Software-defined Radio Devices Research Articles

Enhancing counterfeit RFID tag classification through distance based cognitive risk control

In the realm of radio frequency identification (RFID) systems, combatting counterfeit tags through anti-counterfeiting technologies has garnered significant attention, particularly physical layer identification methods, lauded for their cost-effectiveness and simplicity in deployment. Nonetheless, the performance of physical layer recognition method is significantly impacted by the conditions of the tag detection setting, especially in scenarios characterized by low signal-to-noise ratio (SNR), where classification accuracy tends to suffer. To tackle this challenge head-on, this study proposes the implementation of a cognitive risk control strategy, which fine-tunes tag distance within the tag classification process to bolster the SNR and enhance recognition precision. Beyond the enactment of cognitive risk control, this paper extends its focus to encompass enriched time domain and frequency domain feature extraction, totaling 104 features, aimed at further enhancing classification efficacy. Leveraging software-defined radio devices, classification experiments encompassing seven popular tag types from three distinct manufacturers were conducted. Results from these experiments reveal that upon integrating the cognitive risk control strategy, the average accuracy of tag classification experiences an approximate 11% increase. Concurrently, in comparison to traditional twenty-eight and seven features, the adoption of one-hundred-and-four features translates to an enhancement in classification accuracy by roughly 4.3% and 5.3%, respectively. These findings not only underscore the efficacy of cognitive risk control in elevating label classification accuracy within low SNR environments but also underscore the potential for augmenting classification performance through an increased feature set.

Real-time modulation classification architecture in software defined radio

Real-time automatic modulation classification (AMC) in software-defined radio (SDR), an emerging approach to blindly detect the modulation scheme in future wireless networks with applications ranging from civilian to military, has not received much attention with practical receiver architecture. This paper proposes a simple modified receiver architecture using reduced Convolutional Neural Networks (CNN) to achieve AMC, which is tested on SDR devices under the bit-error-rate (BER) metric. The results show that the proposed receiver does not change the receiver’s original architecture in practice while achieving good BER performance. The results also show that the proposed low-complexity CNN model can fasten running time, suitable for real-time AMC applications.

A Software-Defined Radio Platform for Teaching Beamforming Principles

This paper presents the development and validation of a hybrid beamforming system based on software-defined radio (SDR), designed for telecommunications engineering education. The system provides an agile and user-friendly platform that allows students to observe, test, and evaluate beamforming techniques in real time. The platform integrates a multichannel SDR device (USRP N310) with traditional radiofrequency equipment and open-source software, facilitating hands-on learning experiences. The paper details the proposed hardware and software architecture and documents the calibration and validation phases. The testing and validation processes were conducted using a 3.5 GHz antenna array in both indoor and outdoor environments. The results demonstrated the system’s effectiveness in achieving the desired beam orientations, with experimental results aligning closely with simulation and theoretical predictions. Significant differences in the radiation patterns observed between the indoor and outdoor measurements were documented, highlighting the impact of environmental factors on beamforming performance. The insights gained from this research provide valuable contributions to the education of future telecommunications engineers, enhancing their understanding of practical beamforming applications and the integration of modern SDR technology.

A PAPR correction scheme for LoRa self‐jamming waveforms

AbstractA LoRa self‐jamming scheme that enables secure and covert communications by adding jamming symbols when transmitting is proposed. The scheme has been validated in simulations. The work is continued by evaluating the scheme on real‐world software defined radio (SDR) equipment. Experiments immediately showed that a demodulation is impossible as the received signal is highly distorted. This comes from the fact that adding symbols at transmission actually generates high peak‐to‐average power ratio (PAPR) preventing proper waveform generation by the equipment's hardware. This paper presents a PAPR correction scheme that modifies the waveform. The properties of this modified waveform are analysed and the performances are assessed in simulations with realistic time and frequency synchronization assumption. For the ideal 0 dB PAPR corrected output, the SER performances are close to that of a legacy LoRa communication, while preserving the physical layer security capabilities of the self‐jamming scheme. It is finally shown that our PAPR correction scheme effectively recovers the self‐jamming properties on SDR devices.

A Passive RF Testbed for Human Posture Classification in FM Radio Bands

This paper explores the opportunities and challenges for classifying human posture in indoor scenarios by analyzing the Frequency-Modulated (FM) radio broadcasting signal received at multiple locations. More specifically, we present a passive RF testbed operating in FM radio bands, which allows experimentation with innovative human posture classification techniques. After introducing the details of the proposed testbed, we describe a simple methodology to detect and classify human posture. The methodology includes a detailed study of feature engineering and the assumption of three traditional classification techniques. The implementation of the proposed methodology in software-defined radio devices allows an evaluation of the testbed’s capability to classify human posture in real time. The evaluation results presented in this paper confirm that the accuracy of the classification can be approximately 90%, showing the effectiveness of the proposed testbed and its potential to support the development of future innovative classification techniques by only sensing FM bands in a passive mode.

