Software Radio Research Articles

Direction of Arrival Estimation of Meteors Echoes using Array Radio Antennas

Array antennas have an interesting role in the radio astronomy field. The array antennas allow astronomers to obtain high-resolution signals with high sensitivity to weak signals. This paper estimates the meteors' positions entering the Earth's atmosphere and develops a simulation for array antenna radar to analyze the meteor's echoes. The GNU radio software was used to process the echoes, which is a free open-source software development toolkit that provides signal processing blocks to implement in radio projects. Then, the simulation determines the azimuth and elevation of the meteors. An improved Multiple Signal Classification (MUSIC) algorithm has been suggested to analyze these echoes. The detected power of each meteor echo has a Doppler frequency shift due to the high speed of the meteors, which impacts the accuracy of the Direction of Arrival (DOA) estimation. The Doppler shift was considered in this simulation, and the results showed that the suggested method has low complexity and high resolution and can estimate the meteors' position with the minimum error.

Design an RF Up-Down Convertor using Software Defined Radio and GNU Radio

There is a need to design of RF Up/Down- converter for (from C/Ku to/from L band) signals required for several applications. The proposed work presents a design of an RF Up/Down Converter that makes use of GNU Radio and Software-Defined Radios (SDRs). The converter helps in frequency translation between the C/Ku and L bands, leading to a cost-effective and versatile solution for RF signal processing. By using open-source GNU Radio software, the proposed system enhances accessibility, enabling its deployment in diverse applications, from satellite communication to radar systems. The converter’s unique features include real-time processing capabilities, customization through an intuitive graphical interface, and Python scripting. This idea presents the design considerations, signal processing techniques, and performance evaluation of the RF Up/Down Converter. The advantages of an open-source solution over other available alternatives are in terms of cost, flexibility, and rapid prototyping. Simulation and hardware results demonstrate the efficacy of the proposed work.

Research direction towards exploring software control radio using machine learning algorithms for smart hotel applications with future aspects

ABSTRACT Various research works are focused on intelligent methodologies in the hospitality industry to promote real-time performance enhancement. Although the existing research works are focused on the design of hotel applications, they only estimated the consumers' acquired performance of a smart hotel and studied the customer details. The major research problem handled is the establishment of radio domain expertise into data structures and to process in real-time scenarios, which is carried out by integrating natural language processing and Machine Learning (ML) technologies into software radio. Owing to this flexibility, various opportunities are presented in various applications and thus, it helps in promoting research on hotel environments. The research work is done by gathering the set of recent research works for exploring the algorithmic and application-oriented SDR. Thus, this research explores the various applications and discussion on SDR with the following categories of an introduction to SDR, literature study on collected studies, problem statement, the algorithms used, expected results in terms of performance measures used, advantages and disadvantages of existing studies, and applications focused on SDR. Finally, the research challenges of traditional SDR applications and the future research direction on the smart hotel application-oriented SDR domain are discussed.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm.

Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.

Direction-finding for unmanned aerial vehicles using radio frequency methods

Nowadays, drones, also known as unmanned aerial vehicles (UAVs), are used for a wide range of purposes in the civil, industrial, and military domains. The greater the number of UAVs, the more serious the potential security risks correlated to them. To address these risks, it became necessary to develop systems that can detect, locate, and possibly neutralize such an object. The detection can be performed using different sensors such as imaging, radar, acoustic, and radio frequency (RF) sensors. The localization of the target drone, which can be done using direction finding (DF) methods, is an essential part of such a system. Thus, an RF-based DF testbed is designed and experimentally assessed in this paper. The testbed is implemented using software defined radio (SDR) platforms from the Universal Software Radio Peripheral (USRP) family as hardware equipment, and the MUSIC algorithm for processing the signals received on four different phase-coherent RF channels. A DJI Mavic Air UAV is used as target. For assessing the DF estimation performance, the direction obtained from the drone flight record is compared with the one estimated using the proposed DF testbed. Two different scenarios, a static one and a dynamic one, are considered to highlight the effectiveness of the proposed DF testbed across various conditions. The obtained results indicate an average estimation error of 1.15 degrees from the drone's actual position, in the case of the static scenario, and of 1.86 degrees in the case of the dynamic scenario, results which are on parity with or surpassed those of other drone DF systems described in the literature. The paper also outlines the benefits and challenges of the current approach.

