Dynamic Radio Frequency Research Articles

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

Information-Theoretic Bayesian Inference for Multi-Agent Localization and Tracking of an Radio Frequency Target With Unknown Waveform

Abstract Information-theoretic motion planning and machine learning through Bayesian inference are exploited to localize and track a dynamic radio frequency (RF) emitter with unknown waveform (uncooperative target). A target-state estimator handles non-Gaussian distributions, while mutual information is utilized to coordinate the motion control of a network of mobile sensors (agents) to minimize measurement uncertainty. The mutual information is computed for pairs of sensors through a four-permutation-with-replacement process. The information surfaces are combined to create a composite map, which is then used by agents to plan their motion for more efficient and effective target estimation and tracking. Simulations and physical experiments involving micro-aerial vehicles with time difference of arrival (TDOA) measurements are performed to evaluate the performance of the algorithm. Results show that when two or three agents are used, the algorithm outperforms state-of-the-art methods. Results also show that for four or more agents, the performance is as competitive as an idealized static sensor network.

A Simple Yet Effective Metric for Assessing Doppler Tolerance

The Doppler tolerance of a waveform refers to its behavior when subjected to fast-time Doppler shift imposed by scattering that involves nonnegligible radial velocity. The delay/Doppler ambiguity function characterizes this behavior, with notable attributes of Doppler-dependent mismatch loss (relative to zero Doppler) and possible offset in delay estimation. Previous effort has gone into establishing decision-based criteria where a binary judgement of being Doppler tolerant or not is made. Here, we consider the utility of a simple, yet effective measure of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">degree</i> of Doppler tolerance arising from a fundamental attribute of the ambiguity function that is also convenient to extract. This metric is examined in the context of a variety of different waveform classes including traditional linear/nonlinear chirps, noise-like signals, and phase codes. Extension to wideband operation is likewise considered. The purpose in doing so is to establish a consistent standard that can be readily applied, thereby permitting easy assessment across different parameterizations, as well as introducing a Doppler “quasi-tolerant” trade-space that can ultimately inform automated/cognitive waveform design in increasingly complex and dynamic radio frequency (RF) environments.

A Multi-layered GaGeTe Electro-Optic Device Integrated in Silicon Photonics

Electrically tunable devices contribute significantly to key functions of photonics integrated circuits. Here, we demonstrate the tuning of the optical index of refraction based on hybrid integration of multi-layered anisotropic GaGeTe on a silicon micro-ring resonator (Si-MRR). Under static applied (DC) bias and transverse-electric (TE) polarization, the device exhibits a linear resonance shift without any amplitude modulation. However, for the transverse-magnetic (TM) polarization, both amplitude and phase modulation is observed. The corresponding wavelength shift and half-wave voltage length product <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(V_{\pi} . l)$</tex-math></inline-formula> for the TE polarization are -1.78 pm/V and 0.9 V.cm, respectively. These values are enhanced for the TM polarizations and correspond to - 6.65 pm/V and 0.28 V.cm, respectively. The dynamic radio frequency (RF) response of the devices was also tested at different bias conditions. Remarkably, the device exhibits a 1.6 MHz and 2.1 MHz response at 0 V and 7 V bias, respectively. Based on these findings, the integration of 2D GaGeTe on the silicon photonics platform has great potential for the next generation of integrated photonic applications such as switches and phase shifters.

Closing the Loop on Cognitive Radar for Spectrum Sharing

We investigate three emerging topics essential for the development of cognitive radar (CR) for spectrum sharing: the response time (RT) of the CR, the autonomous regulation of the perception-action cycle (PAC), and the regulation of cognition. The RT measures the latency of all algorithms/hardware and is examined with respect to enabling capabilities of software-defined systems for rapid flexibility and responsiveness. The autonomous regulation of the PAC determines “how fast the CR can interact with the environment” as well as “how fast the CR should interact with the environment.” The regulation of the PAC is explored with respect to pulse-to-pulse waveform agility to coexist successfully with dynamic radio frequency emitters in the ambient electromagnetic environment (EME) and the consequence of modifying the waveform within the coherent processing interval. Finally, the regulation of cognition determines how to select a particular CR technique appropriately for a given dynamic environment. This selection requires a high level, or metadecision process to identify the appropriate CR technique as the EME changes over time and we therefore concentrate discussion on the newly emerging topic for radar called metacognitive radar. The exploration of these three topics include a review of past and current research with discussion of possible future research.

Machine Learning for RF Slicing Using CSI Prediction in Software Defined Large-Scale MIMO Wireless Networks

In this paper, we investigate the machine learning approaches (sparse Bayesian linear regression (SBLR) and support vector machine (SVM)) for channel state information (CSI) prediction and dynamic radio frequency (RF) slicing for software defined virtual wireless networks in large-scale multi-input multi-output (MIMO) wireless networks. Specifically, a subset of the antennas of virtual wireless networks transmits pilot symbols for estimating the CSI and use the estimated CSI dataset to train and estimate the remaining channels and future CSI for virtual networks using machine learning algorithms. This helps not only to predict the CSI with least overhead and fulfills the service demands of users but also to reduce the power consumption and computation overhead in the network. Predicted CSI is leveraged for RF slicing for virtual wireless networks. Simulation results show that the proposed SBLR for predicting CSI results in lower BER and higher data rate for the wireless users. Furthermore, SBLR outperforms the other approaches when we have sparse CSI information and we need to generalize the prediction process.

Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks.

In recent years, unmanned aerial vehicles (UAVs) have been considered an ideal relay platform for enhancing the communication between ground agents, because they fly at high altitudes and are easy to deploy with strong adaptabilities. Their maneuvering allows them to adjust their location to optimize the performance of links, which brings out the relay UAV autonomous mobility control problem. This work addressed the problem in a novel scene with mobile agents and completely unknown wireless channel properties, using only online measured information of received signal strength (RSS) and agent positions. The problem is challenging because of the unknown and dynamic radio frequency (RF) environment cause by agents and UAV maneuvering. We present a framework for both end-to-end communication and multi-agent-inter communication applications, and focus on proposing: (1) least square estimation-based channel approximation with consideration of environment effects and, (2) gradient-based optimal relay position seeking. Simulation results show that considering the environmental effects on channel parameters is meaningful and beneficial in using UAV as relays for the communication of multiple ground agents, and validate that the proposed algorithms optimizes the network performance by controlling the heading of the UAV.

Improving Spectrum Efficiency of Cell-Edge Devices by Incentive Architecture Applications With Dynamic Charging

The gap between the peak-hour Internet and the average level is increasing, which inevitably creates a type of temporary cellular weak coverage when there is a surge in data traffic demand, where any cell-edge device will have a low spectrum efficiency (SE). In this article, we propose a novel incentive architecture based on the dynamic radio frequency charging technology to improve the SE and use the Stackelberg game theory to formulate the problem. In such a model, a small base station (SBS) acts as the leader to offer a desired partition of the resource block obtained by a cell-edge device, while some small energy providers (SEPs) and small virtual access points (SVAPs) that are selected from user equipment act as the followers to make their decisions, respectively, to compete for the free part of such a resource block. Following the potential game rules, all the SEPs compete for a specific free resource part allocated by the SBS, and then, all the SVAPs compete for another nonoverlapping part allocated by the SBS on the basis of the results of the SEPs' potential game. Although our incentive architecture formally has three game stages, it is essentially a two-level Stackelberg game, which is analyzed by using a backward induction method. The theoretical analysis proves the convergence of the above-mentioned game models, and the simulation results demonstrate that the proposed incentive architecture can improve the SE for each cell-edge device.

Influence of multipactor discharge on field-buildup process in radio-frequency plate cavity

In this paper, the hybrid physical model is established based on the equivalent circuit for describing dynamic radio-frequency (RF) field buildup and the particle-in-cell (PIC) method for describing two-sided multipactor discharge in plate cavity. By using our built 1D3V-PIC code for multipactor discharge and fully equivalent circuit code for RF field buildup, the influence of multipactor discharge on the dynamic process of RF field buildup is numerically investigated and analyzed in detail under the condition of cavity with different Q-values. The numerical results could be concluded as follows. Under the condition of no multipactor discharge in dynamic process of RF field buildup, the higher the Q-value, the longer the buildup-time is. The input energy is equal to the sum of stored energy and consumed energy in cavity, the speed of energy storing is higher than the speed of energy consuming at the beginning stage of RF field buildup and then the speed of energy storing becomes lower than the speed of energy consuming. When the process of RF field buildup is finished, the average power of input is equal to the average power of consumed power in cavity. Under the condition of multipactor discharge loading in dynamic process of RF field buildup, the higher the Q-value, the later the start-time is and the longer the interaction time-interval of multipactor discharge is. The bigger the area of secondary electron emission, the higher the peak-value of secondary electron current is. The failure of RF field-buildup is caused by the continuous loading of multipactor discharge. The higher the Q-value or the bigger the area of secondary electron emission, the lower the probability of RF field buildup success is. The simulated results could partly provide a reference for engineering design.

A scale‐independent way for differential estimation in dynamic radio frequency identification systems

SummaryCardinality estimation in radio frequency identification systems has been applied to estimate the population of tags in many applications. However, it is more meaningful to estimate the number of tags moved in and out in a dynamic radio frequency identification system, which is called differential estimation problem. Zero differential estimator is a newly proposed algorithm to solve this problem. However, the time slots consumed by zero differential estimator are relevant to the system scale under the accuracy constraint. This will result in low time efficiency when the system scale is very large. In this paper, we thus propose a scale‐independent algorithm for differential estimation called zero‐one differential estimator. The numbers of tags moved in and out are estimated from the idle slots in two consecutive frames. We can prove that the time slots consumed in our proposed algorithm are not relevant to the system scale under the accuracy constraint. Moreover, we conduct abundant simulations to evaluate the performance of the proposed approach. The simulation results show that the estimation error grows little as the system scale grows. It indicates that our proposed algorithm is indeed scale‐independent. Copyright © 2015 John Wiley &amp; Sons, Ltd.

