As the deployment of optical wireless communication (OWC) technology continues to expand, the demand for robust and reliable device identification methods has become crucial. This paper presents an enhanced thermal-resistant fingerprint model tailored for device identification within OWC systems. By conducting an in-depth analysis of traditional physic-based fingerprints, a temperature-independent fingerprint feature model is proposed to address the distortion of temperature fluctuations on fingerprints. Through extensive simulations and experimental validation, it is demonstrated that the proposed model significantly enhances identification performance, achieving higher accuracy compared to existing physics-based fingerprints. The results indicate the model’s applicability in practical OWC scenarios, underscoring its potential to improve security and efficiency in optical communication.

The application of power over Ethernet (PoE)-enabled lighting has emerged as a groundbreaking technology with the potential to achieve cooperative control and create an energy-saving lighting network. However, fine-grained user-side lighting environment perception is crucial to improve energy efficiency and user experience. To tackle this issue, a PoE-enabled intelligent lighting network with multi-modal sensing is proposed. Specifically, a multi-modal sensor is designed and installed on the side of the luminaire to provide both fine-grained lighting environment monitor and dimming. Then, a multilayer perceptron (MLP)-based estimation scheme is proposed to estimate the illumination and color temperature information around the user activity area. Moreover, an experimental lighting network is constructed and deployed in a classroom to measure practical lighting dataset. The proposed scheme effectively estimates the lighting quality on the user side based on the measurement data. Furthermore, based on the estimated information and the collaborative control capabilities of PoE, the operating current of the luminaire is updated in real time, and thus the ambient light resource is fully utilized. Finally, the proposed lighting network achieves a 39.99% energy saving.

Based on the hybrid-index model, this article investigates the asymptotic feedback set stabilization of Boolean control networks (BCNs) with random impulsive disturbances. In this model, it is assumed that the sequence of intervals between adjacent impulsive instants is independent and identically distributed. This assumption ensures that the subsequence of solutions sampled at impulsive moments is a Markov chain. Based on this assumption and the semi-tensor product (STP), random impulsive BCNs (RI-BCNs) can be converted into impulsive-interval driven probabilistic BCNs (ID-PBCNs), and the input-state transition probability matrix (IS-TPM) is constructed, the calculations of convergent target set in the hybrid domain and the time domain are discussed, and the necessary and sufficient conditions for asymptotic feedback set stabilizability are obtained. On this basis, we propose a design algorithm of state feedback controllers to stabilize RI-BCNs asymptotically with respect to a target set by using state-space partition, which enables the system to converge to a given set with the least number of impulsive intervals. Finally, the effectiveness of the obtained results is verified by simulations.

By equipping the synthetic aperture radar (SAR) sensors on multiple unmanned aerial vehicles (UAVs) to form a UAV swarm (UAVS) and operate collaboratively, UAVS-SAR presents the significant advantages of rapid echoes acquisition, high imaging frame rate as well as high system survivability for advanced SAR applications. However, due to the flexible trajectories as well as the distributed configuration, the problem of the spectrum blurring in the UAVS-SAR is more complicated than that of the conventional monostatic/bistatic SAR configurations, which makes the current fast time domain algorithms (FTDAs) difficult to achieve high imaging performance. In this paper, a novel fast time domain algorithm (FTDA) is developed for UAVS-SAR imaging with both high efficiency and promising accuracy. By developing the hierarchical framework based on the designed spectrum alignment function, the imaging procedures can be realized recursively where back projection (BP) operations are reduced dramatically, and then, the total computational burden are decreased consequently. Moreover, the trajectory difference of the UAVS formation is particularly considered for practical applications, which will inevitably degrade the coherence among the sub-images from different UAV platforms. To address this problem, a correction procedure is designed according to the distributed geometrical configuration. As the coherence between the sub-images is adequately maintained, the data-driven motion compensation (MOCO) is readily developed to remove the residual phase errors to achieve desirable imaging performance. Simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.

