing the dimensionality of the data, while at the same time maintaining high descriptive and predictive capabilities. We provide code to implement the Bayesian model at https://github.com/rafamoral/profiling_tv_watching_behaviour.

An intelligent driver assistance system is developed in this study, which is able to remind the drivers to turn on the head lights or wipers through situation recognition method when driving at night or on rainy days. Furthermore, the object detection results from multiple perspective views are integrated, and the surrounding object detection results are produced for collision avoidance. The system is able to alarm the drivers based on the lightweight deep learning model and the distance estimation method when surrounding vehicles are too close. Experimental results show that the proposed methods and the chosen lightweight model in our proposed system obtain reliable performance and sufficient computational efficiency under limited computing resource. In conclude, our proposed system obtains high probability to be adopted for the development of advanced driver assistance systems (ADAS). The proposed system can not only assist the driver in determining the vision ahead, but also provide an instant overview of the vehicle’s surrounding conditions to enhance driving safety.

Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification. However, most existing approaches focus on learning a single linear metric, which is unsuitable for nonlinear relationships and heterogeneous datasets with locality information. Besides, the hard samples in the training set account for only a small part, which may fail to characterize the global geometry of the metric embedding space. In this paper, we propose a novel deep multiple metric learning (DMML) method for time series classification. DMML contains a convolutional network component to extract nonlinear features of time series. For exploiting locality information, the last feature layer of the convolutional network is divided into several nonoverlapping groups and a separate metric learner is built on each group to get multiple metrics. In order to reduce the correlations among learners and facilitate robust metric learning, we design an adversarial negative generator to synthesize different hard negative complements for different metric learners. Moreover, an auxiliary loss is introduced to increase the robustness of DMML for the magnitude of distance. Extensive experiments on UCR datasets demonstrate the effectiveness of DMML for time series classification.

Effectively learning and extracting the feature representations of 3D point clouds is an important yet challenging task. Most of existing works achieve reasonable performance in 3D vision tasks by modeling the relationships among points appropriately. However, the feature representations are only learned with a specific transform through these methods, which are easy to overlap and thus limit the representation ability of the model. To address these issues, we propose a novel Multi-Transform Learning framework for point clouds (PointMTL), which can extract diverse features from multiple mapping transform to obtain richer representations. Specifically, we build a module named Multi-Transform Encoder (MTE), which encodes and aggregates local features from multiple non-linear transforms. To further explore global context representations, a module named Global Spatial Fusion (GSF) is proposed to capture global information and selectively fuse with local representations. Moreover, to guarantee the richness and diversity of learned representations, we further propose a Spatial Independence Criterion (SIC) strategy to enlarge the differences between the transforms and reduce information redundancies. In contrast to previous works, our approach fully exploits representations from multiple transforms, thus having strong expressiveness and good robustness for point clouds related tasks. The experiments on three typical tasks (i.e., semantic segmentation on S3DIS and ScanNet, part segmentation on ShapeNet and shape classification on ModelNet40) demonstrates the effectiveness of our method.

By analyzing and predicting the traffic states of urban road network, the formation of traffic congestion can be effectively alleviated, so as to improve the traffic capacity of urban road network. In this paper, firstly, we analyze and study the spatio-temporal correlation characteristics of traffic states based on the existing floating car data. At the same time, we extend the traffic conditions of urban road network from the upstream and downstream interaction to the global road network and complete the traffic congestion states discrimination of urban road network based on the spatio-temporal correlation. Secondly, according to the traffic jam aggregation and diffusion characteristics of local Moran's I, a mixed forest prediction method considering the spatio-temporal correlation characteristics of urban road traffic state is constructed by improving the existing random forest algorithm. Finally, an example is given to verify the effect of the prediction method on the short-term prediction of urban road network traffic states.

In this work, we propose a robust tracking algorithm based on context-aware correlation filter framework. In order to improve the richness of the feature representation, the proposed hyper-feature which contains linearly weighted mixture of hand-crafted features (such as HOG, color histogram) and hierarchical deep convolutional features (such as VGGNet). The final output response map is optimized by the Gaussian constrained optimization method to control the response map follow the Gaussian distribution, which gain the robustness to target appearance variations. In addition, in terms of model update, an efficient adaptive model updating method is proposed to suppress the model noises significantly. Extensive experimental results on well-known tracking benchmark datasets to evaluate the proposed algorithm. Experimental results demonstrate that the proposed algorithm performs favorably against many state-of-the-art methods in terms of success rate, accuracy, and robustness.