A machine learning approach for package size estimation using UHF RFID interrogation signature

This paper introduces a new approach for performing package classification and sizing using Radio-Frequency Identification (RFID) systems. This technique is applicable when packages are labeled with or contain multiple RFID-tagged items. During the interrogation of the tags, received signal strength (RSS) statistics and other information, such as the frame count or the reading time, are collected by the reader and used to predict the package type from a set of candidate classes using an Artificial Neural Network (ANN). The primary challenge lies in acquiring sufficient training data for a target scenario to ensure reliable predictions. To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector.

Digital Signal Processing Oriented Electronic Communication Engineering Applications and Practices

This paper first describes the applications of digital signal processors in software radio, speech compression coding, modems and GPS systems, demonstrating the versatility of digital signal processors in practical communication systems. Next, digital signal processing oriented electronic communication practices are presented in the system design section to design high performance communication systems for specific communication needs and environmental conditions. The digital signal processing platform used in the platform architecture is optimized for signal processing and data transmission. The BER is 0.008% when the signal-to-noise ratio is -10 dB. The time complexity of digital signal processing is 7.2 ms when the number of communication nodes is 1000. It shows that digital signal processing oriented electronic communication engineering is important for improving the performance, reliability and efficiency of communication systems.

An efficient reconfigurable FIR filter design with coefficient optimization using a modified bacterial foraging optimization algorithm

ABSTRACT The digital filters play a significant role in the field of digital signal processing (DSP). The finite impulse response (FIR) filter is an attractive choice because of the ease of design and good stability. The digital filters have a wide variety of applications such as signal processing, control systems, telecommunication, etc. They are better than the analogue filters due to their performance. In recent times, software radios have achieved attention owing to requirements for integrated and reconfigurable communication systems. Hence, reconfigurations have emerged as a significant problem in the designs of FIRs. To match the frequencies of DSP applications, higher-order FIRs are required. If length of filters rises, addition and multiplication operations also increase. This paper proposes an efficient hardware design of RFIR that employs modified bacterial foraging optimizations (MBFOs) and common sub-expression eliminations (CSEs) in its executions. MBFOs output restricted counts of filter coefficients with sums of signed-power-of-two (SPT) terms while maintaining the quality of filtered responses. On obtaining coefficients, eliminations are executed by CSEs where hardware complexities are determined in terms of adders. Model sim software validated RFIRs using the Verilog code. The proposed design of RFIRs was compared with existing designs in terms of power usages, frequencies and areas.

A Private SDR 5G SA Network: An Evaluation of the Quality of Service (QoS) and Computational Resource Allocation for eMBB Slice

This paper assesses the network performance in terms of Quality of Service (QoS) for the enhanced Mobile Broadband (eMBB) slicing of a private Stand-Alone (SA) network and a base station, identified as Next-Generation Node B (gNB), which is operating at 3.75GHz and using a Software Defined Radio (SDR) device. The contributions of this work are the evaluation of the computational resource allocation of a private 5G SA and the comparison of the QoS performance of the private SA network with a controlled laboratory testbed and a simulation tool, which is known as UERANSIM (User Equipment and Radio Access Network). Additionally, open-source monitoring systems were used in order to collect memory and CPU usage of the network during the experimentation. For the deployment of the SDR 5G SA gNB, the Universal Radio Software Peripheral (USRP) version B210 equipment was utilized. The results also consider the evaluation of QoS metrics i.e. throughput, jitter, round trip time and propagation delay to assess the performance of the private SDR 5G SA network.

Реалізація алгоритму пошуку кореляційного інтерферометричного пеленгатора засобами програмної платформи GNU радіо