Implementation of In-Band Full-Duplex Using Software Defined Radio with Adaptive Filter-Based Self-Interference Cancellation

For next generation wireless communication systems, high throughput, low latency, and large user accommodation are popular and important required characteristics. To achieve these requirements for next generation wireless communication systems, an in-band full-duplex (IBFD) communication system is one of the possible candidate technologies. However, to realize IBFD systems, there is an essential problem that there exists a large self-interference (SI) due to the simultaneous signal transmission and reception in the IBFD systems. Therefore, to implement the IBFD system, it is necessary to realize a series of effective SI cancellation processes. In this study, we implemented a prototype of SI cancellation processes with our designed antenna, analog circuit, and digital cancellation function using an adaptive filter. For system implementation, we introduce software-defined radio (SDR) devices in this study. By using SDR devices, which can be customized by users, the evaluations of complicated wireless access systems like IBFD can be realized easily. Besides the validation stage of system practicality, the system development can be more effective by using SDR devices. Therefore, we utilize SDR devices to implement the proposed IBFD system and conduct experiments to evaluate its performance. The results show that the SI cancellation effect can reach nearly 100 dB with 10−3 order bit error rate (BER) after signal demodulation. From the experiment results, it can be seen obviously that the implemented prototype can effectively cancel the large amount of SI and obtain satisfied digital demodulation results, which validates the effectiveness of the developed system.

Performance Analysis of MIMO-OFDM System Using Predistortion Neural Network with Convolutional Coding Addition to Reduce SDR-Based HPA Nonlinearity

In recent years, the development of communication technology has advanced at an accelerated rate. Communication technologies such as 4G, 5G, Wi-Fi 5 (802.11ac), and Wi-Fi 6 (802.11ax) are extensively used today due to their excellent system quality and extremely high data transfer rates. Some of these technologies incorporate MIMO-OFDM into their protocol. MIMO-OFDM is widely used in modern communication systems due to its benefits, which include high data rates, spectral efficiency, and fading resistance. Despite these benefits, MIMO-OFDM has disadvantages, with the use of a nonlinear HPA being one of them. Nonlinear HPA causes in-band and out-of-band distortions in MIMO-OFDM signals. Utilizing predistortion (PD) is one way of solving this issue. PD is a technique that uses the inverse distortion of the HPA to compensate for the nonlinear characteristics of the HPA. To enhance the quality of MIMO-OFDM systems that the use of HPA has degraded, the convolutional coding (CC) method can be combined with the help of PD. Convolutional coding is a type of channel coding that can be used for error detection and correction. This study will evaluate a combined technique of PD neural networks (PDNN) and CC on the MIMO-OFDM system using Software Defined Radio (SDR) devices. The evaluation of this system led to the use of a technique that combines PDNN and CC to improve SNR and minimise BER on MIMO-OFDM systems that HPA on SDR devices has degraded. In addition, at code rates 1/2, 2/3, and 3/4, using PDNN reduces the SNR value required to achieve BER equal to 0 by 12.037%, 37.8%, and 4.10% when compared to Digital Predistortion (DPD).

Software-Defined Radio-Based Contactless Localization for Diverse Human Activity Recognition

This paper presents a study on contactless localization for activity recognition based on radio-frequency sensing. The focus of this study is on the quantitative analysis of the collected data, which is in the form of channel state information (CSI). The proposed method utilizes a software-defined radio (SDR) system in combination with an ensemble learning technique to localize and identify daily living activities such as leaning, sitting, standing and walking. Specifically, SDR device, Universal Software Radio Peripheral (USRP) models X300/X310 are utilized to collect data on the aforementioned activities. The data is collected from an empty space and a participant performing daily living activities in different territories. The acquired data is labelled based on the region, zone and performed activity. The CSI data is subsequently preprocessed and fed into an ensemble-based machine learning algorithm for classification. Furthermore, the stability analysis of the proposed method is performed to evaluate its reliability and robustness. The performance of the algorithm is evaluated using various metrics, including a confusion matrix, accuracy, cross-validation score and training time. The results demonstrate that the proposed ensemble-based approach achieves a high accuracy of up to 90% in activity recognition, highlighting the effectiveness of the proposed method in contactless localization for activity recognition.

