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This work investigates the security issue of the energy-constrained untrusted relay network with imperfect channel reciprocity, where the relay without the built-in power supply can only scavenge energy from radio-frequency signals radiated by the source and destination. A hybrid power-splitting (PS)- and time-switching (TS)-based relaying (HPTR) protocol is presented to improve the degraded secrecy performance due to the incomplete self-interference cancellation at the destination. To evaluate the secrecy throughput (ST) of the studied system, the analytical expression of the secrecy outage probability under the delay-limited transmission mode and the closed-form lower bound of the ergodic secrecy capacity under delay-tolerant transmission mode are derived. Both linear and nonlinear models for the energy harvester at the relay are compared. The optimal PS and TS ratios are evaluated numerically. The theoretical derivations are validated by numerical results, revealing that the residual jamming has a negative effect on the secrecy performance of untrusted relay networks, which can be alleviated by the HPTR protocol. Besides, we compare the ST performance of the HPTR protocol with that of the PS and TS relaying schemes, and the results show that the HPTR protocol outperforms both PS and TS relaying protocols in terms of the ST.

Simultaneous wireless information and power transfer (SWIPT) is a major breakthrough in the field of low-power wireless information transmissions. In this paper, the secrecy performance of the SWIPT-enabled relay network with full-duplex destination-aided jamming is assessed, where both the power-splitting (PS) and time-switching (TS) schemes at the relay are considered with the linear and nonlinear energy harvesting models. The relay harvests energy from the confidential signal and artificial noise sent by the source and destination, respectively, and forwards the amplified signal to the destination, in the presence of an eavesdropper. The analytical closed-form expressions of the connection outage probability (COP), secrecy outage probability (SOP), and transmission outage probability (TOP) for PS- and TS-based schemes are derived, and the closed-form expression of the lower bound of ergodic secrecy capacity (ESC) is calculated. The asymptotic-form expressions of the COP, SOP, TOP, and ESC are further analyzed to capture the valuable information in the high SNR regime. Numerical results verify the correctness of analytical results, reveal the effects of the PS/TS ratio, and transmit the signal-to-noise ratio on secrecy performance.

The simultaneous wireless information and power transfer (SWIPT) has recently attracted much attention since both information and energy are integrated within radio frequency signals, which is considered to be one of the most important technologies for future networks. However, security becomes one of the most critical issues in SWIPT networks due to its broadcasting features over wireless media and the transferring requirements for strong signal strength. To improve the security of SWIPT networks, in this paper, a SWIPT system enabled by full-duplex (FD) jamming is proposed, where an FD energy-limited receiver with the power splitting (PS) structure, powered by a transmitter, generates jamming signals. Both linear and nonlinear energy harvesting (EH) models are considered. To evaluate the secrecy throughput (ST) of the system, we derive the closed-form connection outage probability and secrecy outage probability for the proposed PS-based scheme in the delay-constrained transmission (DCT) mode. The ST is maximized by optimizing the PS ratio based on the asymptotic cases of the high signal-to-noise ratio as well as the perfect self-interference cancellation (SIC). Besides, the exact integral forms of the ergodic secrecy capacity and their closed-form lower bounds are presented for both the PS-based and the time switching (TS)-based schemes in the delay-tolerant transmission (DTT) mode. Simulation results suggest that the PS-based scheme outperforms the TS-based scheme for the DTT mode. For the DCT mode, the PS-based scheme can achieve better performance than the TS-based scheme only in low target communication rate, small PS/TS ratio, high transmit power, and weak path loss. Meanwhile, given a tolerable amount of SIC, the ST obtained in the linear EH model is higher than that achieved in the nonlinear EH model.

