Radio frequency fingerprint collaborative intelligent blind identification for green radios

Radio frequency fingerprint identification (RFFI) technology identifies the emitter by extracting one or more unintentional features of the signal from the emitter. To solve the problem that the traditional deep learning network is not highly adaptable for the contour features extracted from the signal, this paper proposes a novel RFFI method based on a deformable convolutional network. This network makes the convolution operation more biased towards the useful information content in the feature map with higher energy, and ignores part of the background noise information. Moreover, a distributed federated learning system is used to solve the problem of insufficient number of local training samples for a multi-party joint training model without exchanging the original data of the samples. The federated learning center receives the network parameters uploaded by all local models for aggregation, and feeds the aggregated parameters back to each local model for a global update. The proposed blind identification method requires less information and no training sequences and pilots. Thus, it achieves energy-efficiency and spectrum-efficiency. Simulation verifies that the proposed method can achieve better recognition performance and is beneficial for green radio

Modeling Impact of Weather Conditions on 5G Communication and Mitigation Measures on Control of Automated Intersections

69A3551747117Recent research activities are focused on improving Vehicle-to-Vehicle Communication (V2V) based on the 5G Technology. V2V applications are important because they are expected to reduce the risk of accidents up to 80%, enhance traffic management, mitigate congestion, and optimize fuel consumption. Typical autonomous vehicle applications require a high bandwidth transmission channel, so the 5G communication channel might be a reliable solution to support this technology. The dedicated short-range communications (DSRC), characterized by a frequency bandwidth of 5.9 GHz, were used as vehicular connectivity with bandwidth up to 200 mb/s and limited capacity, and it is here utilized for comparison to 5G. The 5G band can support connected autonomous vehicles with higher data rates and larger bandwidth. The 5G communication channel is suitable for vehicular connectivity since it has a very high bandwidth in the millimeter waves spectrum range. The quality of 5G wireless communication channels between connected vehicles is affected by weather conditions such as rain, snow, fog, dust, and sand. In this report, the effect of dust and sand on the propagation of millimeter waves is presented. The effect of dust and sand on the communication path loss of DSRC and 5G frequency band is investigated in the case of Urban areas and Highway conditions. Results show that the attenuation caused by dust and sand depends on the particle size of sand, frequency of propagating wave, and concentration of dust. Finally, a new model of link margin is presented to estimate the effect of dust and sand on DSRC (5.9 GHz) and 5G (28 GHz-73.5 GHz) communication path loss

A system of systems engineering approach for unit commitment in multi-area power markets

In power systems, the main grid might be a group of several interconnected areas. The areas can be self-governing with their own polices and rules. According to the concept of system of systems (SoS) engineering, this paper presents a decentralized decision-making framework to determine an economical hourly generation schedule for a multi-area power system. Each self-governing area is modeled as an independent system, and the entire power system is modeled as a SoS. The proposed decentralized unit commitment algorithm takes into account the privacy of each independent system, and only a limited data information such as power exchange between the areas, needs to be exchanged between the systems. An iterative decentralized optimization model is presented to find the optimal operating point of all independent systems in the SoS-based power system architecture. The numerical results show the effectiveness of the proposed SoS framework and solution methodology

Intelligent passive detection of aerial target in space-air-ground integrated networks

Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks (SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is −36dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios

Conformal Array Pattern Synthesis and Activated Elements Selection Strategy Based on PSOGSA Algorithm

The pattern synthesis and activated element selection for conformal array is investigated based on hybrid particle swarm optimization-gravitational search algorithm (PSOGSA) in this paper. With the introduction of PSOGSA algorithm which is a novel hybrid optimization technique, the element excitations are optimized to obtain the desired pattern for conformal array in the case of considering uncoupled and coupled element pattern. Numerical simulation and full-wave electromagnetic calculation verify the advantage and efficiency of our method. Then, a novel strategy of activated element selection based on PSOGSA algorithm is proposed for saving the energy consumption in conformal array

A Hybrid Method of Adaptive Cross Approximation Algorithm and Chebyshev Approximation Technique for Fast Broadband BCS Prediction Applicable to Passive Radar Detection

