Search

The optimization objective function of sensor management for target identification is commonly established based on information theory indicators such as information gain, discrimination, discrimination gain, and quadratic entropy, which can control the sensors to reduce the overall uncertainty of all targets to be identified but ignores the speed of target being confirmed as identified. Therefore, inspired by the maximum posterior criterion of target identification and the target identification confirmation mechanism, we study a sensor management method that preferentially allocates resources to identifiable targets. Firstly, in the distributed target identification framework based on Bayesian theory, an improved identification probability prediction method that provides feedback the global identification results to local classifiers is proposed, which can improve the accuracy of identification probability prediction. Secondly, an effective sensor management function based on information entropy and expected confidence level is proposed to optimize the identification uncertainty itself rather than its variation, which can increase the priority of targets that satisfy the desired confidence level. In the end, the sensor management for target identification is modeled as a sensor allocation problem, and the optimization objective function based on the effective function is constructed, which can improve the target identification speed. The experimental results show that the correct identification rate of the proposed method is comparable to the methods based on information gain, discrimination, discrimination gain, and quadratic entropy in different scenarios, but the average time to confirm the identification is the shortest. [ABSTRACT FROM AUTHOR]

• The field tests were carried out for more than two years on Chinese high-speed railway lines, it is found that the main failure cause of the BBF is the resonant vibration of 8–20 Hz which excites the first-order natural mode of the frame. • It is found that the measured data are quite different from the IEC61373 standard, it is necessary to reassess whether the limits in the standard are safe or not for Chinese high-speed railway lines. • The bolster exhibits obvious non-Gaussian characteristics, it could be inferred that the Gaussian signal maybe not always suitable for the input load spectrum. Aiming to solve the failure problem of welded frame suspended under high-speed EMU, a long-term tracking test was carried out. The modal resonance of 8–20 Hz is the main cause of structural failure. The characteristics of the in-service vibration environment and the difference with the standard load spectrum are analyzed. The measured load spectrum exceeds the IEC61373 standard load spectrum in the frequency range of 10–20 Hz and 30–120 Hz. Meanwhile, the kurtosis of measured acceleration signal does not always follow the Gaussian distribution for the carbody bolsters, and the acceleration and dynamic stress amplitude of the bolster and suspension equipment frame increase more with higher train speed. After a finite element model with welded joints was developed to evaluate the fatigue life, an optimized prototype BBF was designed. The reliability of the optimized structure is verified by rig test. The optimized structure avoids 8–20 Hz resonance and meets the test requirements. [ABSTRACT FROM AUTHOR]

The energy-efficient resource allocation problem is investigated for the downlink massive multiple-input–multiple-output (MIMO) frequency-division duplexing (FDD) system under a correlated Rayleigh fading channel. Our objective is to maximize energy efficiency by adjusting the training duration, training power, and data power, under the constraint of the total transmit energy and spectral efficiency requirement for the user, which evaluates the impacts of the training and data transmission phases simultaneously. The optimization problem is established in a complicated form, where the difficulties lie in the nonanalytic expression of the involved objective function, as well as the nonconvex nature of the optimization problem. To solve it, the deterministic equivalent approximation methodology is introduced to obtain an accurate analytical expression of the cost function. Based on this, the optimization problem in a nonconvex fractional form is transformed into an equivalent optimization problem with a parametric subtractive form, which is still nonconvex. Thus, the objective function is lower bounded by a concave function, which leads to the availability of standard convex theory, and finally, an iterative resource allocation algorithm is proposed. Moreover, we obtain the closed-form solutions using the Lambert W function for some special channel cases. Numerical results validate the benefits of the proposed scheme and illustrate the tradeoff between training and data transmission. [ABSTRACT FROM PUBLISHER]

The cellular communications scenario involving the coexistence of the one-hop direct transmission and the two-hop relaying is studied in this paper. In contrast to conventional cellular systems featuring orthogonal information flows served by decoupled channel resources via, e.g., time division, we propose a novel protocol in which two information flows are served simultaneously via the shared channel resource, thus constituting a spectral-efficient solution for cellular communications. On the other hand, however, an inevitable issue associated with the proposed protocol is the interflow interference, which may lead to serious deterioration on both information flows. To tackle this issue, we develop an overhearing-based protocol that utilizes the overheard interference as useful side information in the receiver design. Specifically, depending on the interference levels, an adaptive linear minimum mean squared error (MMSE) and nonlinear MMSE successive interference cancellation (SIC) receiver exploiting the overheard interference at the direct mobile terminal is developed. To balance between the two information flows, we develop the asymptotically optimum superposition coding at the base station (BS) in the high-power regime. Furthermore, the optimum relay beamforming matrix maximizing the bottleneck of the achievable rates of the two information flows is developed subject to a finite power constraint. Finally, simulations demonstrate a remarkable throughput gain over the conventional cellular systems. [ABSTRACT FROM AUTHOR]