This work investigates the proactive eavesdropping through a friendly full-duplex (FD) relay and a legitimate monitor over the wireless-powered suspicious communication network, where one suspect source performs energy harvesting (EH) from one dedicated power beacon and then communicates with one suspect destination. Under this setup, a relay-aided proactive eavesdropping scheme is designed to improve the surveillance performance of the system. The closed-form expressions are derived under linear and nonlinear EH models, including the decoding outage probability for the suspicious link, the eavesdropping outage probability for the eavesdropping link, and the average eavesdropping rate (AER). Based on these obtained expressions, an optimization problem to maximize the AER is formulated by jointly optimizing the power and location of the relay. The existence of the optimal solution is carefully analyzed. Moreover, the separate optimization issues of the power and location are first established, and then a bisection-based searching algorithm is presented for the joint optimization problem with the linear EH model. Furthermore, to reduce the complexity of joint optimization, a low-complexity learning-based iteration algorithm is further proposed by employing the well-fitting characteristic of the deep feedforward neural network. Numerical results verify the effectiveness of the proposed algorithm, and show that the optimized FD relay-aided proactive eavesdropping scheme outperforms the reference schemes. Finally, the application scenarios of the proposed proactive eavesdropping scheme are discussed.

Due to the widespread use of Android devices and apps, it is important to develop tools and techniques to improve app quality and performance. Our work focuses on a problem related to hardware sensors on Android devices: the failure to disable unneeded sensors, which leads to sensor leaks and thus battery drain. We propose the Sentinel testing tool to uncover such leaks. The tool performs static analysis of app code and produces a model which maps GUI events to callback methods that affect sensor behavior. Edges in the model are labeled with symbols representing the acquiring/releasing of sensors and the opening/closing of UI windows. The model is traversed to identify paths that are likely to exhibit sensor leaks during run-time execution based on two context-free languages over the symbol alphabet. The reported paths are then used to generate test cases. The execution of each test case tracks the run-time behavior of sensors and reports observed leaks. This approach has been applied to both open-sourced and closed-sourced regular Android applications as well as watch faces for Android Wear smartwatches. Our experimental results indicate that Sentinel effectively detects sensor leaks, while focusing the testing efforts on a very small subset of possible GUI event sequences.

The issues of the physical layer security are studied, where one transmitter (Alice), one full-duplex (FD) legitimate receiver (Bob) and one multi-antenna jammer (Charlie) are considered to defend against multiple passive and non-colluding eavesdroppers (Eves), for multiple-input multiple-output wiretap channels. With the assumption that the complete channel state information (CSI) for main channels and the statistical CSI for Eves' channels are available, an improved secrecy transmission design is proposed to minimise the secrecy outage probability (SOP), under the minimum secrecy rate requirement. In addition to integrating artificial noise aided transmit beamforming and multi-antenna cooperative jammer to impair Eves' channels, which is widely used in many existing works, a FD legitimate receiver is applied together to further enhance physical layer security. Based on the basic probability theory and convex optimisation, the accurate closed-form expression of the SOP is derived, and the impacts on secrecy performance are described, including the qualities of main channels, the Alice's transmit power and the number of Eves. Numerical results show that the proposed scheme using the FD Bob can significantly enhance the system security, as compared with that employing the half-duplex Bob.

Simultaneous wireless information and power transfer (SWIPT) is a promising technique for energy-constrained wireless networks. To improve the security of SWIPT-enabled relay networks, in this paper, a full-duplex (FD) destinationaided jamming strategy is proposed, where a power splitting (PS) scheme is considered and two transmission phases are designed. In the first phase, the energy-limited half-duplex relay receives the signal superposed by confidential information and artificial noise (AN) from the source and destination, respectively. And then the FD destination transmits AN to the passive eavesdropper and receives the signal from the relay concurrently in the second phase. To maximize the secrecy capacity (SC), the individual optimizations of the PS ratio and power allocation (PA) coefficient are investigated, and the approximate optimal closed-form solutions of PS and PA ratios are derived. Besides, an iterative algorithm is also proposed to obtain the global optimal SC by updating the PS and PA ratios alternately. Simulation results confirm that the theoretical PS and PA ratios coincide closely with the exact results, and demonstrate that the optimal SC obtained by the iterative algorithm matches well with that given by the exhaustive search.