In the realm of modern radio equipment development, the integration of Software Defined Radio (SDR) systems, encompassing both software and hardware components, has become widespread. SDR systems, particularly, find applications in the creation of direction finders for radio signal sources. The effectiveness of direction finding and the identification of radio radiation sources largely hinge on the technical specifications of the equipment. For contemporary systems aimed at detecting the location of radio radiation sources, direction finders based on a correlation interferometer prove to be highly suitable. These direction finders excel in performing direction finding for a wide array of radio signals, capable of recognizing broadband modulated signals. They can simultaneously process and identify several signals within a single frequency channel, originating either from a singular source of radio signals (coherent) or from multiple sources (incoherent). SDR technology enables programmable reconfiguration of a radio receiver through software. Such receivers boast a broadband radio frequency component with a significant dynamic range, a high-speed Analog-to-Digital Converter (ADC) path, a robust signal processor, and a specialized digital filtering path. SDR receivers are adaptable to signals of various standards and frequencies, depending on the specific tasks at hand. The GNU RADIO software platform stands out as the most flexible open-source software platform for SDR systems. This article delves into the utilization of the GNU RADIO software platform to implement the search algorithm for the direction finder of the correlation interferometer. The Ettus USRP N210 SDR platform was selected for implementing the direction finder using the correlation interferometer algorithm. This study introduces a two-channel software-controlled model of the direction finder, operating according to the correlation interferometer algorithm. The measurement results for the placement angle of radio signal sources yielded an individual absolute error ranging from 2 to 4 degrees, with an average absolute error of about 3 degrees. These results are quite commendable for such mobile two-channel systems. While systems with four or more channels boast higher accuracy, they tend to have lower economic feasibility.

A novel self‐interference cancellation architecture based on asymmetrical power divider and its closed‐loop control

AbstractThe asymmetric radio frequency (RF) self‐interference cancellation (SIC) architecture is studied, and the closed‐loop control of RF SIC based on software radio is realized. This system uses a circulator to isolate the receiving and transmitting chains, and its RF reconstruction chain is composed of a 4‐tap filter with fixed delay and tunable attenuation for each tap. The optimization of tap filter and asymmetric power divider's power allocation ratio is discussed. In the experiment, sensing, calibration, optimization algorithms and hardware control programs were proposed. A closed‐loop gradient descent algorithm for tuning the variable attenuator is implemented on the GNU Radio. From 910 MHz to 930 MHz, the isolation between the receiving and transmitting ports increases 40 dB, achieving an isolation of more than 65 dB and the transmission energy efficiency increases by 60%.

Comparisons of soft tissue changes after Herbst appliance treatment in three different growth phases

This study aimed to compare the soft tissue effects of Herbst appliance in Class II malocclusion patients treated in three different craniofacial growth phases: prepubertal (PRE), circumpubertal (CIR), and postpubertal (POS). In total, 95 patients with Class II Division 1 malocclusion previously treated with a Herbst appliance were analyzed. Through the cervical vertebral maturation stages method, patients were allocated into three groups depending on the growth craniofacial phase at the beginning of treatment: PRE, CIR, and POS. Seventeen cephalometric measures were evaluated from each lateral radiograph before and after Herbst therapy using the Radiocef 2 software (Radio Memory, Belo Horizonte). Intragroup and intergroup treatment changes were compared statistically using a paired t test and MANOVA test, respectively. Soft tissue thickness changes were related only to mandible; all three mandibular measurements (L1_LL, B_B', and Pog_Pog') showed thickening for the PRE group ranging from 0.92 mm (Pog_Pog') to 2.02 mm (B_B'), and only lower lip thickened overtime for the POS group (L1_LL = 0.99 mm). Soft and hard tissue pogonion displaced anteriorly, but only the soft tissue showed differences among groups; PRE group presented more anterior displacement than POS group (3.61 mm and 1.39 mm, respectively). Hard and soft tissue facial convexity decreased more in the PRE and CIR groups than in the POS group. Mentolabial sulcus depth reduced more in the PRE (1.07 mm) and CIR (1.29 mm) groups than in the POS (0.55 mm) group. Horizontal movement of the skeletal and soft pogonion presented a moderate-high positive correlation (r = 0.783), and hard and soft facial convexity showed a moderate-low positive correlation (r = 0.403). Herbst appliance therapy produces soft tissue improvements in the three phases of craniofacial growth, being greater in patients in the PRE and CIR phases.

Path Loss Characterization in an Outdoor Corridor Environment for IoT-5G in a Smart Campus University at 850 MHz and 3.5 GHz Frequency Bands.

The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios.