In this paper, an overhearing protocol is proposed for two-way cooperative multiantenna relaying systems, where the relays equipped with multiple antennas collaborate to relay signals between the base station (BS) and two user equipment units (UEs). In the proposed overhearing protocol, the UE in the uplink transmission phase transmits only in the first time slot, i.e., it remains silent in the second time slot, whereas the previous overhearing protocol assumes that the UE transmits also in the second time slot. Therefore, the proposed overhearing protocol is more power efficient. The precoding matrix at each cooperative relay is optimized in the sense of minimizing the weighted mean squared error (WMSE). Simulation results show that the proposed scheme shows not only lower mean squared error but also higher achievable sum rate than existing cooperative relaying schemes. [ABSTRACT FROM AUTHOR]

In this paper, we present two novel interference management stratagems for coexisting one primary user (PU) and multiple secondary users (SUs) by exploiting the unused spatial directions at PU. The cognitive stations sense their environment to determine the users they are interfering with, and adapt to it by designing the corresponding precoders using interference alignment (IA) in order to avoid causing performance degradation to nearby PU and SUs. The first proposed approach judiciously designs the set of precoders based on an improved version of minimum weighted leakage interference algorithm. However, there are still leftover interference signals in SUs’ desired signal space due to the limited iterative times in the first algorithm. To tackle this problem, another scheme combining the first one and power allocation method at the secondary stations is developed. Numerical results validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The conventional two-way relaying (TWR) protocol requires that the transmitter (receiver) in one direction must be the receiver (transmitter) in the other direction, a limitation precluding the application of the TWR for sophisticated real-world wireless networks. In this paper, we study a more general multicell system consisting of a downlink (DL) traffic in one cell and an uplink (UL) traffic in an adjacent cell, with a multiantenna relay located in the cell edge and shared by both cells. For the coexistence of DL and UL transmissions, we propose exploiting the overheard signals from the adjacent cell (commonly known as the intercell interference) to improve the quality of signal reception in both cells. To reduce the power consumption to suit for green networks, we design the optimum relay precoder to minimize the total power at the relay yet satisfying the rate constraints for both the DL and UL traffic flows. The original precoder design is a nonconvex problem that is difficult to solve. To make the problem tractable, we transform the nonconvex problem to an equivalent quadratically constrained quadratic program (QCQP), which is then solved by the semidefinite relaxation (SDR) technique. Finally, simulations validate the effectiveness of our proposed protocol together with the optimized relay precoder. [ABSTRACT FROM PUBLISHER]

In this paper, the joint transceiver design issue is investigated for multi-cell systems with multiple mobile terminals (MTs). An optimization problem is formulated based on the criterion of mean squared error minimization for the joint design of the cooperative precoder of all multi-input multi-output (MIMO) cells and the cooperative decoder of all MIMO MTs. With a fixed decoder, the optimum precoder is first derived in a closed-form expression, where no exchange of data streams among all of the MIMO cells is required due to the limited capability of the backhaul. Subsequently, an optimum decoder is then obtained in an analytical expression with the fixed precoder. Based on these two expressions, an iterative algorithm with guaranteed convergence is finally developed to carry out the joint optimization of the cooperative precoders and decoders. Simulation results demonstrating the effectiveness of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]

This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non‐convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes. [ABSTRACT FROM AUTHOR]

Video Question Answering (VideoQA) requires a thorough comprehension of linguistic and visual modalities. However, recent methods confront two problems: (1) Synchronous modeling of object action and frame scene instead of a step-by-step manner, which can better mine potential semantic attributes of videos, lacks research; (2) The relationship between cross-modal alignments at different granularity of abstraction is not fully utilized. Based on these insights, we propose a novel method named hierarchical synchronization with structured multi-granularity interaction (HSSMI) for VideoQA. First, a hierarchical synchronous reasoning module is put forward to model objects' relations and dynamics and synchronously capture their synergistic influences over time when analyzing whole frames. It is seen as multiple Object ConvLSTMs (O-CLSTMs) in isolation or a Frame ConvLSTM (F-CLSTM) in collectivity. Specifically, O-CLSTM learns the object-level action states under neighboring spatial interplays. Meanwhile, F-CLSTM learns the frame-level scene state, where action information from O-CLSTMs is selectively aggregated into a common memory cell of F-CLSTM as instructed by questions. Besides, a boundary detector is equipped to discover scene discontinuities, enabling F-CLSTM to alter its time connectivity and adapt its sequential encoding process to videos. Thereafter, we develop a conditional VLAD with topic constraints for discriminative modality summarization. Last, a structured multi-granularity interaction module is proposed to integrate complemented clues on the global alignment between scene summary and full question and the local alignments between action summaries and words. This module encourages useful information passing through compositional syntactical topologies of questions to predict answers. Experiments on three public benchmark datasets demonstrate the superiority of our HSSMI against other state-of-the-art methods. Codes will be publicly available at https://github.com/Qiss33/HSSMI. • Propose a fine-grained vision-language interaction network (HSSMI) for VideoQA. • Hierarchical synchronous reasoning for mining latent semantic structures of videos. • Exploit complementarity of global and local alignments by iterative message passing. • Extensive experiments demonstrate the robustness and effectiveness of HSSMI. [ABSTRACT FROM AUTHOR]