The rapid evolution of next-generation communi¬cation technology has underscored the critical challenge of spectrum resource scarcity. Visible light communication (VLC) is widely recognized as a promising solution to tackle this issue. Benefits from the rich spectrum resources and no electromagnetic interference radiation, VLC can be widely used in various indoor communication scenarios. However, the accessing of the existing network for the VLC system still remains a challenge. Thus, an optical fiber-based VLC structure is proposed to unlock traditional network cable transmission for the first time. Specifically, an optoelectronic media converter is designed to efficiently connect the fiber signal and the desired electrical signal. Moreover, an analog equalizer with low implementation complexity is exploited to extend the LED bandwidth, ensuring transmitted signals without distortion. It is worth noting that the VLC receiver adopts the auto gain control amplifier to meet the signal voltage requirement of the Ethernet physical layer. Experimental testbed is conducted and corresponding results demonstrated that the transmission rate of the proposed VLC system can reach 100Mbps with sufficiently low bit error rate. Therefore, this validates the capability of the proposed system to directly connect the VLC access point with the existing optical network facilities.

This article analyses the performance of a power-domain nonorthogonal multiple access (PD-NOMA)-based visible light communication (VLC) system in the Internet of Things (IoT) network. It specifically investigates the various VLC system channel models in indoor and outdoor IoT network settings. To enhance the existing analysis of the PD-NOMA-based VLC system performance, this study first analyses the distribution function of the received signals and the system noise in real-world environments. Second, it explores the impact of environmental noise on the system’s bit error rate (BER) under indoor Gaussian channels and derives the theoretical BER formulas for both the indoor and outdoor settings. Finally, theoretical symbol error rate (SER) formulas are derived for the three-device PD-NOMA-VLC system in both the indoor and outdoor environments. Numerical simulations demonstrate a close match between the theoretical analysis and simulation results, particularly in high signal-to-noise ratio (SNR) conditions.

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

Visible light positioning (VLP) has advantages over traditional indoor positioning techniques in terms of high positioning accuracy and low cost. However, in resource-constrained VLP systems, especially devices deployed on Internet-of-Things networks, both the bandwidth of the light-emitting diode (LED)-based transmitter and the sampling rate of the photodetector (PD)-based receiver are limited. In turn, this significantly limits the positioning accuracy. To address this issue and accommodate the scenario of low bandwidth and hardware cost, a novel positioning scheme with low bandwidth requirements and high-precision pulse reconstruction is proposed in the paper. Specifically, a new beacon signal is designed based on the on-off keying pulse pairs to remove the synchronization requirement and save bandwidth costs. Then, based on the maximum a posteriori probability criterion, a low sampling rate positioning scheme is exploited to estimate the location from the pulse pairs received by the PD. Moreover, an experimental test-bed is constructed to verify the effectiveness and feasibility of the proposed positioning scheme. Experimental results demonstrate that the proposed scheme achieves a positioning accuracy of 1.7 cm by using the reconstructed 2 GHz sampling rate in the case of a bandwidth of 50 MHz and a real sampling rate of 100 MHz. The positioning accuracy achieved by the proposed scheme remains within 30 cm, even under the few MHz of inherent LED bandwidth.

Visible light positioning (VLP) as a promising technology can provide high-precision indoor positioning services without electromagnetic interference, especially in multiple light-emitting diode (LED) scenarios. However, both high synchronization control cost and the multi-path interference are inevitable, which limits the performance gain of the VLP systems. In order to solve this problem, a differential phase of arrival (DPOA)-based positioning scheme is proposed in this paper to remove the reliance on time synchronization and alleviate the multi-path interference. Specifically, a dual-frequency orthogonal beacon is designed and the DPOA-based positioning algorithm is exploited to extract the differential phase information. Then, the positioning optimization problem is formulated by considering both positioning error and location constraints. Thus, the semi-definite programming (SDP) optimization method is exploited to obtain position. The simulation results demonstrate that the positioning accuracy achieved by the proposed scheme outperforms that obtained by traditional received signal strength and the time difference of arrival-based schemes, 90% positioning errors of the proposed method remain within 1.3 cm. Moreover, an experimental VLP test-bed is conducted to verify the feasibility and effectiveness of the proposed positioning scheme. The experimental results show that the average positioning error is 7.12 cm, which meets the centimetre-level positioning requirements.

Compressive sensing theory have been proposed in the field of radar for target detection. The challenge of compressive sensing applied to passive bistatic radar lies in the high computational complexity aggravated by the transmitted signal dependent time-varying sparse model. So, we propose a transmitted-signal-free and time-invariant sparse model for passive radar based on orthogonal frequency division multiplexing waveforms. We first generate the sparse model by using the surveillance signal and pilot information only, and exploiting the sparsity of scene including only a few targets and clutter. Then, range-Doppler profile for target detection can be implemented based on our proposed sparse model. Finally, simulation and experimental results illustrate that our proposed sparse model has high detection performance.