Continuous phase modulation (CPM) is a promising modulation scheme, due to its constant envelope and compact spectrum. However, the application of CPM is limited by the demodulation and the strict requirements of synchronization. A novel method based on the convolution neural network (CNN) is proposed for binary partial response CPM signals, where the structure of the neural network is designed according to the traditional demodulation processing. Specifically, the convolution kernels are applied to extract the high-dimensional features, which are different from the branch metrics calculated by the matched filters and phase rotation. And then the extracted features are mapped in the fully-connected layers, which plays the same role as the Viterbi decoder. Besides, the moving step of the convolution kernels is small, so that the extracted features can obtain more information than the branch metrics, even though there are some timing errors. Our numerical evaluations demonstrate that the performance of the proposed method approaches that of the theoretical optimal method. Moreover, the designed network is robust to normalized timing variance with no extra training.

The in-phase/quadrature (I/Q) branches imbalance leads to mirror subcarrier interference and worsens the performance of zero-intermediate frequency (zero-IF)-based orthogonal frequency division multiplexing (OFDM) systems. To tackle the signal detection issue of in-phase/quadrature imbalance (IQI) at the transceiver, a deep learning-based approach is proposed by using the convolutional neural network. Specifically, the network model and parameters are well-designed based on the features of the channel impulse response and the mirror interference of IQI. To verify the designed model, it is first trained by simulated data under the off-line training and then used directly to recover the on-line transmitted data. The simulation results demonstrate that the proposed method shows excellent performance in processing OFDM signals under the case of IQI and it is more robust than traditional methods even without cyclic prefix.

This work investigates a relay-aided proactive eavesdropping network, where a legitimate monitor eavesdrops on communication between suspicious users via a friendly amplify-and-forward full-duplex (FD) relay. The closed-form expressions are derived, including the decoding outage probability for the suspicious link, the eavesdropping non-outage probability for the eavesdropping link, and the average eavesdropping rate (AER). To maximize the AER, separate optimization problems for the power and location of the relay are settled, employing the good fitting characteristic of the deep feedforward neural network. Then, a low-complexity learning-based iteration algorithm is proposed to solve the joint optimization problem. Numerical results demonstrate the effectiveness and optimality of the proposed algorithm, and show that the optimized FD relay-aided proactive eavesdropping scheme outperforms the existing benchmark schemes in terms of the AER.

This paper studies a legitimate proactive eavesdropping scenario where a central monitor covertly wiretaps the communications between suspicious users through a full-duplex multi-antenna relay. Specifically, the suspicious information is intercepted by the eavesdropping relay, and then encoded and forwarded to the central monitor. At the same time, the relay sends jamming signals to the suspect destination to reduce the quality of suspicious communication. To minimize the suspicious secrecy rate, the optimal forwarding and jamming beamforming vectors of the eavesdropping relay are first designed. Based on this optimized result, the best power allocation ratio is obtained. To this end, the optimal beamforming vectors and power allocation ratio are derived in closed-form. The simulation results verify the existence of the optimal forwarding power of the relay and show that the proposed scheme achieves better performance than the conventional schemes.

Legitimate wireless surveillance plays an important and preventive role in protecting public safety. To promote the eavesdropping performance of wireless surveillance systems, a proactive eavesdropping scheme with a decode-and-forward relay is proposed, where the relay intercepts suspicious information and interferes with the suspicious receiver simultaneously. The closed-form expressions are derived, including the decoding outage probability, eavesdropping outage probability, and average eavesdropping rate. To maximize the average eavesdropping rate, the transmit power and location deployment at the relay are optimized. Specifically, the closed-form result of approximate optimal transmit power for the relay is obtained, and the bisection-based algorithm is further proposed to verify its correctness. Simulation results validate that the proposed scheme achieves better eavesdropping performance than that without optimization.

In this article, the three-dimensional trajectory tracking control of an autonomous underwater vehicle is addressed. The vehicle is assumed to be underactuated and the system parameters and the external disturbances are unknown. First, the five degrees of freedom kinematics and dynamics model of underactuated autonomous underwater vehicle are acquired. Following this, reduced-order linear extended state observers are designed to estimate and compensate for the uncertainties that exist in the model and the external disturbances. A backstepping active disturbance rejection control method is designed with the help of a time-varying barrier Lyapunov function to constrain the position tracking error. Furthermore, the controller system can be proved to be stable by employing the Lyapunov stability theory. Finally, the simulation and comparative analyses demonstrate the usefulness and robustness of the proposed controller in the presence of internal parameter uncertainties and external time-varying disturbances.