The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, a blockchain-enabled relay-aided wireless network for sustainable e-agriculture is investigated, where best relay selection scheme based on the maximization of equivalent signal to noise ratio/signal to interference ratio (SNR/SIR) is considered. The performance of best relay selection under α − μ channel are derived in the paper, where closed-form expressions for transmission successful rate (TSR) and overall communication throughput (OCT) with and without interference are obtained. From derivation results, as the number of potential relay nodes increase, TSR and OCT performance will be ameliorated correspondingly. Numerical simulations have been performed to validate the correctness of our derivations. [ABSTRACT FROM AUTHOR]

Power control in massive multiple input multiple output (MIMO) systems is an appealing technique to improve network performance and reliability. The traditional methods to solve such problems are based on the convex optimization theory, which incurs high computational complexity. In contrast, this work leverages deep neural networks to maximize the minimum data rate of the downlink users in the massive MIMO network. Based on the different setups in the practical systems, namely in-distribution case, weakly out-of-distribution case, and out-of-distribution case, we propose three approaches to solve them. Specifically, first, we establish a densely connected neural network for solving the power control problem. Then, for the case where the test data and training data come from the same distributions (the in-distribution case), we use the established neural network to obtain an approximately optimal power control strategy, and propose a self-training-based algorithm to use the unlabeled samples collected in the actual system to further improve the system performance. For the case that the test data and training data come from different distributions and a small amount of labeled target domain data can be obtained (the weakly out-of-distribution case), we propose a pre-training and fine-tuning algorithm to solve the problem. For the issue that we can not obtain the labels of the target domain data (the out-of-distribution case), we transform the problem into a covariate shift problem and propose an algorithm by weighting the loss function. Numerical results demonstrate that the proposed schemes not only match the optimal solution well but also have low online inference complexity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV’s maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, due to the bandwidth constraint, the distributed nodes (DNs) might only provide binary representatives of the source signal, and then transmit them to the central node (CN). In this paper, we consider the joint estimation of signal amplitude and background noise variance from binary observations over decentralized channels. We first analyze the Cramér–Rao lower bounds (CRLBs) of the parameters of interest and develop a quasilinear estimator (QLE), in which the desirable estimates can be obtained from several intermediate parameters linearly. Next, we consider a more realistic situation where the decentralized channel is noisy during the data transmission. Based on the error propagation model, the asymptotic analysis shows that the performance of the proposed QLE is mainly dominated by the thresholds of the quantizers, which encourages us to adopt a correlated quantization (CQ) scheme by exploiting the spatial correlation among background noises/channel noises. To ease the implementation of QLE in practice, an adaptive quantization (AQ) scheme is also proposed so as to obtain reasonable selections of the required thresholds. Finally, numerical simulations are provided to validate our theoretical findings. [ABSTRACT FROM AUTHOR]

Vehicle to everything (V2X) has been deemed a promising technology due to its potential to achieve traffic safety and efficiency. This paper considers a V2X downlink system with a simultaneous wireless information and power transfer (SWIPT) system where the base station not only conveys data and energy to two types of wireless vehicular receivers, such as one hybrid power-splitting vehicular receiver, and multiple energy vehicular receivers, but also prevents information from being intercepted by the potential eavesdroppers (idle energy vehicular receivers). Both the base station and the energy vehicular receivers are equipped with multiple antennas, whereas the information vehicular receiver is equipped with a single antenna. In particular, the imperfect channel state information (CSI) and the practical nonlinear energy harvesting (EH) model are taken into account. The non-convex optimization problem is formulated to maximize the minimum harvested energy power among the energy vehicular receivers satisfying the lowest harvested energy power threshold at the information vehicular receiver and secure vehicular communication requirements. In light of the intractability of the optimization problem, the semidefinite relaxation (SDR) technique and variable substitutions are applied, and the optimal solution is proven to be tight. A number of results demonstrate that the proposed robust secure beamforming scheme has better performance than other schemes. [ABSTRACT FROM AUTHOR]