As an effective way to integrate the information contained in multiple medical images under different modalities, medical image synthesis and fusion have emerged in various clinical applications such as disease diagnosis and treatment planning. In this paper, an invertible and variable augmented network (iVAN) is proposed for medical image synthesis and fusion. In iVAN, the channel number of the network input and output is the same through variable augmentation technology, and data relevance is enhanced, which is conducive to the generation of characterization information. Meanwhile, the invertible network is used to achieve the bidirectional inference processes. Empowered by the invertible and variable augmentation schemes, iVAN not only be applied to the mappings of multi-input to one-output and multi-input to multi-output, but also to the case of one-input to multi-output. Experimental results demonstrated superior performance and potential task flexibility of the proposed method, compared with existing synthesis and fusion methods.

Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples. While it has tremendous potential to promote the image colorization task, the performance is limited owing to the high-dimension of data manifold and model capability. This study presents a novel scheme that exploits the score-based generative model in wavelet domain to address the issues. By taking advantage of the multi-scale and multi-channel representation via wavelet transform, the proposed model learns the richer priors from stacked coarse and detailed wavelet coefficient components jointly and effectively. This strategy also reduces the dimension of the original manifold and alleviates the curse of dimensionality, which is beneficial for estimation and sampling. Moreover, dual consistency terms in the wavelet domain, namely data-consistency and structure-consistency are devised to leverage colorization task better. Specifically, in the training phase, a set of multi-channel tensors consisting of wavelet coefficients is used as the input to train the network with denoising score matching. In the inference phase, samples are iteratively generated via annealed Langevin dynamics with data and structure consistencies. Experiments demonstrated remarkable improvements of the proposed method on both generation and colorization quality, particularly in colorization robustness and diversity.

Clutter rejection is a key technique for high-frequency passive radar (HFPR). To solve this problem, the traditional signal processing methods have been used, which mainly depend on prior information of the feature differences between target and clutter in time, space, or frequency domain. As a new attempt to deep-mine the clutter feature automatically and reject it by only data-driven processing, a novel clutter rejection method based on graph-relational mapping using a deep learning network is proposed in this letter. In this method, the clutter rejection problem is turned into an image-to-image translation problem between the range-Doppler (RD) spectrograms before and after clutter rejection. A deep-learning-enabled image translation network (CycleGAN) is exploited to learn from training data of RD spectrograms and to establish the mapping relationship. When processing clutter rejection tasks, the trained network can automatically extract clutter features without prior information and save manpower. The performance evaluations of the novel clutter rejection method are also investigated, and the experimental results confirm that the proposed method can effectively reject clutter in HFPR.

The clutter encountered in the ground-penetrating radar (GPR) system severely obscures the visibility of subsurface objects, especially in the case of overlapping target responses and clutter. In this letter, a novel self-supervised learning strategy with dual-network architecture and pseudolabels is proposed. First, the dual-network consists of two subnetworks: one is to simulate the low-rank part, and another simulates the sparse part. Second, the raw GPR data are decomposed as the sum of low-rank and sparse matrices by robust nonnegative matrix factorization (RNMF), termed two pseudolabels. Then, these pseudolabels guide the two subnetworks to accurately reconstruct the target response and clutter trace by trace, respectively. Results based on simulated data by gprMax and real datasets demonstrate that the proposed method is effective in separating target response from clutter and can achieve a similar effect as RNMF in less time without any prior information.