Longer Term Time Dynamics of Bit Error Rates

n this paper an analysis is conducted to study the properties of indoor and outdoor wireless channel conditions over long time scales (several minutes). It shows that long term time dynamics exist in many different situations even when transmitters and receivers are stationary and the distance between them is only a few meters. We use software-defined radio (SDR) that implements radio communication functionalities in software. Transmissions have been achieved using the Universal Software Radio Peripheral (USRP), with the signal processing and time synchronization occurring in the GNU Radio environment on the LINUX (Ubuntu 14.04 LTS) platform. The work has been implemented in a typical school environment i.e., indoor conditions including a lab environment along with a hallway and outdoor conditions comprising a quadrangle environment. We used the benchmarking tool in the GNU Radio that defines and modifies modulation schemes, transmitter and receiver gains, operating frequencies, and other channel properties. Per-packet bit error rate is computed, visualized, and compared across multiple scenarios. We find a series of interesting time dynamics of the BER over several minutes that are not commonly studied in the literature.

CRB-based comparative study of array signal processing direction finding methods supported with SDR platform implementation

Radio Direction-Finding (DF) is an extremely important process in numerous commercial and military applications. Array signal processing-based DF methods have been developed to improve estimation performance in recent applications that require higher resolution and accuracy. Practically, angle of arrival (AOA) estimation is challenging due to various intrinsic and extrinsic parameters that affect the results. Therefore, clarifying these parameters and researching their impact on various DF methods are considered important research topics. The Cramer-Rao Bound (CRB) for estimating AOA is calculated based on a model of a linear antenna array system. This bound identify the factors that significantly impact the estimation performance. The current study conducts a comparative analysis of the most widely used array-signal-processing-based direction-finding (DF) methods under different conditions of the CRB parameters. According to the simulation results, Multiple Signal Classification (MUSIC) AOA estimator provides more accurate and stable results. Accordingly, it is chosen for implementation using a prototype testbed based on Software Defined Radio (SDR) technology using National Instruments Universal Software Radio Peripheral (NI-USRP) platform version 2930. Experimental outcomes were tested at various AOA values. The appendix includes the complete derivation for the CRB relation applied on the system model.

Hand-Breathe: Noncontact Monitoring of Breathing Abnormalities From Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method.

Robust OFDM System for Drilling Data Transmission Using Acoustic Wave

Drilling data communication mainly includes mud pulse telemetry and electromagnetic wave telemetry. These methods have the advantages of economy and convenience, but they also have the disadvantages of low data transmission rate and large signal attenuation. A data transmission system while drilling was designed based on the key technologies and basic principles of Orthogonal Frequency Division Multiplex (OFDM). The hardware of the system includes the Universal Software Radio Peripheral (USRP) motherboard and daughter board, power amplifier circuit and piezoelectric transducer. The software used in the system is C and Python. Compared with traditional methods, new methods using acoustic signals have many advantages. This new wireless transmission system was designed by combining down-hole data transmission with wireless communication. This method has advantages in oil exploration and development.

Ultrasonic vibration measurement using heterodyne interferometry and SDR phase meter

Ultrasonic vibration measurement and the development of ultrasonic vibration sensors have a significant impact on underwater pattern detection, providing good support for the exploitation and usage of marine resources. Advanced digital signal processing algorithms improve mechanical displacement measurements using ultrasonic speed and pm-level interferometers in real-time. In recent years, developing digital algorithms and employing low-cost software-defined radio (SDR) software applied to communication systems and other general-purpose systems with flexible solutions are essential in modern industrial applications. Specifically, the SDR can be consistent with real-time phase-change measurements of MHz-frequency interference signals for a heterodyne interferometer. This paper combines a heterodyne interferometer and a real-time SDR phase meter, demonstrating an ultrasonic vibration instrument's high-speed vibrating measurement capabilities. A double-pass interferometer is implemented to produce interference signals modulated with a sine waveform phase change associated with a tool's ultrasonic vibrating displacement, calculated by the phase meter's quadrature demodulation algorithm. The measurement results show that the system detects the sine-wave vibration trajectory generated by the vibrator at a frequency of 20 kHz and an amplitude of ~460 nm. The principle of the measurement system, instrumentations, experiments, and results are discussed in the paper.

Implementation of a 4G/5G Base Station Using the srsRAN Software and the USRP Software Radio Module

This article presents the potential applications and scenarios for the implementation of a software-defined radio (SDR) module operating as a base station in 4G/5G networks. The paper presents test configurations of the srsRAN software environment in conjunction with various types of programmable radio modules. Then, the key performance metrics of the mobile telephony system and potential problems that may be encountered while implementing hardware and software layers are presented.