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks. [ABSTRACT FROM AUTHOR]

The sparse signal recovery-based direction of arrival (DOA) estimation has received a great deal of attention over the past decade. From the sparse representation point of view, ℓ0-norm is the best choice to evaluate the sparsity of a vector. However, solving an ℓ0-norm minimization problem is non-deterministic polynomial hard (NP-hard). Thus, The common idea for many sparse DOA estimation methods is to use the ℓ1-norm as the sparsity metric. However, its sparse solution may not coincide with the solution resulting from the ℓ0-norm thus deteriorating the DOA estimation performance. In this paper, we propose a new sparse method based on ℓp (0

Energy harvesting (EH) is believed to be a promising technology in next generation wireless networks due to its potential to extend the working time for uncharged nodes and improve the user experience. We investigate the outage performance of dual-hop decode-and-forward relay networks, where EH from wireless signals is considered at the relay node. Then, we derive the cumulative distribution function of the received SNRs. We also formulate the relationship among the outage probability, full EH slots and the power splitting factor by deriving the asymptotic expressions of the outage probability of our system. Simulation results closely match with our theoretical analysis. [ABSTRACT FROM AUTHOR]

In heterogeneous cellular networks (HCNs), the deployed small cell base stations share the same spectrum resource with that of macro base station, which causes cross-tier interferences and the performance depreciation of the system. This paper investigates the downlink outage performance of HCNs and its resource allocation problem to mitigate interferences. Specifically, we first derive closed-form expressions of overall outage probability of the system. Then, the resource allocation problem is proposed to reduce the outage probability of HCNs. This problem is formulated as a matching game, and a stable matching is considered to be the solution. Finally, to address this matching problem, a distributed algorithm is proposed which can find a stable matching. Both analytical results and simulations show that the proposed algorithm improves the overall performance of HCNs. [ABSTRACT FROM AUTHOR]

With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultradense networks (UDNs), user-centric overlapped clustering relying on AP cooperation is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each user equipment. This ambitious goal can be achieved by the joint design of user-centric overlapped clustering and resource allocation. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) have to be carefully considered in UDNs. To tackle these challenges, we formulate a joint user-centric overlapped clustering and resource allocation problem with the goal of maximizing the system's spectral efficiency. To efficiently solve this problem, it is decoupled into two subproblems, which can be solved independently with the aid of our graph–theoretical framework. We first develop a novel distributed three-stage user-centric clustering solution, which is aware of the APs’ traffic-loads. Then, based on the overlapped clusters constructed, we propose a novel two-stage graph-based user-centric resource allocation scheme for mitigating the resultant inter-cluster interference, despite the limited availability of orthogonal RBs. Our numerical results confirm the benefits of the proposed framework and show that it outperforms the benchmark solutions in terms of both its per area aggregated user rate and user rate. [ABSTRACT FROM PUBLISHER]

Wireless energy transmission is considered in the Internet-of-Things (IoT) dual-hop communication systems. The power splitting method is employed at the relay node to receive the wireless energy and the wireless information simultaneously from the source to the relay node in the first resource block. With the harvested energy at the relay node, one wireless information node and one wireless energy harvest node are served simultaneously by the relay node in the second resource block. The optimization problem is formulated to maximize the energy efficiency defined as the ratio of the wireless information throughput of the first destination node over the power consumed by the whole IoT systems, which is subject to the required minimum energy harvested at the second destination node of the energy transfer. To tackle the non-convex problem, the exact expression of the wireless information throughput is approximated by the high signal to noise ratio (SNR) approximation method, which is shown very tight to the exact expression. Hence, the asymptotically optimal solution is derived in analytical expression by employing the Lagrangian method. Numerical simulations verify the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Reactive oxygen species (ROS) induced oxidative stress leads to cell damage and neurological disorders in astrocytes. The gap junction protein connexin43 (Cx43) could form intercellular channels in astrocytes and the expression of Cx43 plays an important role in protecting the cells from damage. In the present study, we investigated the contribution of Cx43 to astrocytic necrosis induced by the ROS hydrogen peroxide (H2O2) and the mechanism by which AMPK was involved in this process. Fluorescence microscopy, flow cytometry, and western blot were used quantitatively and qualitatively to determine the cell apoptosis, necrosis, and protein expression. Lack of Cx43 expression or blockage of Cx43 channels resulted in increased H2O2-induced astrocytic necrosis, supporting a cell protective effect of functional Cx43 channels. Our data suggest that AMPK is important for Cx43-mediated ROS resistance. Inhibition of AMPK activation results in reduction of necrosis and ROS production. Taken together, our findings suggest that the role of Cx43 in response to H2O2 stress is dependent on the activation of AMPK signaling pathways and regulates ROS production and cell necrosis. [ABSTRACT FROM AUTHOR]