Fast factorized back-projection (FFBP) is a classic fast time-domain algorithm (FTDA), which is not limited by the assumption of azimuth-invariant of echo signal and is suitable for the bistatic synthetic aperture radar (BiSAR) process of arbitrary geometric configuration. However, when the conventional FFBP processing is employed for continuous imaging of multiple full-apertures, the processing efficiency will be decreased significantly, and difficulty will be introduced in motion compensation (MOCO) development. The main contributions in this article include the following two aspects: 1) a new FTDA framework based on FFBP implementation is developed for continuous imaging where echo data are divided into several full-aperture data blocks and then processed separately by FFBP implementation to reduce redundant BP operations for achieving high efficiency and 2) an efficient and effective data-driven MOCO methodology is developed based on the new FTDA framework for high focusing quality. In MOCO, because the phase error functions of subimages are estimated in the phase history domain from different local polar coordinate systems, these phase error functions are actually discontinuous in the spatial domain, which will bring significant discontinuity and defocusing into the final image. To address this problem, the correspondence of error functions between the spatial domain and the wavenumber domain is revealed based on which the phase error functions are reconstructed to remove the discontinuity for high focusing quality. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Battery service time is a critical constraint on the availability and functionality of mobile devices. Equipping larger batteries may mitigate such deficiency yet it raises the challenges on thermal limit and physical size. Changing the battery chemistry is another solution, which however usually benefits the energy efficiency of only part of the applications, depending on their software behaviors. To address the challenges of battery energy efficiency and heat dissipation in limited physical space, we propose CAPMAN, a management framework that jointly optimizes thecooling andactivepowermanagement in a smartphone, a typical mobile device, equipped with a hybrid battery pack. We establish the framework with three components. First, we abstract the correlation among the batteries, devices and software into a finite state machine model, whose state transitions can be triggered by actions like system calls and user activities. Second, we propose a battery scheduling algorithm that determines the more suitable battery for cooling/active power use, with respect to the dynamic software behaviors and their impact on the hardware states, based on a Markov decision process (MDP). Third, we design a facility for joint cooling and active power management by coordinating TECs and batteries. With the three major designs, CAPMAN realizes software defined management that schedules heterogeneous batteries and TEC cooling in a timely manner. In addition, CAPMAN provides an online algorithm with a proved O(1.05)-competitiveness performance. With a pair of big.LITTLE batteries, we prototype CAPMAN on multiple popular smartphones and a PYNQ development board. The evaluation with real-world workloads shows that compared to the current mainstream, CAPMAN can achieve 114 percent longer battery service time under skewed loads; compared to the state-of-the-practice baselines, CAPMAN shows 55 percent performance gain and 53 percent less energy use on average. Those results approve that big.LITTLE batteries with sophisticated software defined management is an effective way to prolong the battery service times on mobile devices.

Abstract Cloud markets advocate powerful instances to take computation over from the cyber‐physical system (CPS). Combining the Cloud and CPS layer, the whole Cloud–CPS framework is designed to achieve both accurate data sensing and fast data analysis. While most researchers trust the computation side, and focus on the actuator in the physical space to ensure the service‐level objectives, SLO, that is, deadline misses, cloud can be a threat to the service sustainability as instance may fail, especially when one tries to make a cost‐effective design. Specifically, users must bear the risk of instance failure. These risks can cause the entire cyber‐physical system to collapse. Our work tackles the cloud aspect of the sustainability challenge from the cloud side in a cloud–CPS framework. We have studied the instance selection problem for the CPS systems, and propose a Cost‐Risk Analysis for Instance Recommendation, or CRAIR, to support a sustainable Cloud–CPS framework. We have adopted the classic risk analysis process from the portfolio management in hedge financial market, combining with the system modeling for the CPS instance selection, as an optimization problem. To solve this problem, we formulate it as a multi‐armed bandit problem and solve it with our upper confidence bound bandit algorithm together, our CRAIR can provide an online risk analysis to maximize the profit with a comparative ratio of O(1+ ). We have evaluated CRAIR based on simulations using real‐world Google and Alibaba workloads and cloud market numbers. The results show that, compared to traditional approaches, our approach provides the best tradeoff between SLOs and costs. All users achieve their SLOs goals while minimizing their average expenses by 34.6%. By using CRAIR for instance selection, the CPS service can maximize its benefit under a controlled risk.

Visible light communication (VLC) is a promising technique in the fifth and beyond wireless communication networks. In this paper, a secure multiple-input single-output VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support energy-limited devices taking into account a practical non-linear energy harvesting model. Specifically, the optimal beamforming design problems for minimizing transmit power and maximizing the minimum secrecy rate are studied under the imperfect channel state information (CSI). S-Procedure and a bisection search is applied to tackle challenging non-convex problems and to obtain efficient resource allocation algorithm. It is proved that optimal beamforming schemes can be obtained. It is found that there is a non-trivial trade-off between the average harvested power and the minimum secrecy rate. Moreover, we show that the quality of CSI has a significant impact on achievable performance.