In this paper, a turning circle based trajectory planning method (TCBTPM) is proposed to conduct trajectory planning of an underactuated autonomous underwater vehicle (AUV) for mobile docking mission. Firstly, the six degrees of freedom kinematics and dynamics model of underactuated AUV is acquired. Following this, the simulation of horizontal turning circle trial is studied and the overview of mobile docking mission is introduced. Furthermore, TCBTPM is used to predict the relative position of AUV and the mobile docking station (MDS) in advance and generate a feasible trajectory for the mobile docking mission. Finally, a mobile docking guidance system containing TCBTPM, pure pursuit (PP) guidance law and line-of-sight (LOS) guidance law is employed to obtain desired guidance commands and guides the underactuated AUV to dock efficiently and reliably.

In this paper, the joint communication and computation resource allocation problem for a single cell massive MIMO based mobile edge computing (MEC) network is discussed. In particular, we study the joint optimization of the pilot transmission power, data transmission power and the computing resource at the MEC server with the target of minimizing the maximal offloading delay over multiple users. Due to the non-convexity of the formulated problem and the defined optimal solution conditions, two suboptimal algorithms, i.e., the external point iteration and gradient descent based penalty function algorithm (EPF-GDA) and the external penalty function iteration based fruit fly optimization algorithm (EPF-FOA), are proposed to obtain the suboptimal solutions. The performance of the proposed algorithms, such as the users’ offloading delay, is verified by numerical simulations. And our results confirm the great performance gain of the algorithm EPF-FOA over EPF-GDA under different circumstances.

Two key unsolved problems faced by frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR), namely, spatial-variant linear range cell migration (RCM) and range frequency aliasing in a traditional frequency scaling (FS) algorithm, are mitigated in this paper. For FMCW-SAR platform, it usually operates at low altitude space for which the squint angle is range dependent. This range-dependent squint angle in return causes serious spatial-variant linear RCM that deteriorates image quality if it is not properly handled. To focus squint FMCW-SAR, FS is normally utilized. This operation, however, creates an issue, range frequency aliasing that needs to be addressed as well. To overcome these two problems, in this paper, based on the modified inverse Chirp-Z transform (ICZT) and series inversion, a novel imaging algorithm performing the spatial-variant linear RCM correction and eliminating the range frequency aliasing for focusing squint FMCW-SAR is proposed. First, the influences of the range-dependent squint angle on imaging quality are analyzed. Based on the obtained analysis result, a new scaling factor containing the linear RCM spatial variance correction is designed, and then, an efficient modified ICZT is developed to remove the range-dependent RCM and frequency aliasing. As a result, the well-focused SAR image is produced. The proposed method can be efficiently implemented, since it only requires fast Fourier transform and no interpolation is demanded. Finally, the experimental results with simulated data demonstrate the effectiveness of the proposed algorithm.

In this paper, a modified proximate time-optimal control (PTOC) is proposed based on reduced order extended state observer (RESO) and controller scaling method, for the heading control of underactuated autonomous underwater vehicles (AUV) with input nonlinearities. First, a simple and practical modified ADRC is proposed for heading control with input nonlinearities. The non-symmetric dead-zone with unknown parameters is modeled as a combination of linear input and bounded disturbances, and the RESO is designed to estimate and compensate it. The influence of actuator saturation constraint is reduced by limiting the control signal before it goes into the RESO. Then, a parameter self-tuning strategy is proposed for the feedback controller with the changed control gain by the controller scaling method. The control gain is quadratic respect to the velocity of AUV, the active disturbance rejection control (ADRC) controller parameters can be adjusted based on the AUV velocity using the strategy. And finally, the modified PTOC, based on RESO and with an updating strategy for the limit of linear region, is proposed to improve the control performance. Several simulation experiments are carried out, the effectiveness of self-tuning modified ADRC is verified, and the proposed modified PTOC shows better performance.