For limited feedback precoding systems with the feedback delay, the accurate channel state information at the transmitter is crucial for the system performance. We first model the one-dimensional subspaces spanned by the direction vector of MISO time-varying channel as points on the Grassmannian manifold $$G_{n,1}$$ , and propose the single-stream predictive beamforming algorithm on the Grassmannian manifold $$G_{n,1}$$ . Next, we extend the algorithm to the spatial multiplexing MIMO time-varying channel and propose multiple-stream predictive precoding algorithm by modeling the column spaces spanned by principal right singular matrix of the channels on the Grassmannian manifold $$G_{n,p}$$ . More specifically, by exploiting the differential geometric properties of the Grassmannian manifold, we propose an adaptive tracking codebook on the tangent space of Grassmannian manifold to improve the quantization accuracy. The computer simulation results show that the proposed precoding scheme exhibits promised system performance over previous precoding scheme for both transit beamforming MISO system and spatial multiplexing MIMO system. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while maximum-ratio (MR) combining is adopted for the remaining APs. A corresponding AP selection scheme based on a novel proposed metric representing inter-user interference is proposed. We also propose a new fairness coefficient taking SE performance into account to indicate the UE fairness. Moreover, the performance of the proposed scheme is investigated under fractional power control and max–min fairness (MMF) power control. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. It is also shown that compared with the benchmark LP-MMSE combining, the proposed location-aided combining trades 13.45 % average SE loss for 26.61 % UE fairness improvement and 28.58 % complexity reduction when γ = 0.6 . And by adjusting the threshold γ , a good trade-off between the average SE, UE fairness and computational complexity can be provided by the proposed scheme. Furthermore, the proposed scheme with fractional power control can better demonstrate the advantages of trade-off performance than MMF power control and full power transmission. [ABSTRACT FROM AUTHOR]

Random interleavers are widely employed in digital communication systems to combat channel fading and burst errors. In applications such as grant-free access by Internet of Things (IoT) devices, accurately identifying a specific irregular interleaving pattern within an interleaver period is vital to both terminal recognition and data recovery. In this work, we investigate effective approaches for random interleaving pattern identification in Reed-Solomon (RS) coded data streams. We first propose an algorithm of low computational complexity to detect positions of code symbols belonging to the same RS codeword group (RSCG) under modest bit error rate. We further develop another low-complexity algorithm to successfully identify random interleaving patterns for RS code symbols within each RS codeword under moderate to high error rate applications. Our theoretical analysis and simulation results corroborate to demonstrate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

• Novel physical insights are provided into second-order weight error bounds of both strictly linear (SL) and widely linear (WL) estimators for noncircular Gaussian data with both mean square error (MSE) and Gaussian entropy criteria. • By employing the strong uncorrelating transform (SUT), which allows for a joint diagonalization of both the input covariance matrix and the complementary covariance matrix, the boundedness of complementary weight error variances for different estimators are discussed. • A joint consideration of both the standard and complementary weight error variance analyses is shown to be capable of measuring weight error power distribution of these estimators over real and imaginary data channels, an important finding which is not possible to obtain through the standard variance analysis only. Novel physical insights are provided into second-order weight error bounds for both strictly linear (SL) and widely linear (WL) estimators for noncircular Gaussian data, under both mean square error (MSE) and Gaussian entropy criteria. This is achieved by first defining complementary weight error variances of these estimators and by further exploiting the so obtained additional degrees of freedom, related to complex noncircularity. Next, the strong uncorrelating transform (SUT) is employed for a joint diagonalization of both the input covariance matrix and the complementary covariance matrix, to allow for the boundedness of complementary weight error variances for different estimators to be addressed. Furthermore, a joint consideration of both the standard and complementary weight error variance analyses is shown to make it possible to measure weight error power distribution of these estimators over both real and imaginary data channels, an important finding which is not possible to obtain through the standard variance analysis only. Simulations in both system identification and channel estimation settings support the analysis. [ABSTRACT FROM AUTHOR]