Modern smartphone is far from being ubiquitous due to limited energy capacity. Recent research suggests that heterogeneous batteries may expose power saving opportunities that fit dynamic software patterns. Yet it is still a challenge on thermal and power management for a hybrid battery pack in a smartphone. To address this challenge, we propose a system framework, called CAPMAN , which supports joint optimization of cooling and active power management in smartphones. The framework consists of three major techniques: 1) A Markov decision process (MDP) technique that models battery types, and cooling/active power use from state and action nodes; 2) A structural similarity approximation that speeds up the convergence of MDP computation, providing battery scheduling decisions; 3) A TEC and battery management facility to realize the cooling and active power management. In addition, CAPMAN provides an online algorithm with a proved worst-case ${\mathbf{O}}\left( {\frac{1}{{1 - \rho }}} \right)$-competitiveness performance, whereρis the factor of discount. We have prototyped CAPMAN with popular smartphones and heterogeneous batteries, and evaluated them with real-world workloads. Results show that CAPMAN can achieve 114% longer service time under skewed loads, compared to the original phone. Compared to the state-of-the-practice baselines, CAPMAN shows 55% performance gain and 53% less energy use on average. As such, CAPMAN approves that big.LITTLE batteries with a careful system design is an effective way to prolong smartphone service times.

When observing from a spaceborne or unmanned airborne platform, aperture synthesis radiometric imaging (ASRI) is sometimes configured with an irregularly distributed array. In the previous study, the array factor forming (AFF) has been proposed to reconstruct the brightness temperature (BT) image of the observed scene. However, it is found that AFF is invalid when system noise is taken into account. In order to solve the problem, AFF with regularization is proposed in this letter. The reason why the conventional AFF becomes invalid is analyzed, and how AFF with regularization provides a stable solution is discussed. Numerical simulation and experimental results show that the BT image reconstructed by AFF with regularization is more accurate than that reconstructed by the conventional AFF, which demonstrates the validation of the method.

Colorization is the process of coloring monochrome images. It has been widely used in photo processing and scientific illustration. However, colorizing grayscale images is an intrinsic ill-posed and ambiguous problem, with multiple plausible solutions. To address this issue, we develop a novel progressive automatic colorization via iterative generative models (iGM) that can produce satisfactory colorization in an unsupervised manner. In particular, the generative model is exploited in multi-color spaces (e.g., RGB, YCbCr) jointly and enforced with linearly autocorrelative constraint. This is regarded as the key prior information to pave the way for producing the most probable colorization in high-dimensional space. Experiments on indoor and outdoor scenes reveal that iGM produces more realistic and finer results, compared to state-of-the-arts.

Visible light communication (VLC) is a promising technology for the next generation wireless communication systems due to its high spectral efficiency and energy efficiency. In this article, a secure multiple-input single-output (MISO) VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support multiple energy-limited devices tacking into account a practical non-linear energy harvesting model. The transmit power minimization and the minimum secrecy rate maximization problems are formulated under both perfect and imperfect channel state information, respectively. To further improve the user connectivity, those problems are also investigated in MISO-VLC networks with non-orthogonal multiple access (NOMA). To tackle these challenging non-convex problems, semidefinite program relaxation and $-Procedure are exploited. It is proved that optimal beamforming schemes can be obtained for the considered two types problems in MISO-VLC SLIPT networks, while a sub-optimal solution can be obtained for the transmit power minimization problem in those networks with NOMA. It is found that there is a non-trivial trade-off between the average harvested power and maximum-minimum secrecy rate. Moreover, it is shown that the performance achieved with NOMA outperforms that of conventional orthogonal multiple access in MISO-VLC SLIPT networks, despite the existence of imperfect channel state information.

Image restoration (IR) is a long-standing challenging problem in low-level image processing. It is of utmost importance to learn good image priors for pursuing visually pleasing results. In this paper, we develop a multi-channel and multi-model-based denoising autoencoder network as image prior for solving IR problem. Specifically, the network that trained on RGB-channel images is used to construct a prior at first, and then the learned prior is incorporated into single-channel grayscale IR tasks. To achieve the goal, we employ the auxiliary variable technique to integrate the higher-dimensional network-driven prior information into the iterative restoration procedure. In addition, according to the weighted aggregation idea, a multi-model strategy is put forward to enhance the network stability that favors to avoid getting trapped in local optima. Extensive experiments on image deblurring and deblocking tasks show that the proposed algorithm is efficient, robust, and yields state-of-the-art restoration quality on grayscale images.