An integrated framework for RFID adoption and diffusion with a stage-scale-scope cubicle model: A case of Indonesia

This study presents a comprehensive framework to identify dynamic radio frequency identification (RFID) adoption and diffusion from three different perspectives: stages of adoption, levels of analysis, and domain of issues. The main concern of this study is stages of adoption, which covers three phases in respect of the maturity of the RFID project and the sophistication of business applications and RFID technology. The level of analysis involves different units of analysis beyond the organization level, including the industry- and country-level, which is lacking in the current literature. To understand dimensions of RFID issues, a strategy, technology, organization, people and environment (STOPE) based approach was applied. An extensive review of prior literature was conducted to find various RFID success factors and the Delphi method was applied to find positions of these factors within the framework. Based on the Delphi, some factors belong to early stage of adoption, and some others persist in the later stages of adoption. At the country level, factors such as RFID national policy, R&D policy and income per capita were accepted by most experts at the preliminary and intermediate stage; strategy and environment were accepted as important domains. To find practical implications of the framework, a case study of Indonesia was conducted at each level of analysis. Analytic hierarchy process (AHP) was applied to identify most important factors and important domains of issues with respect to the factors from the Delphi results. At country level, RFID policy, vision of leadership and RFID potential market are the most important factors; strategy and environment are the most important domains.

Effective Suppression of IV Knee Walk-Out in AlGaN/GaN HEMTs for Pulsed-IV Pulsed-RF With a Large Signal Network Analyzer

IV knee walk-out in AlGaN/GaN high electron mobility transistors (HEMTs) on a Sapphire substrate is analyzed using dynamic radio frequency (RF) load-lines acquired with a large signal network analyzer (LSNA) for both continuous-wave (CW) and pulsed-IV/RF excitations. When thermal effects and traps are bypassed using pulsed-IV biasing and pulsed-RF excitations, the IV knee walk-out observed in CW load-lines is found to be effectively suppressed and the device delivers the maximum output power expected for class A operation. It is also demonstrated using pulsed-IV/RF measurements at various substrate temperatures that the IV knee walk-out primarily arises from thermal effects at high bias rather than trapping in the on-wafer devices characterized

A compact, high-resolution Paul ion trap mass spectrometer with electron-impact ionization

A Paul ion trap has been developed for use as a high-resolution mass spectrometer. It is of small size (r0=10 mm), having a resolution of m/Δm=324, which is limited by the machining accuracy of the trap. It has a demonstrated mass range of 1–300 u, and a sensitivity of 2×1014 counts/Torr s, or to 500 parts per trillion detection sensitivity in a typical vacuum of 10−5 Torr. Ionization of the room-temperature gas within the trap is carried out with an electron beam traversing the trapping volume. The trap operates in a radio frequency only mode, and no dc is required. Trapping is accomplished within the well depth of the dynamic radio frequency potential, and no cooling gas is required, such as helium. This combination of factors makes the trap potentially of use for autonomous operation in harsh environments requiring low power, low weight, and low volume, such as undersea, on the surface of a planet or asteroid, or in a spacecraft.

Table-based dynamic FET model assembled from small-signal models

A data-table-based large-signal metal-semiconductor field-effect transistor model is presented based on an ensemble of bias-dependent small-signal equivalent circuits. The model is capable of accurate simulation of small-signal S-parameters as well as large-signal performance over the data-acquisition bias range. In addition to the dc current sources, the model contains two quasi-static charge sources and a dynamic radio frequency (RF) current source, which depends on the temperature rise. By introducing the dynamic RF current source, the problem of path dependence, which occurs in modeling large-size devices and devices with dispersion, is resolved. All equivalent elements are obtained by cubic spline interpolation. Extrapolation of the model beyond the measurement range is taken into account. The model is extracted by an in-house software without involving optimization. The validity of the model is demonstrated by comparing the simulation of small-signal S-parameters over a wide bias range, as well as power, linearity, and waveform characteristics to the measured data.

Microwave radar and electronic environment simulator

A facility capable of direct synthesis of radar systems and their electromagnetic environments operating concurrently with missile flight simulations, range instrumentation sim ulations, and related test-range experiments has been needed for many years. This paper describes the design, development, and ap plication of a general-purpose Radar and Electromagnetic Environment Simulator operating under the combined control of digital and analog computers. This simulator is one of the more recent developments of the Analyses and Computation Directorate, White Sands Missile Range. In this system the combined operation of computers and microwave hardware is utilized to create realistic dynamic radio-frequency environmental effects based on time de lay, doppler shift, and RF-signal-power equations com bined with simulated radar and missile flight geometry dynamics and kinematics. Associated radar antenna pat terns, target backscatter patterns, clutter, and other target- induced RF phenomena are included. The simulator includes a flexible system for simulating RF signal generation and receiver signal-processing and provides a broad range of adjustment of radar parameters. The simulated transmitter signals are propagated through the computer-controlled environment simulation and are received and tracked by the receiver signal-processing equipment as in normal radar operations. Prototype radar subsystems and components, tracking beacons, jammers, counter-countermeasure receivers, and many other devices can be coupled into the simulator to be studied within the confines of the simulation laboratory.