With increasing traffic congestion and accidents, the Internet of Vehicles (IoV) has become a focus of fifth generation (5G) heterogeneous networks (HetNets) and intelligent transportation systems (ITS). IoV has been studied extensively to improve vehicles, road safety, and efficiency. However, the limited spectrum efficiency and interference caused in the 5G HetNets may require robust technology and architecture. The enabling requirements of IoV in 5G HetNets are elaborated and conceptualized in the article. The deep learning algorithms proposed and used in IoV in 5G HetNets can be achieved to reduce delay and improve transmission reliability. The research progress, advantages, and challenges of deep learning in 5G HetNets IoV are illustrated and explored in detail. Moreover, the overall framework of IoV in 5G HetNets is outlined and compared with the related research, which can become the Foundation for future development of intelligent IoV. [ABSTRACT FROM AUTHOR]

To meet the demand of green communications of ultradense mobile devices (MDs), an energy-efficient mechanism, jointly considering the computation offloading and resource management, is designed to minimize the network-wide weighted energy consumption under the delay constraints of MDs for ultradense heterogeneous networks. Such a mechanism tightly integrates with the adjustment of computation capability and transmission power of MDs. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we design an effective algorithm to solve it. Specifically, by utilizing the powerful global searching capability of genetic algorithm (GA) and the accurate local searching capability of particle swarm optimization (PSO), the adaptive GA with diversity-guided is firstly used for coarse-grained search, and then adaptive PSO is utilized for fine-grained search. After that, some detailed analyses on the convergence, computation complexity and parallel implement are provided for algorithms. Simulation results show that the designed algorithm can achieve a lower network energy consumption than other offloading algorithms in general. At the same time, the numerical simulation also reveals that the designed algorithm may be more suitable for ultradense networks than existing algorithms. [ABSTRACT FROM AUTHOR]

Abstract: The TiB2-contained composite Fe–B–C coatings are deposited by the plasma transferred-arc (PTA) powder surfacing process. The coating''s thermal ability, arc ablation resistance and wear resistance at high temperature were analyzed. It is concluded that TiB2-contained composite Fe–B–C coating having excellent wear resistance at 600 °C and tempering resistance at 900 °C. Furthermore, this coating can effectively resist the arc ablation (120 A arc currents) within 7 s. [Copyright &y& Elsevier]

Ultra-dense heterogeneous networks (UDHNs) have been widely regarded as a promising solution to enhance the reuse efficiency of spatial frequency and thus improve the overall network performance. In such networks, a load balancing problem caused by coexisting macrocells and small cells should be treated seriously, which means some effective user associations are essential for load balancing. To this end, we design two types of offloading (load balancing) schemes for UDHNs to maximize a logarithmic utility of long-term rates. To guarantee the load balancing gain, a frequency partitioning scheme is designed to degrade the cross-plane interference, and a time partitioning strategy is developed to eliminate the strong interference received by some users offloaded from macrocells. In these offloading schemes, the main difference between them is whether a time partitioning factor needs to be optimized. As for the problems formulated in these schemes, we design a distributed algorithm by utilizing dual decomposition and develop a centralized algorithm with a two-layer iteration. Then, we give some detailed convergence and complexity analyses for them. Numerical results show that the proposed schemes yield some significant performance gains relative to some traditional ones, and the centralized algorithm often achieves a better association performance than a distributed one since the former almost always tries to optimize a time partitioning factor. [ABSTRACT FROM AUTHOR]

Thanks to comprehensive potential with new access technology, massive bandwidths and ultrahigh reliability, it is highly expected that recent 5G network and beyond 5G network (B5G) will fundamentally influence various autonomous systems, applications and devices. Also, Artificial intelligence (Ai) is expanding its applicability to numerous existing and next generation systems and research branches. Conversely, Ai can be utilized to threaten 5G-enabled components as well as to attack Ai-based systems and machine learning algorithms. So, it is indispensable to investigate new Ai-based security threats and solutions in 5G-enabled smart cities. in this article, we introduce Ai-driven 5G integrated virtual emotion system, called 5G-i-VEmoSYS, which covers Ai-VEmoBAR, Ai-VEmoFLOW, and Ai-VEmoMAP as applications in 5G environments. First, we explain how to operate those Ai-driven promising applications in 5G-enabled components including autonomous vehicles, smart UAVs or drones. Also, we describe how to perform those applications in public areas and private areas with different perspectives. Then, we carefully deal with research challenges, security threats and issues according to the proposed 5G-i-VEmoSYS. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation. [ABSTRACT FROM AUTHOR]