The fifth generation (5G) technology offers a set of security services in order to provide secure communication to a large number of various devices. 5G Authentication and Key Agreement (5G-AKA) authentication service acts as the first step of securing the 5G network communication. This paper aims to quantitatively analyze the dependability of 5G-AKA authentication service. We develop the continuous-time Markov chain (CTMC) models to capture the operational details of the 5G-AKA authentication services when facing the service failure. We also derive formulas for calculating metrics of interest, including Defects Per Million (DPM), authentication service's first restoration time from failure and total cost of ownership (TCO). Extensive numerical analysis is applied to investigate the impact of parameters on the evaluation metrics.

Terahertz (THz) communications recently attract significant attention and become an emerging technology pillar for sixth generation (6G) wireless systems. Due to the serious path attenuation of THz signals, THz communication is applicable for the short-distance indoor scenarios. However, the THz waves are easily blocked by obstacles, leading to a communication interruption. To this end, an intelligent reflecting surface (IRS), which interacts with incident THz waves in a controlled manner by adjusting the discrete phase shifts of the IRS elements, is considered as a promising technology to mitigate blockage vulnerability and enhance coverage capability for indoor scenarios. In light of graphene-enabled hardware structure of an IRS, the IRS-assisted THz multiple-input multiple-output (MIMO) system model is developed. Moreover, an iterative atom pruning based subspace pursuit (IAP-SP) scheme is developed for channel estimation. Compared to the classical subspace pursuit (SP) scheme, the proposed IAP-SP algorithm can substantially reduce the computational complexity while maintaining accurate channel recovery. With the estimated channel, a data rate maximization problem is formulated, which can be converted to a discrete phase shift search problem. The exhaustive search method is firstly proposed to obtain the optimal transmission rate but endure extremely high computational burden. Then, a local search method is proposed to decrease the number of possible discrete phase candidates of IRS while undergoes obvious performance loss. Interestingly, a novel feedforward fully connected structure based deep neural network (DNN) scheme is put forward, which has the ability to learn how to output the optimal phase shift configurations by inputting the features of estimated channel. Simulation results demonstrate that, in contrast with the exhaustive search scheme and the local search scheme, the proposed DNN-based scheme achieves a near-optimal communication rate performance. Meanwhile, the DNN-based scheme enormously alleviates the computational complexity and allows for dynamic parameter adaption in rapid-varying channel conditions.

A compact circularly polarized (CP) spiral antenna with enhanced impedance and 3-dB axial ratio (AR) bandwidths that is fed by a tapered coax balun is presented. The antenna is composed of a two-arm nonself-complementarity planar equiangular spiral, a z-directional helix, absorbing material with a stepped cylindrical honeycomb structure, and a ground plane. The z-directional helix is used to extend the current path and to improve the impedance matching and AR in the low-frequency band. The coupling between the nonself-complementarity planar spiral arms is increased, which improves the impedance matching in the low-frequency band and the AR at approximately 5.55 GHz. The stepped cylindrical absorbing material can reduce the reverse current and improve the AR in the high-frequency band. The measured results show that the impedance bandwidth and 3-dB AR bandwidths range from 0.5 to 6 GHz and from 0.5 to 5.8 GHz, respectively. The overall size of the proposed antenna is 0.189λL × 0.065λL (diameter × height, where λL is the free-space wavelength at the starting frequency).

Moving target defense (MTD) has recently emerged as a game-changer in the confrontation between cyberattack and defense. MTD mechanism constantly and randomly changes the system configurations to create uncertainty of the attack surface against cyber-adversaries. To date, researches on the evaluation of MTD techniques either focused on analyzing the effectiveness of MTD or studying the system performance loss due to the use of MTD. The impact on job/service running on the protected system is always ignored. In this paper, we propose an SRN (Stochastic reward net) based analytical modeling approach to investigate how MTD techniques influence the job running on protected system from the perspective of job finish time. The SRN model developed in this paper captures the behaviors of both the adversary and the job execution process. Furthermore, we carry out numerical analysis to study the impact of different system parameters on job finish time and other evaluation metrics. The results in this paper can help defenders choose a better MTD configuration to complete the job execution as soon as possible.

One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. In existing massive MIMO systems, however, conventional phased arrays are used for beamforming. This method results in excessive power consumption and high hardware costs. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we develop a new type of high-gain yet low-cost RIS that bears 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. This radical design forms the basis for the world's first wireless communication prototype using RIS having 256 two-bit elements. The prototype consists of modular hardware and flexible software that encompass the following: the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in wireless communications. We show that, at 2.3 GHz, the proposed RIS can achieve a 21.7 dBi antenna gain. At the millimeter wave (mmWave) frequency, that is, 28.5 GHz, it attains a 19.1 dBi antenna gain. Furthermore, it has been shown that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.