Time-domain algorithms have special advantages for bistatic forward-looking synthetic aperture radar (BFSAR) applications with complex geometric configuration. In this paper, a improved fast time-domain algorithm based on orthogonal elliptical polar (OEP) coordinate is proposed for BFSAR imaging, which has prominently reduced burden in computation. In addition, the non-systematic range cell migration (NsRCM) is also analyzed and corrected in the motion compensation (MOCO) process. Simulation experiments are presented and analyzed to validate the superiority of the proposed algorithm.

In this paper, we investigate a novel simultaneous lightwave information and power transfer (SLIPT) in visible light communication (VLC) systems, where a photo diode (PD) and a solar panel are utilized as the information receiver and the energy harvester, respectively. By systematically analyzing both the information receiver and the energy harvester, we obtain the explicit expressions to characterize the illumination-rate-energy region. Based on the derived expressions, we investigate the downlink unicast transmission of multi-LED multi-user SLIPT VLC networks, and study the total transmit power minimization problem under the rate requirements, the minimum energy harvesting requirements, and dimming control constraints. To solve such non-convex problem, we exploit the semidefinite relaxation (SDR) technique and relax the problem into a convex problem, which can be efficiently solved via interior-point methods. Moreover, for the sake of users' fairness, we further investigate the beamformer design to maximize the minimal rate under both minimum energy harvesting and dimming control constraints. Finally, the numerical results are provided to evaluate the proposed SLIPT system.

This paper investigates the maximal ratio combining (MRC) performance of an amplify and forward (AF) relay system in Nakagami- m fading environments. The study considers a general scenario with distinct m fading parameters for the following three links, source to relay link, and source to destination link and relay to destination link. We derive new closed form expressions for the statistics of important performance metrics, including the moment generating function, outage probability, higher order moments of equivalent signal to noise ratio (SNR), ergodic capacity, and average symbol error probability (SEP) of common modulation types. In particular, we focus on analytical SEP expressions in the context of an additive white generalized Gaussian noise (AWGGN). As an active area of research, generalized noise receives much attention for its flexible model. However, analytical performance of modulation scheme in generalized noise type has not been found in open literature for AF relaying with MRC despite its practical usefulness. Without the help of analytical solutions, the SEP in generalized noise can only be obtained by a large number of repeated simulation experiments. Therefore, we present the general SEP expression by using special Fox’s H function. Simulation results verify the accuracy of our theoretical analysis and show that the diversity order of MRC criterion linearly depends upon Nakagami parameters of three links.

Mirrored aperture synthesis (MAS) has been proposed to utilize a few antennas to provide high spatial resolution for atmospheric remote sensing. In order to verify the fundamentals of mirrored aperture synthesis (MAS) further, a prototype working at V band was developed in the past two years. In this paper, the prototype is briefly introduced, and then some indoor experiments and the experimental results for one-dimensional MAS based on the prototype are presented, mainly including the fringe pattern experiment for verifying the correlation output and the imaging experiment based on two point sources for evaluating the spatial resolution of MAS.

Radar and communication (RadCom) systems have received increasing attention due to their high energy efficiency and spectral efficiency. They have been identified as green communications. This paper is concerned with a joint estimation of range‐Doppler‐angle parameters for an orthogonal frequency division multiplexing (OFDM) based RadCom system. The key idea of the proposed method is to derive different factor matrices by the tensor decomposition method and then extract parameters of the targets from these factor matrices. Different from the classical tensor decomposition method via alternating least squares or higher‐order singular value decomposition, we adopt a greedy based method with each step constituted by a rank‐1 approximation subproblem. To avoid local extremum, the rank‐1 approximation is solved by using a multiple random initialized tensor power method with a comparison procedure followed. A parameterized rectification method is also proposed to incorporate the inherent structures of the factor matrices. The proposed algorithm can estimate all the parameters simultaneously without parameter pairing requirement. The numerical experiments demonstrate superior performance of the proposed algorithm compared with the existing methods.