Proposed by Maddah-Ali and Niesen, a coded caching scheme has been verified to alleviate the load of networks efficiently. Recently, a new technique called placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, we consider a caching system in the scope of ultra dense networks (UDNs). Each base station (BS) has a finite cache and stores some contents. We propose an efficient coded content caching scheme called double coded caching to make the transmission robust to in-and-out wireless network quality. Then the dynamic caching and multicast scheduling are considered to jointly minimize the average delay and power of the content-centric wireless networks. This stochastic optimization problem can be formulated as a Markov decision process (MDP) with unknown transition probabilities and large state space. We propose a deep reinforcement learning approach to deal with the decision problem. Our algorithm uses a variational auto-encoder (VAE) neural network to approximate the state sufficiently, and uses a weighted double Q-learning scheme to reduce variance and overestimation of the Q function. Numerical results demonstrate that the proposed double coded caching scheme increases the probability of the successful transmission, and the caching and scheduling policy can effectively reduce the delay and the power consumption. [ABSTRACT FROM AUTHOR]

In this paper, we devote to maximize the secrecy energy efficiency (SEE) for a cloud radio access network where exists an eavesdropper wiretapping information from remote radio head. We first establish the system model and then formulate the power optimization problem to maximize the SEE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form and two algorithms are then proposed to solve it. Simulation results indicate that the proposed the iterative optimization algorithms can obtain higher SEE and present the merit of AN in enhancing the system performance when compared with conventional methods. [ABSTRACT FROM AUTHOR]

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution, i.e., Nash equilibrium (NE) point, based on the derived conditions of the uniqueness of NE, which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Instead of achievable rate in the conventional association, we utilize the long-term rate to design two types of association strategies for load balancing in heterogeneous cellular networks (HCNs). In the first type, we try to balance the network loads by maximizing the weighted sum of long-term rates. Based on this type, we introduce a power control (PC) to mitigate the network interference and reduce the energy consumption in the second type. Since the long-term rate of any user associated with some base stations (BSs) mainly depends on the achievable rate received by this user from the selected BS and the load of this BS, it can be used as a key factor of association design for load balancing in HCNs. To solve the first association problem, we design a one-layer iterative algorithm by converting the original sum-of-ratio form into a parameterized polynomial one. By combining it with a PC algorithm, we give a two-layer iterative solution for the second problem. Specifically, the outer layer performs a user association using the algorithm mentioned in the first type, and the inner layer updates the transmit power of each BS using a power update function. At last, we give some convergence and complexity analyses for these proposed algorithms. As shown in numerical results, the proposed strategies have a superior performance than the conventional association, and the second association strategy achieves a higher load balancing gain and energy efficiency than conventional scheme and other offloading strategies. [ABSTRACT FROM AUTHOR]

Cylindromatosis (CYLD) is a deubiquitinating enzyme that regulates multiple key signaling pathways involved in the pathophysiology of knee osteoarthritis (KOA). Previous studies have indicated that the expression of CYLD in the articular cartilage of patients with KOA is significantly higher than in healthy controls. However, limited data are available regarding the association between CYLD expression and the severity of KOA. The aim of the present study was to investigate the association between CYLD expression in joint tissues and the severity of KOA. A total of 156 individual tibial plateau samples were obtained between January 2011 and January 2016 from patients that had undergone total knee arthroplasty due to KOA or from healthy controls. The severity of KOA was evaluated using the Kellgren Lawrence (KL) and Mankin scoring systems. Additionally, CYLD expression in the articular cartilage and subchondral bone was analyzed using immunohistochemistry. Compared with the healthy controls, patients with KOA exhibited significantly increased CYLD levels in the articular cartilage (6.53±2.01 vs. 28.69±13.23, P<0.001) and significantly decreased CYLD levels in the subchondral bone (11.46±2.34 vs. 3.50±2.54, P<0.001). Correlation analysis indicated that CYLD expression in the articular cartilage was positively correlated with the KL (r=0.837, P<0.001) and Mankin scores (r=0.925, P<0.001), while its expression in the subchondral bone was negatively correlated with the KL (r=-0.802, P<0.001) and Mankin scores (r=-0.844, P<0.001). The results of the present study demonstrate that CYLD levels in the articular cartilage and subchondral bone are associated with the severity of KOA. Thus, CYLD may be a potential diagnostic and predictive biomarker for KOA and a novel target in its treatment. [ABSTRACT FROM AUTHOR]