This work considers watch faces for Android Wear devices such as smartwatches. Watch faces are a popular category of apps that display current time and relevant contextual information. Our study of watch faces in an app market indicates that energy efficiency is a key concern for users and developers. The first contribution of this work is the definition of several energy-inefficiency patterns of watch face behavior, focusing on two energy-intensive resources: sensors and displays. Based on these patterns, we propose a control-flow model and static analysis algorithms to identify instances of these patterns. The algorithms use interprocedural control-flow analysis of callback methods and the invocation sequences of these methods. Potential energy inefficiencies are then used for automated test generation and execution, where the static analysis reports are validated via run-time execution. Our experimental results and case studies demonstrate that the analysis achieves high precision and low cost, and provide insights into potential pitfalls faced by developers of watch faces.

Due to the widespread use of Android devices and apps, it is important to develop tools and techniques to improve app quality and performance. Our work focuses on a problem related to hardware sensors on Android devices: the failure to disable unneeded sensors, which leads to sensor leaks and thus battery drain. We propose the Sentinel testing tool to uncover such leaks. The tool performs static analysis of app code and produces a model which maps GUI events to callback methods that affect sensor behavior. The model is traversed to identify paths that are likely to exhibit sensor leaks during run-time execution. The reported paths are then used to generate test cases. The execution of each test case tracks the run-time behavior of sensors and reports observed leaks. Our experimental results indicate that Sentinel effectively detects sensor leaks, while focusing the testing efforts on a very small subset of possible GUI event sequences.

Android apps often contain third-party libraries. For many program analyses, it is important to identify the library code in a given closed-source Android app. There are several clients of such library detection, including security analysis, clone/repackage detection, and library removal/isolation. However, library detection is complicated significantly by commonly-used code obfuscation techniques for Android. Although some of the state-of-the-art library detection tools are intended to be resilient to obfuscation, there is still room to improve recall, precision, and analysis cost. We propose a new approach to detect third-party libraries in obfuscated apps. The approach relies on obfuscation-resilient code features derived from the interprocedural structure and behavior of the app (e.g., call graphs of methods). The design of our approach is informed by close examination of the code features preserved by typical Android obfuscators. To reduce analysis cost, we use similarity digests as an efficient mechanism for identifying a small number of likely matches. We implemented this approach in the Orlis library detection tool. As demonstrated by our experimental results, Orlis advances the state of the art and presents an attractive choice for detection of third-party libraries in Android apps.

When applying Q-learning algorithm to AUV route planning, it is difficult to strike a balance between exploration and exploitation. Usually, the fixed greedy rate or the greedy rate with fixed change trend is adopted according to experience. However, the value of greedy rate in the above method cannot be matched with the learning environment, and often occurs a problem of falling into a local optimal solution or slow learning convergence. In order to solve the above problems, we proposed an adaptive Q-learning algorithm. It guides the greedy rate with environmental complexity, and the environmental complexity degree of AUV is evaluated by the environmental complexity mathematical model. By setting up the simulation experiment of two-dimensional environment, the fixed greedy rate and the greedy rate with fixed change trend is compared. The simulation results show that the adaptive Q-learning algorithm considering environmental complexity converges faster and is less prone to fall into local optimal solutions.

This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous underwater vehicles, giving reachable path between the start and end points. Terrain-aided navigation overcomes the challenges of underwater domain, such as visual distortion and radio frequency signal attenuation, which make landmark-based localization infeasible. The path-planning algorithms improve the terrain-aided navigation positioning accuracy by considering terrain complexity. To evaluate the four algorithms, we designed simulation experiments that use real-word seabed bathymetry data. The results of autonomous underwater vehicle navigation by terrain-aided navigation in these four cases are obtained and analyzed.