This paper is devoted to an investigation of an exact average symbol error probability (SEP) for amplify and forward (AF) relaying in independent Nakagami‐m fading environments with a nonnegative integer plus one‐half m, which covers many actual scenarios, such as one‐side Gaussian distribution (m = 0.5). Using moment generating function approach, the closed‐form SEP is expressed in the form of Lauricella multivariate hypergeometric function. Four modulation modes are considered: rectangular quadrature amplitude modulation (QAM), M‐ary phase shift keying (MPSK), M‐ary differential phase shift keying (MDPSK), and π/4 differential quaternary phase shift keying (DQPSK). The result is very simple and general for a nonnegative integer plus one‐half m, which covers the same range as integer m. The tightness of theoretical analysis is confirmed by computer simulation results.

Image prior information is a determinative factor to tackle with the ill-posed problem. In this paper, we present a multi-filters guided low-rank tensor coding (MF-LRTC) model for image restoration. The appeal of constructing a low-rank tensor is obvious in many cases for data that naturally comes from different scales and directions. The MF-LRTC takes advantages of the low-rank tensor coding to capture the sparse convolutional features generated by multi-filters representation. Using such a low-rank tensor coding would reduce the redundancy between feature vectors at neighboring locations and improve the efficiency of the overall sparse representation. In this work, we are committed to achieving this goal by convoluting the target image with filters to formulate multi-features images. Then similarity-grouped cube set extracted from the multi-features images is regarded as a low-rank tensor. The potential effectiveness of this tensor construction strategy is demonstrated in image restoration including image deblurring and compressed sensing (CS) applications.

This study considers an improved decode and forward (DF) protocol in an orthogonal frequency division multiplexing‐based cooperative system, where the improved DF protocol implies that the source node is allowed to emit the same symbol in the second phase as that in the first phase, irrespective to whether the relay is idle or not. Because rate provision is one of the main design goals in wireless network, the authors construct an optimisation problem to improve the overall sum rate of the system and propose a joint power allocation and subcarrier pairing algorithm. Total power constraint and individual power constraints at the source node and the relay node will be treated differently. Both theoretical analysis and simulation results demonstrate that the authors' proposed joint algorithm for the improved DF protocol drastically harvests remarkable gains from the extra repeat transmission in the second phase and is superior to other existing methods.

Physical‐layer security from an energy‐efficient perspective is of crucial importance in cognitive radio (CR). A CR network is considered where a secondary user (SU) coexists with a primary user in the presence of an eavesdropper and channel fading. Secure energy efficiency (EE) maximisation problems are formulated in secure green CR based on the condition that a minimum secrecy capacity (SC) of a SU is guaranteed. A peak interference power constraint and an average (ATP)/peak transmit power (PTP) constraint are imposed in the SU's Tx. Using fractional programming and the Lagrange dual method, energy‐efficient optimal power allocation strategies are proposed to efficiently solve the secure EE maximisation problems. It is shown that the secure EE of the SU achieved under the ATP constraint is higher than that obtained under the PTP constraint. The tradeoff is elucidated between the secure EE and the SC of the SU.

The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete searching solver has a great potential in its conversion ability. In this paper, we present a two-step strategy to efficiently extend the parameter searching solver to a two-order multivariance polynomial model, as a sum of three subspaces. We show that the first subspace in the two-order model is the most important and the second one can be seen as a refinement. In the first stage of our model, the gradient correlation similarity (Gcs) measure is used on the first subspace to obtain an immediate grayed image. Then, Gcs is applied again to select the optimal result from the immediate grayed image plus the second subspace-induced candidate images. Experimental results show the advantages of the proposed approach in terms of quantitative evaluation, qualitative evaluation, and algorithm complexity.

Because the principle of mirrored interferometric aperture synthesis (MIAS) is different from that of the conventional interferometric aperture synthesis, the antenna array for 1-D MIAS should be redesigned. And in order to get a precise estimation of the cosine visibilities, the maximum rank of the transfer matrix should be set as the key constraint condition of the array optimization model, but this has not been clearly presented before. In this letter, the maximum rank of the transfer matrix is discussed and proved by the mathematical induction method. When the position of each antenna in the array is an even multiple of 0.5, the value for the maximum rank is proven to be M - 2, and when the position of each antenna is an odd multiple of 0.5, the value for the maximum rank is proven to be M - 1, where M is the number of the spatial frequencies provided by the array. This conclusion is significant for the array design of 1-D MIAS.

