Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks

Abstract Non-Orthogonal Multiplex Access (NOMA) can be deployed in Unmanned Aerial Vehicle (UAV) networks to improve spectrum efficiency. Due to the broadcasting feature of NOMA-UAV networks, it is essential to focus on the security of the wireless system. This paper focuses on maximizing the secrecy sum-rate under the constraint of the achievable rate of the legitimate channels. To tackle the non-convexity optimization problem, a reinforcement learning-based alternative optimization algorithm is proposed. Firstly, with the help of successive convex approximations, the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization tools. Afterwards, through plenty of explorations on the wireless environment, the Q-learning networks approach the optimal location transition strategy of the UAV, even without the wireless channel state information

A Robust Method for GPS/BDS Pseudorange Differential Positioning Based on the Helmert Variance Component Estimation

The use of global navigation satellite system (GNSS) is entering a new era of joint positioning based on the use of multifrequencies and multimodes. Ensuring the correct weighting of observations from each system and satellite has become a key problem during real-time positioning. This paper addresses the issue of weights of observations as well as the quality control of GPS/BDS pseudoranges in the context of real-time relative positioning. Thus, in the first place, the Helmert variance component estimation (VCE) is used to determine the relative weighting of observations from the two systems, and then, we introduce robustness estimation theory and construct a new method. The method is resistant to the influence of outliers in the observations by selecting weight iterations. To do this, we selected GPS/BDS observation data at baseline lengths of 40 km, 46 km, and 64 km for verification and analysis. Experimental results show that, in terms of the relative positioning of medium-to-long baseline based on GPS/BDS pseudorange observations, when observed values incorporate large gross errors, our method can reduce the weighting of suspicious or abnormal values and weaken their impact on positioning solutions, so that the positioning results will not appear to have large deviation

Link Selection in Buffer-Aided Cooperative Networks for Green IoT

In this paper, the link selection algorithm in buffer-aided cooperative networks with multiple co-channel interferers for green internet of things (IoT) is investigated. In the considered system, the relay node selects the better link from the first and the second hop with the help of buffers. We evaluate the effects of the system parameters by deriving the analytical analysis as well as the asymptotic results on the exact expressions of the outage probability. The impacts of system parameters, such as the predefined target rate, the priority-outage tradeoff parameter, the buffer length and the number of interferers are evaluated in different setup scenarios. Considerable performance gains can be obtained compared with the classical decode-and-forward relaying networks. Simulation results are provided to verify the theoretical analysis

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis

BACKGROUND: To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. RESULTS: We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. CONCLUSION: PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s)

Deep Learning Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks

Perfect channel state information (CSI) is required in most of the classical physical layer security techniques, while it is difficult to obtain the ideal CSI due to the time varying wireless fading channel. Although imperfect CSI has a greatly impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to valuate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas are investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning based detectors compared with the classical maximum likelihood algorithm

Sum Rate Analysis of MU-MIMO with a 3D MIMO Base Station Exploiting Elevation Features

Although the three-dimensional (3D) channel model considering the elevation factor has been used to analyze the performance of multiuser multiple-input multiple-output (MU-MIMO) systems, less attention is paid to the effect of the elevation variation. In this paper, we elaborate the sum rate of MU-MIMO systems with a 3D base station (BS) exploiting different elevations. To illustrate clearly, we consider a high-rise building scenario. Due to the floor height, each floor corresponds to an elevation. Therefore, we can analyze the sum rate performance for each floor and discuss its effect on the performance of the whole building. This work can be seen as the first attempt to analyze the sum rate performance for high-rise buildings in modern city and used as a reference for infrastructure

Improving yields by switching central metal ions in porphyrazine-catalyzed oxidation of glucose into value-added organic acids with SnO2 in aqueous solution

Photocatalysis has exhibited huge potential in selective conversion of glucose into value-added chemicals. Therefore, modulation of photocatalytic material for selective upgrading of glucose is significant. Here, we have investigated the insertion of different central metal ions, Fe, Co, Mn, and Zn, into porphyrazine loading with SnO2 for access to more efficient transformation of glucose into value-added organic acids in aqueous solution at mild reaction conditions. The best selectivity for organic acids containing glucaric acid, gluconic acid, and formic acid of 85.9% at 41.2% glucose conversion was attained by using the SnO2/CoPz composite after reacting for 3 h. The effects of central metal ions on surficial potential and related possible factors have been studied. Experimental results showed that the introduction of metalloporphyrazine with different central metal ions on the surface of SnO2 has a significant effect on the separation of photogenerated charges, changing the adsorption and desorption of glucose and products on the catalyst surface. The central metal ions of cobalt and iron contributed more to the positive effects toward enhancing conversion of glucose and yields of products, and manganese and zinc contributed more to the negative effects, resulting in the poor yield of products. The differences from the central metals may attribute to the surficial potential change of the composite and the coordination effects between the metal and oxygen atom. An appropriate surficial potential environment of the photocatalyst may achieve a better interactive relationship between the catalyst and reactant, while appropriate ability of producing active species matched with adsorption and desorption abilities would gain a better yield of products. These results have provided valued ideas for designing more efficient photocatalysts in selective oxidation of glucose utilizing clean solar energy in the future