Aims: The aim of this conventional case-control study was to investigate the prevalence and relationship between Helicobacter pylori infection in type 2 diabetes mellitus (DM) and diabetic nephropathy (DN).Methods: A total of 241 type 2 DM patients and 69 non-diabetic subjects with dyspeptic symptoms were enrolled in the study. Gastroduodenal lesions were observed by gastrointestinal endoscopy and the presence of H. pylori was identified by rapid urease test and serum IgG antibodies to H. pylori. According to the urinary albumin excretion rate (UAE), patients were classified into diabetes mellitus group (DM group, with UAE <30 mg/24h); diabetic nephropathy group 1 (DN group 1, with UAE 30 mg/24 h to <300 mg/24 h); and diabetic nephropathy group 2 (DN group 2 ≥ 300 mg/24 h). The 69 non-diabetic subjects were used as control group. The serum levels of inflammatory factors such as tumor necrosis factor-α (TNF-α) and interleukin (IL)-8 were determined using ELISA.Results: The prevalence of H. pylori infection in DN group 1 and DN group 2 was 45/72 (62.5%) and 34/53 (64.15%), respectively, which was significantly higher than in control [28/65 (43.1%)] and DM groups [42.9% (27/63)]. No significant differences of H. pylori prevalence were detected between DN groups as well as DM and control groups. Interestingly, in both DN groups, higher levels of IL-8, TNF-α and urinary albumin excretion rate were found in H. pylori positive subjects.Conclusions: Diabetic nephropathy patients are more susceptible to H. pylori infection. Our data support an association between H. pylori infection and diabetic nephropathy. [ABSTRACT FROM AUTHOR]

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions. [ABSTRACT FROM AUTHOR]

• miR-200c overexpression upregulated PAI-2 in TNBC. • miR-200c/PAI-2 increased macrophages. • miR-200c/PAI-2 promotes the progression of TNBC by inducing macrophage polarization. To investigate the effect of miR-200c/PAI-2 on macrophage polarization into M2-type TAMs in TNBC. PAI-2 expression in MDA-MB-231con, MDA-MB-231miR-200ab and MDA-MB-231miR-200c breast cancer cells was evaluated by RT-PCR and immunofluorescence (IF), while the expression of the TAM marker F4/80 and the M2-type TAM marker CD206 in MDA-MB-231con, MDA-MB-231miR-200c and MDA-MB-231miR-200c/siPAI-2 mouse lung metastatic tumor tissues was examined with immunohistochemistry (IHC). The effects of RAW264.7 cells on MDA-MB-231con, MDA-MB-231miR-200c and MDA-MB-231miR-200c/siPAI-2 were examined by transwell co-culture. CD206 expression in RAW264.7 cells were confirmed by immunostaining. The level of PAI-2 and IL-10 in the co-culture supernatants were assessed using ELISA. 1. RT-PCR and IF analysis showed that PAI-2 was upregulated in MDA-MB-231miR-200c cells. 2. IHC assays analysis showed that the numbers of F4/80 and CD206 positive cells were increased in MDA-MB-231miR-200c tumor tissues, while in MDA-MB-231miR-200c/siPAI-2 tumor tissues were decreased. 3. Transwell co-culture assays analysis showed that MDA-MB-231miR-200c cells significantly promoted the cell migration ability compared with the control group, while knockdown PAI-2 significantly inhibited the cell migration ability (P < 0.05). 4. Transwell co-culture and immunostaining assays analysis showed that overexpression miR-200c in MDA-MB-231 cell line increased the CD206 expression in RAW264.7 cells, while knockdown PAI-2 decreased. 5. ELISA assays analysis showed that miR-200c-mediated MDA-MB-231 cells significantly increased the secretion of PAI-2 and IL-10, while decreased the secretion of PAI-2 and IL-10 in MDA-MB-231 miR-200c/siPAI-2 cells. miR-200c promotes the malignant progressions of TNBC by PAI-2 upregulation and M2 phenotype macrophages polarization. [ABSTRACT FROM AUTHOR]

• The jointly optimization can be significantly higher than the literature. • The proposed scheme's performance is better than only transmit power optimization. • The energy efficiency decreases with parameter a and b increasing. • The joint optimization can improve the overall system energy efficiency. • The proposed scheme can reduce the system BER effectively and significantly. For the ambient backscatter communication system, a joint optimization algorithm based on the energy efficiency maximization criterion was proposed in this paper by designing a two-hop wireless information and energy transmission scheme. The source side of this system was configured with multiple antennas and employed Max Ratio Transmission (MRT) precoding method. The relay node (hot spots or tag node) utilized the power split approach to transmit information and harvest energy. In allusion to the established transmission system with large scale nodes, a joint optimization algorithm was applied to the number of source antennas, the transmission power and power splitting factor to obtain the maximum energy efficiency of the system. Using the high SNR approximation method transformed the non-convex into convex to solve the non-convex problem in the optimization target. The optimal solution of joint optimization for transmit power, antennas' number and power splitting factor was derived by using Lambert function function. Finally, the simulation results show that the proposed joint design achieves significant energy efficiency gain as compared to other optimized schemes. [ABSTRACT FROM AUTHOR]