With the rapid development of the social economy, our lives are flooded with all kinds of counterfeit products. The public's attitude of greedy for petty and cheap has encouraged unscrupulous manufacturers to take advantage of the opportunity to provide low-cost counterfeit products, suppress the profits of legitimate manufacturers, and also make the public lose confidence in the quality of the products. At present, the most widely used anti-counterfeiting system based on QR codes on the market. However, existing traceability systems are still mostly built in a centralized manner, and the central agency provides trust guarantees, but the public still has great doubts about the credibility of the central agency. The introduction of blockchain technology can perfectly solve the lack of existing architecture and the environment. In this research, we propose an IoT-based traceable drug anti-counterfeiting management system, a comprehensive plan from drug research and development, certification, production to sales. The framework we propose meets the requirements of information security for data integrity, resistance to replay attacks, irreversible information, and non-repudiation.

This paper deals with the stability, stabilization and L2- gain problem of periodic piecewise impulsive linear system. Firstly, by developing a new Lyapunov function called multiple piecewise time-varying Lyapunov function, a sufficient condition that guarantee the system λ* - exponentially stable is established. Moreover, a state-feedback controller is designed to stabilize the system. Then L2- gain analysis is investigated to obtain a result that guarantee the system λ* - exponentially stable and with L2- gain performance. Finally, numerical simulation examples are provided to verify the effectiveness of the results proposed in this paper.

Interconnectivity is an important development trend of future energy revolution. A more precise planning model is needed to enhance the interaction of multiple energy sources. In this paper, a joint planning model of active distribution network and transportation network including electricity, gas, heat, and traffic loads is proposed. The main highlights of this model are summarized below. Firstly, this issue puts forward a novel mixed user equilibrium mathematical model nesting fast charging station user equilibrium considering the charging fee, charging time, and queuing time of various charging facilities. Secondly, this work constructs a new generation of suburban integrated energy system (SIES) model considering electricity, biogas, gas, and heat energy, and fully explores the significant role of biogas digestors in a SIES. Thirdly, because the proposed model is a mixed integer nonlinear problem, the piecewise linear method, big-M method, and second-order cone relaxation are used to deal with the nonlinear constraints. Finally, a SIES based on the IEEE 33-node distribution network and a 12-node traffic network is designed for case studies. The results revealed that the new equilibrium will decrease vehicle charging cost strongly up to 19.58%. Moreover, biogas digesters are conducive to new energy consumption, resulting in the proportion of power supply from the upper-level grid falling to 30.87%.

Optical stopping can serve as a light-controlled solution to implementing photonic components. In contrast to saturated optical stopping that provides enough excited electrons to entirely absorb signal lights, unsaturated optical stopping introduces a photon competition mechanism for signal lights and pump lights due to insufficient excited electrons. This photon competition mechanism produces a kind of regular signals, which is very similar to the signals applied in pulse-coupled neural network. However, due to its unclear physical mechanism, it is impossible to further improve performances of the unsaturated optical stopping-based components. Here, we propose a kinetic model to reveal physical mechanism of the unsaturated optical stopping and establish a corresponding formalism that enables calculation of time response of the unsaturated optical stopping-based amorphous arsenic sulfide thin-film waveguides. Our work provides a new insight into the physical phenomenon of unsaturated optical stopping and builds a theoretical foundation for further research on response characteristics of amorphous arsenic sulfide thin-film waveguides.

This paper is concerned with the spectrum sensing problem for cognitive radio networks with correlated receiving multiple antennas in the time-varying fading channel. We first consider the scenario that all the antennas have the same noise variance and present a generalized real-valued weighted-covariance-based detection (GRWCD) method. In particular, we derive the distribution of the GRWCD statistic under the null hypothesis, which allows us to develop the theoretical decision threshold for a given false alarm probability. Besides, we derive the distribution of the GRWCD statistic under the alternative hypothesis, which enables us to provide a mathematical expression for the detection probability as well as the theoretical receiver operating characteristic. Meanwhile, we consider a more general scenario of unequal per-antenna noise variances and present a modified GRWCD method as well as the theoretical expressions of the decision threshold. The simulation results are provided to verify the accuracy of the derived results and show that the proposed two methods are capable of providing performance improvement over several advanced methods in the literature.