For static analysis researchers, Android software presents a wide variety of interesting challenges. The target of our work is static detection of energy-drain defects in Android applications. The management of energy-intensive resources (e.g., GPS) creates various opportunities for software defects. Our goal is to detect statically “missing deactivation” energy-drain defects in the user interface of the application. First, we define precisely two patterns of run-time energy-drain behaviors, based on modeling of Android GUI control-flow paths and energy-related listener leaks along such paths. Next, we define a static detection algorithm targeting these patterns. The analysis considers valid interprocedural control-flow paths in a callback method and its transitive callees, in order to detect operations that add or remove listeners. Sequences of callbacks are then analyzed for possible listener leaks. Our evaluation considers the detection of GUI-related energy-drain defects reported in prior work, as well as new defects not discovered by prior approaches. In summary, the detection is very effective and precise, suggesting that the proposed analysis is suitable for practical use in static checking tools for Android.

In this paper, we describe two novel channel estimation technologies for orthogonal frequency-division multiplexing mobile communication systems using cluster discriminant analysis for sparse multipath channels. The least-squares estimator has a merit of low complexity and simple structure; however one of its drawbacks is that it does not take into consideration the effect of noise. Conventional DFT-based channel estimator improved its performance by suppressing time domain noise, but it does not completely suppress the noise. In order to overcome this disadvantage, we propose two novel channel estimation algorithms for Orthogonal frequency division multiplexing systems based on cluster analysis and discriminant analysis. The cluster analysis can be used to cluster residual noise. Discriminant analysis can distinguish the noise and channel taps in time domain. Computer simulation demonstrates the performance of the proposed algorithms in terms of bit error rate and mean square error performance.

Based on the space alternating generalized expectation maximization (SAGE) technique, and the ordered serial interference cancellation (OSIC) algorithm, an improved low-complexity inter-carrier interference (ICI) mitigation scheme, named SAGE-OSIC, is proposed for high mobile orthogonal frequency division multiplexing (OFDM) systems, where the subcarriers are updated in series, according to the order of the decreasing signal to interference plus noise ratio (SINR) or signal to noise ratio (SNR). The computational complexity is investigated, and simulation studies are concluded over multipath fading and rapidly time-varying channels. It is indicated that the proposed SAGE-OSIC scheme significantly outperforms the existing low-complexity ICI mitigation methods based on SAGE, without any increase in the computational complexity, especially for the large Doppler shifts in highly mobile environments. Furthermore, it can also be concluded that the performance of the proposed SAGE-OSIC approaches that of the vertical Bell laboratory layered space-time (VBLAST) scheme, which demands very high computation.

This work develops a static analysis to create a model of the behavior of an Android application's GUI. We propose the window transition graph (WTG), a model representing the possible GUI window sequences and their associated events and callbacks. A key component and contribution of our work is the careful modeling of the stack of currently-active windows, the changes to this stack, and the effects of callbacks related to these changes. To the best of our knowledge, this is the first detailed study of this important static analysis problem for Android. We develop novel analysis algorithms for WTG construction and traversal, based on this modeling of the window stack. We also describe an application of the WTG for GUI test generation, using path traversals. The evaluation of the proposed algorithms indicates their effectiveness and practicality.

In this paper, we study the effects of interference to channel estimation and present an iterative total least-squares algorithm for channel estimation by using the conjugate gradient method in time-varying multipath fading channels. The Doppler shift of fast-fading channels will generate inter-carrier interference (ICI), hence, degrade the performance of orthogonal frequency-division multiplexing (OFDM) systems. Furthermore, when the length of channel impulse response (CIR) overstep the duration of cyclic prefix (CP), which will cause inter-symbol interference (ISI) and degrade system performance severely. Conventional channel estimation and equalization schemes, if applied to this case of insufficient CP, suffer significant performance degradation. By ISI and ICI analysis, an Interference elimination technique is proposed for channel estimation. After analyzing perturbation of least squares problems, we present total least-squares (TLS) scheme to eliminate the ICI, ISI and noise. We present a short theoretical overview of the TLS problem. In our estimation, residual ISI and ICI are not being handled as a noise. Since the introduction of interference, the operation of matrix becomes perplexed. We implement the Conjugate gradient total least-squares method. Simulation results confirm the performance of Conjugate gradient total least-squares method and our theoretical analysis.