A hybrid method combining the adaptive cross approximation method (ACA) and the Chebyshev approximation technique (CAT) is presented for fast wideband BCS prediction of arbitrary-shaped 3D targets based on non-cooperative radiation sources. The incident and scattering angles can be computed by using their longitudes, latitudes and altitudes according to the relative positions of the satellite, the target and the passive bistatic radar. The ACA technique can be employed to reduce the memory requirement and computation time by compressing the low-rank matrix blocks. By exploiting the CAT into ACA, it is only required to calculate the currents at several Chebyshev–Gauss frequency sampling points instead of direct point-by-point simulations. Moreover, a wider frequency band can be obtained by using the Maehly approximation. Three numerical examples are presented to validate the accuracy and efficiency of the hybrid ACA-CAT method

Cross-View Action Recognition via a Continuous Virtual Path

In this paper, we propose a novel method for cross-view action recognition via a continuous virtual path which con-nects the source view and the target view. Each point on this virtual path is a virtual view which is obtained by a linear transformation of the action descriptor. All the virtu-al views are concatenated into an infinite-dimensional fea-ture to characterize continuous changes from the source to the target view. However, these infinite-dimensional fea-tures cannot be used directly. Thus, we propose a virtual view kernel to compute the value of similarity between two infinite-dimensional features, which can be readily used to construct any kernelized classifiers. In addition, there are a lot of unlabeled samples from the target view, which can be utilized to improve the performance of classifiers. Thus, we present a constraint strategy to explore the information contained in the unlabeled samples. The rationality behind the constraint is that any action video belongs to only one class. Our method is verified on the IXMAS dataset, and the experimental results demonstrate that our method achieves better performance than the state-of-the-art methods. 1

Synergistic treatment of blast furnace slag and basic oxygen furnace slag for efficient recovery of iron: Phase transformation and oxidation mechanisms

In light of the steel industry's rapid advancements, the availability of high-quality mineral resources is diminishing. Therefore, the recovery of iron from BOF slag is of great significance to the sustainability development. Considering the compositional characteristics of BOF slag, the transformation of the iron-containing phase into (Mn,Mg)yFe3-yO4 is the key step. Thus, a novel process for recovering iron resources by synergistic treatment of blast furnace slag (BFS) and BOF slag was proposed. This research employed FactSage thermodynamic simulation, XRD, SEM-EDS, XPS, and EPMA to analysis the impact of BFS addition (10–50 %), cooling methods (from water-cooling to furnace-cooling), and temperature (1400–1600 °C) on phase transformation and the RO oxidation mechanism, and the conditions of (Mn,Mg)yFe3-yO4 generation and enrichment was obtained. The results show that at BFS addition of 30 %, reaction temperature 1400 °C and furnace-cooling, the iron-containing phase (Ca2Fe2O5 and RO) was almost completely transformed into (Mn,Mg)yFe3-yO4. The oxidation mechanism of RO was formation of (Mn,Mg)yFe3-yO4 by cation diffusion. Under optimal conditions, the iron recovery rate and the grade reached 65.74 % and 32.07 %, respectively, which can be used as raw material for ironmaking. Meanwhile, the main phase of the tailing slag was β-Ca2SiO4, without f-CaO, which has the potential to be used in the cement and concrete industries with the advantages of both low cost and eco-friendly. Therefore, the process with green, efficient and low cost was provided, which is a feasible idea for the comprehensive utilization of industrial solid waste

Tunable broadband transmissive terahertz cross-polarization converter enabled by a hybrid metal-graphene metasurface

Graphene has shown potential in terahertz (THz) polarization modulation due to highly tunable optoelectronic properties, fast photoelectric response, and ease of integration. However, the performance of polarization converters based on graphene metasurfaces is often limited by the achievable carrier mobility of large-area graphene. In this paper, a flexible and tunable broadband transmissive THz cross-polarization converter based on a hybrid metal-graphene metasurface is proposed. It is composed of two metal grating layers with a graphene-loaded 45-degree antenna array sandwiched between them. The THz response of the antenna can be tuned by adjusting the graphene Fermi level, which further alters the cross-polarization conversion efficiency (CPCE) of the device. The average CPCE can be continuously tuned from 80.3% to 4.5% within a broadband from 0.6 to 2.0 THz, and the average modulation depth of the whole band is 94.2%. The mechanisms of this highly efficient polarization conversion and dynamic modulation are explained with a transfer matrix method and an equivalent circuit model. Furthermore, the proposed structure has a low requirement on the graphene mobility, which is only 500 cm2/(V·s) here. This work provides a new approach to highly efficient tunable cross-polarization conversion over a broadband in THz, which will promote the application of graphene-based polarization modulators in THz sensing, imaging and communication