Radio Frequency Fingerprint Identification Based on Metric Learning

With the popularization of the internet of things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. However, the problems of poor performance in a highly noisy environment and less consideration of calculation resources are urgent to be resolved in a practical RFF application domain. The authors propose a new RFF identification method based on metric learning. They used power spectrum density (PSD) to extract the RFF from the nonlinearity of the RF front end. Then they adopted the large margin nearest neighbor (LMNN) classification algorithm to identify eight software-defined radio (SDR) devices. Different from existing RFF identification algorithms, the proposed LMNN method is more general and can learn the optimal metric from the wireless communication environment. Furthermore, they propose a new training and test strategy based on mixed SNR, which significantly improves the performance of conventional low-complexity RFF identification methods. Experimental results show that the proposed method can achieve 99.8% identification accuracy with 30dB SNR and 96.83% with 10dB SNR. In conclusion, the study demonstrates the effectiveness of the proposed method in recognition efficiency and computational complexity.

Implementation and Performance Analysis of a Low Resolution OFDM System Prototype with Low Cost Hardware

The present work focuses on the implementation and analyzes of performance of a low-resolution OFDM system prototype with low-cost hardware. A software defined radio (SDR) system was chosen in this implementation due to its various advantages over a traditional radio system. Among the options of SDR devices available, the use of universal software radio peripherals (USRP) was avoided due to its high cost, despite its popularity in this field of research. Alternatively, a combination of two low-cost SDRs, "Hackrf One" and "RTL-SDR Blog V3" with the GNU RADIO, a popular, free and open source radio software, were used. Thus, it was possible to emulate the behavior of a low resolution A/D converter in the receiver, characterize its performance and estimate its energy savings. This allowed us to determine the feasibility of building a component with the analog-to-digital conversion function with few bits of resolution. We conclude that the performance of an A/D converter with at least 4 bits of resolution is pretty reasonable and that this reduction in the number of bits, in comparison to 8-bit A/D converter, represent a fairly expressive energy saving.

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition.

Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects.

Design of high‐speed software defined radar with GPU accelerator

Software defined radar (SDRadar) systems have become an important area for future radar development and are based on similar concepts to Software defined radio (SDR). Most of the processing like filtering, frequency conversion and signal generation are implemented in software. Currently, radar systems tend to have complex signal processing and operate at wider bandwidth, which means that limits on the available computational power must be considered when designing a SDRadar system. This paper presents a feasible solution to this potential limitation by accelerating the signal processing using a GPU to enable the development of a high speed SDRadar system. The developed system overcomes the limitation on the processing speed by CPU-only, and has been tested on three different SDR devices. Results show that, with GPU accelerator, the processing rate can achieve up to 80 MHz compared to 20 MHz with the CPU-only. The high speed processing makes it possible to run in real-time and process full bandwidth across the WiFi signal acquired by multiple channels. The gains made through porting the processing to the GPU moves the technology towards real-world application in various scenarios ranging from healthcare to IoT, and other applications that required significant computational processing.

Software-Defined Radio-Based Evaluation Platform for Highly Mobile IEEE 802.22 System

Wide-area communication systems using TV white space (TVWS) are promising technologies capable of full-coverage and cost-effective vehicular and transportation communications owing to their excellent propagation characteristics. IEEE 802.22 was developed as a TVWS-based fixed communication standard to support a wide coverage area of 10–30 km. To cope with the various applications in mobile communication, the transmission performance needs to be enhanced and evaluated. Thus, it becomes necessary to develop an experimental platform to enable the flexible implementation of transmitter and receiver algorithms. In this regard, we propose a software-defined radio (SDR)-based evaluation platform enabling the bench testing of physical layer (PHY) performance for highly mobile IEEE 802.22 communication systems. Using this platform, radio frequency signals can be transmitted and received using reconfigurable hardware and software, with the received baseband signals being recorded and stored by the SDR device. The stored data were post-processed using a MATLAB-based program to evaluate the transmission performance. The performance achieved by SDR-based evaluation system is similar to computer simulations, thereby demonstrating the feasibility of highly mobile IEEE 802.22 communication systems. Moreover, the proposed evaluation platform is available for future field experiments to encourage the development and evaluation of novel PHY algorithms.

Использование SDR технологии для задач сетевого позиционирования. Формирование опорных сигналов LTE