From the viewpoint of physical-layer authentication, spoofing attacks can be foiled by checking channel state information (CSI). Existing CSI-based authentication algorithms mostly require a deep knowledge of the channel variation to deliver decent performance. In this paper, we investigate CSI-based authenticators that can spare the effort to predetermine channel properties by utilizing deep neural networks (DNNs). First, we propose a convolutional neural network (CNN)-enabled authenticator that is able to extract the local features in CSI. Next, the recurrent neural network (RNN) is employed to capture the dependencies between different frequencies in CSI. In addition, we propose to use the convolutional recurrent neural network (CRNN)-a combination of the CNN and the RNN-to learn local and contextual information in CSI for user authentication. Finally, experiments based on Universal Software Radio Peripherals (USRPs) are conducted to demonstrate the performance of the proposed methods on real-world channel estimates. According to the experimental results, the proposed DNNs-enabled schemes can significantly outperform the dynamic time warping (DTW) technique and a heuristic Neyman-Pearson (NP) test in the aspects of false alarm and miss detection. Besides, the hybrid of the CNN and the RNN can further promote the authentication accuracy.

This paper presents a control strategy named auxiliary surfaces sliding mode control (AS-SMC) by using Positive Invariant Set (PIS) to control a class of continuous nonlinear systems with state constraints. The PIS can be regarded as a special kind of sliding surface, and its invariance ensures that the system state can satisfy the constraints in the convergence process. The stability analysis and a PIS theory proof are given. The control strategy is successfully tested in numerical simulations and even effectively applied to the coaxial unmanned helicopter flight control system on hardware in the loop (HIL) platform. The results verify the effectiveness of the proposed control strategy for the experimental set-up.

In real-time wireless control systems, ultra-reliable and low-latency communication (URLLC) is critical for the connection between the remote controller and its control objective. Since both transmission delay and packet loss can lead to control performance loss, our goal is to optimize control performance by jointly considering control and URLLC constraints in this paper. To achieve this goal, we formulate an optimal problem to minimize control cost by optimizing the packet drop and wireless resource allocation. To solve the problem, we analyze the relationship between communication and control. Then, based on the relationship, we decompose the original problem into two subproblems: 1) an optimal packet-drop problem to minimize control cost and 2) an optimal resource allocation problem to minimize communication packet error. Finally, the corresponding solutions for each subproblem can be obtained. Compared with the traditional method only considering the communication aspect, the proposed packet-drop and resource allocation method shows remarkable performance gain in terms of control cost.

Localizing noncooperative transmitter (Tx) and receiver (Rx) that belong to another system is important in many scenarios, e.g., interference management in cognitive radio systems and user behavior learning in ad hoc wireless networks. However, obtaining the locations of these nodes in particular in frequency-division duplex systems is challenging, since the localization network usually does not know the spectrum that the Rx uses for backward transmission. In this paper, we propose to use the full-duplex relay technique to localize a noncooperative Rx, which does not require the knowledge of the Rx’s backward transmission spectrum. In the proposed method, localization sensors alternatively act as a full-duplex amplify-and-forward relay to trigger the power control of the Tx–Rx link. Then, by detecting the power adjustment of the Tx, each localization sensor can estimate the time difference of arrival between the direct and relay signals. Finally, the Rx location can be calculated from triangulation. Simulation results show that the proposed method can effectively localize the Rx, which validates its potential for receiver-aware applications and services.

High transmission rate and secure communication have been identified as the key targets that need to be effectively addressed by fifth generation wireless systems. In this context, the concept of physical-layer security becomes attractive, as it can establish perfect security using only the characteristics of wireless medium. Nonetheless, to further increase the spectral efficiency, an emerging concept, termed physical-layer service integration (PHY-SI), has been recognized as an effective means. Its basic idea is to combine multiple coexisting services, i.e., multicast/broadcast service and confidential service, into one integral service for one-time transmission at the transmitter side. This article first provides a tutorial on typical PHY-SI models. Furthermore, we propose some state-of-the-art solutions to improve the overall performance of PHY-SI in certain important communication scenarios. In particular, we highlight the extension of several concepts borrowed from conventional single-service communications, such as artificial noise, eigenmode transmission, to the scenario of PHY-SI. These techniques are shown to be effective in the design of reliable and robust PHY-SI schemes. Finally, several potential research directions are identified for future work.

We consider the problem of spectrum sharing in a cognitive radio system consisting of a primary user and a secondary user. The primary user and the secondary user work in a non-cooperative manner. Specifically, the primary user is assumed to update its transmitted power based on a pre-defined power control policy. The secondary user does not have any knowledge about the primary user's transmit power, or its power control strategy. The objective of this paper is to develop a learning-based power control method for the secondary user in order to share the common spectrum with the primary user. To assist the secondary user, a set of sensor nodes are spatially deployed to collect the received signal strength information at different locations in the wireless environment. We develop a deep reinforcement learning-based method, which the secondary user can use to intelligently adjust its transmit power such that after a few rounds of interaction with the primary user, both users can transmit their own data successfully with required qualities of service. Our experimental results show that the secondary user can interact with the primary user efficiently to reach a goal state (defined as a state in which both users can successfully transmit their data) from any initial states within a few number of steps.