While the RGB2GRAY conversion with fixed parameters is a classical and widely used tool for image decolorization, recent studies showed that adapting weighting parameters in a two-order multivariance polynomial model has great potential to improve the conversion ability. In this paper, by viewing the two-order model as the sum of three subspaces, it is observed that the first subspace in the two-order model has the dominating importance and the second and the third subspace can be seen as refinement. Therefore, we present a semiparametric strategy to take advantage of both the RGB2GRAY and the two-order models. In the proposed method, the RGB2GRAY result on the first subspace is treated as an immediate grayed image, and then the parameters in the second and the third subspace are optimized. Experimental results show that the proposed approach is comparable to other state-of-the-art algorithms in both quantitative evaluation and visual quality, especially for images with abundant colors and patterns. This algorithm also exhibits good resistance to noise. In addition, instead of the color contrast preserving ratio using the first-order gradient for decolorization quality metric, the color contrast correlation preserving ratio utilizing the second-order gradient is calculated as a new perceptual quality metric.

This paper presents a novel Log-Euclidean metric inspired color-to-gray conversion model for faithfully preserving the contrast details of color image, which differs from the traditional Euclidean metric approaches. In the proposed model, motivated by the fact that Log-Euclidean metric has promising invariance properties such as inversion invariant and similarity invariant, we present a Log-Euclidean metric-based maximum function to model the decolorization procedure. The Gaussian-like penalty function consisting of the Log-Euclidean metric between gradients of the input color image and transformed grayscale image is incorporated to better reflect the degree of preserving feature discriminability and color ordering in color-to-gray conversion. A discrete searching algorithm is employed to solve the proposed model with linear parametric and non-negative constraints. Extensive evaluation experiments show that the proposed method outperforms the state-of-the-art methods both quantitatively and qualitatively.

A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

This paper presents a novel gradient correlation similarity (Gcs) measure-based decolorization model for faithfully preserving the appearance of the original color image. Contrary to the conventional data-fidelity term consisting of gradient error-norm-based measures, the newly defined Gcs measure calculates the summation of the gradient correlation between each channel of the color image and the transformed grayscale image. Two efficient algorithms are developed to solve the proposed model. On one hand, due to the highly nonlinear nature of Gcs measure, a solver consisting of the augmented Lagrangian and alternating direction method is adopted to deal with its approximated linear parametric model. The presented algorithm exhibits excellent iterative convergence and attains superior performance. On the other hand, a discrete searching solver is proposed by determining the solution with the minimum function value from the linear parametric model-induced candidate images. The non-iterative solver has advantages in simplicity and speed with only several simple arithmetic operations, leading to real-time computational speed. In addition, it is very robust with respect to the parameter and candidates. Extensive experiments under a variety of test images and a comprehensive evaluation against existing state-of-the-art methods consistently demonstrate the potential of the proposed model and algorithms.

A integrated propagation model for VANET in densely built-up urban scenario, based on analyzing the special factors of the measurements, is proposed in the paper. The proposed propagation model can be used as the physical module in OMNet++ to evaluate the packet level performance for VANET in a densely built-up urban scenario. The effectiveness of the proposed method is demonstrated by using experiments under various radio environments in Wuhan, China. The simulation results of the statistical packet level performance are approximately consistent with that in measurements, and the error between measurements and simulation is acceptable.

In order to catch the iterated growth and evolution of the future mobile communications, this paper has proposed to study on a novel prediction model for radio propagation as well with its applications based on the radio propagation theory and the inverse theory. The prediction of radio propagation in a mobile network can be treated as an inverse problem. Instead of through the high-precision geometric modeling of wireless environments, this problem can be solved by an inversion of the measured data (under all priori constraints). So a complicated propagation prediction problem can be simplified to a system of large scale ill-condition equations, which can be solved by genetic algorithm appropriately. The effectiveness of the proposed method has been demonstrated using experiments under various radio environments in Guang Dong, China. It was shown that the prediction results were approximately consistent with independent checking samples. The advantages of the proposed strategies compared with existing approaches are well demonstrated.

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). Understanding and modeling packet error in the VANET is especially relevant to the design and analysis of higher layer wireless communication protocols. In this paper, the analysis of the packet error characteristics based on measurements in live VANET in different scenarios is shown form which Traces-4 are selected for further analysis. Based on the statistical dependence between burstlengths and gaplengths, we present a packet-level Markov (PLM) model which is capable of describing the measured statistics properly. The parameters of the model are obtained from the packet error statistical characteristics. Finally, it is demonstrated that both the accuracy and the efficiency of the proposed PLM model outperform other popular Markov models.