Mobile relaying is emerged as a promising technique to assist wireless communication, driven by the rapid development of unmanned aerial vehicles. In this paper, we study secure transmission in a four-node (source, destination, mobile relay, and eavesdropper) system, wherein we focus on maximizing the secrecy rate via jointly optimizing the relay trajectory and the source/relay transmit power. Nevertheless, due to the coupling of the trajectory designing and the power allocating, the secrecy rate maximization problem is intractable to solve. Accordingly, we propose an alternating optimization approach, wherein the trajectory designing and the power allocating are tackled in an alternating manner. Unfortunately, the trajectory designing is a nonconvex problem, and thus, it is still hard to solve. To circumvent the nonconvexity, we exploit sequential convex programming to derive an iterative algorithm, which is proven to converge to a Karush-Kuhn-Tucker point of the trajectory design problem. The simulation results demonstrate the efficacy of the joint power and trajectory design in improving the secrecy throughput.

We present a design of a single-frequency green InGaN external cavity diode laser (ECDL) with 40-mW output power and 10-MHz linewidth. The ECDL was mounted in the Littrow configuration, with an overall efficiency of approximately 52% and a tuning region of 8 nm. We analyzed the output efficiency of the ECDL under different polarization directions and emitted light patterns. We used a spectrometer to analyze the laser spectrum under the different applied currents and temperatures. Finally, we obtained the linewidth of the ECDL using a scanning Fabry-Perot interferometer and found that the linewidth varied with the wavelength.

We propose and experimentally demonstrate photonic measurement of microwave frequency by utilizing an optomechanical microring resonator (MRR). The pump powers injected into the MRR would result the resonance red-shifts based on nonlinear effects. In the case of optical single sideband modulation, the frequency intervals between the optical carrier and the corresponding MRR resonance are tunable. Then microwave photonic filters with tunable central frequency could be achieved. Through using two microwave photonic filter responses with different central frequencies, an amplitude comparison function (ACF) (i.e., microwave power ratios) could be obtained. The microwave frequency could be calculated through the frequency-to-power mapping in the monotonic increasing region of the ACF. Moreover, the frequency resolution could be effectively improved by multiple measurement steps, so as to reduce the measurement errors. In the experiment, the microwave frequency could be measured from 8 to 18 GHz with errors lower than 0.035 GHz. The required highest pump power is low as -3.9 dBm. In the future, the measurement range could be largely increased by employing vector network analyzer with a larger bandwidth. The significant optomechanical device and measurement method are competent to measure microwave frequency with dominant advantages, such as all-optical control, compact footprint, low-error, and low-power consumption.

This paper studies the throughput maximization problem for a three-node buffer-aided relay channel with non-ideal circuit power. In particular, the half-duplex decode-and-forward relaying scheme is adopted. Moreover, the buffers at the relay own the capability to temporarily store the data received from the source. The throughput maximization problem over infinite time horizon for the considered system is investigated, considering the extra power consumption by the circuits during transmissions. The optimal power allocation is obtained by examining two cases on whether the relay can support the source transmissions, and is then shown to be with an “on-off” structure, i.e., the source and the relay transmit with certain probabilities, respectively. Next, the dynamics of the stored data at the relay is modeled under the derived optimal power allocation, and the minimum buffer size required for data storage is quantified. Finally, numerical results validate the analysis.

Content-centric networking (CCN) aims to improve network reliability, scalability, and security by changing the way that information is organized and retrieved in the current Internet. One critical issue in CCN is in-network cache allocation. It is known that in-network caching mechanisms are vulnerable to distributed denial of service attacks, especially to pollution attacks. That is, a caching mechanism under pollution attacks cannot work well. The past years witnessed kinds of proposals of cache allocation mechanisms. However, none of them could effectively allocate in-network cache while defending against attacks. In this paper, we propose a lightweight non-collaborative cache allocation approach (IFDD), which could not only enhance in-network caching performance in terms of the cache hit ratio and the request processing delay, but also defend against pollution attacks. By lightweight, we mean that IFDD generates low communication overhead (due to non-collaboration) and computational overhead at routers. The key idea behind IFDD is to combine the content popularity with the content locality in making caching decision. Extensive simulation results on ndnSIM platform demonstrate the capability of the proposed approach in improving cache allocation performance while reducing the impact of pollution attacks.