An analysis of the state of the problem of building prototypes of fourth and fifth generation network positioning technology, using the approach of model-based design and software-defined radio SDR (Software-Defined Radio) according to open foreign sources showed the high actuality and relevance of this direction. At the same time, insufficient attention is paid to this area of research and development in native sources. This paper proposes the layout of the developed SDR demonstrator of network positioning technology. The task of developing the demonstrator is to substantiate technical solutions to improve the accuracy of determining the location of devices in modern and future communication networks in the absence of signals from the global navigation satellite system. The purpose of using SDR technology for network positioning tasks is to get the opportunity to improve the developed technical solutions in the process of their actual operation. Developed demonstrator includes the following subsystems: a subsystem of eNB base station prototype with known coordinates that implements the generation and transmission of reference signals of the LTE standard; prototype of the user equipment UE, which implements the reception and processing of the reference signals of the LTE standard, as well as the primary processing of measurements using the reference signals of the LTE standard; eNB base station prototype synchronization subsystem, which implements the distribution of a timestamp signal; the SDR demonstrator control subsystem, which implements the control of the procedures for collecting primary measurements of network positioning and their secondary processing with the resulting estimate of the coordinates of the user equipment UE. Also, this article describes the results of software-hardware implementation and experimental testing of the LTE reference signal generator on USRP SDR devices in Matlab environment. The procedure for generating reference signals is uniquely determined by the cell identifier, and the generator itself is an integral part of the layout of the SDR network positioning technology being developed. Experimental testing of the reference signal generator in laboratory conditions using objective control tools showed correct determination of the group identificator and cell identificator within group, which uniquely determine initialized cell identifier.

Protecting COVID-19 Vaccine Transportation and Storage from Analog Cybersecurity Threats

Protecting COVID-19 Vaccine Transportation and Storage from Analog Cybersecurity Threats

Multi-link channel simulation based on propagation graph theory and application in performance evaluation of TDOA positioning system

In order to solve the problems of high measurement cost and many uncontrollable interference factors in the accuracy evaluation of time difference of arrival (TDOA) localization method by measurement, a method of evaluating the accuracy of a TDOA localization system based on multi-link channel propagation graph theory is proposed. In this paper, we use the propagation graph theory to generate different channel impulse responses, and ultilize software-defined radio devices to generate channel-distorted radio frequency signals as inputs to multiple TDOA sensors and construct a novel TDOA localization evaluation method. In order to verify the accuracy of the TDOA technology effectively, we simulate the method in 4 typical scenarios. It is the first time that a complex scenario is simulated using multi-channel radio frequency signal to test the localization accuracy of the TDOA system. The simulation result shows that when the signal bandwidth is not smaller than 200 kHz, the multi-link localization system based on graph modeling shows good and stable performance. Moreover, graph modeling method with diffraction model embedded enhances the accuracy of performance simulation for the TDOA system, particularly in the non-line-of-sight scenarios.

Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites

Software-defined radio (SDR) devices have made a massive contribution to communication systems by reducing the cost and development time for radio frequency (RF) designs. SDRs opened the gate to programmers and enabled them to increase the capabilities of these easily manipulated systems. The next step is to upgrade the reconfigurability into adaptability, which is the focus of this paper. This research contributes to improving SDR-based systems by designing an adaptable packet communication transmitter and receiver that can utilize the communication window of CubeSats and small satellites. According to the feedback from the receiver, the transmitter modifies the characteristics of the signal. Theoretically, the system can adopt many modes, but for simplicity and to prove the concept, here, the changes are limited to three data rates of the Gaussian minimum shift keying (GMSK) modulation scheme, i.e., 2400 bps GMSK, 4800 bps GMSK and 9600 bps GMSK, which are the most popular in amateur small satellites. The system program was developed using GNU Radio Companion (GRC) software and Python scripts. With the help of GRC software, the design was simulated and its behavior in simulated conditions observed. The transmitter packetizes the data into AX.25 packets and transmits them in patches. Between these patches, it sends signaling packets. The patch size is preselected. Alternatively, the receiver extracts the data and saves it in a dedicated file. It directly replies with a feedback message whenever it gets the signaling packets. Based on the content of the feedback message, the characteristics of the transmitted signal are altered. The packet rate and the actual useful data rate are measured and compared with the selected data rate, and the packet success rate of the system operating at a fixed data rate is also measured while simulating channel noise to achieve the desired Signal-to-Noise Ratio (SNR).

WNOS: Enabling Principled Software-Defined Wireless Networking

This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from well-understood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lower-level details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware. We first discuss the main architectural principles of WNOS. Then, we discuss a new approach to automatically generate solution algorithms for each of the resulting subproblems in an automated fashion. Finally, we illustrate a prototype implementation of WNOS on software-defined radio devices and test its effectiveness by considering specific cross-layer control problems. Experimental results indicate that, based on the automatically generated distributed control programs, WNOS achieves 18%, 56% and 80.4% utility gain in networks with low, medium and high levels of interference; maybe more importantly, we illustrate how the global network behavior can be controlled by modifying a few lines of code on a centralized abstraction.

SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security.

In order to secure wireless communications, we consider the usage of physical-layer security (PLS) mechanisms (i.e., coding for secrecy mechanisms) combined with self-interference generation. We present a prototype implementation of a scrambled coding for secrecy mechanisms with interference generation by the legitimate receiver and the cancellation of the effect of self-interference (SI). Regarding the SI cancellation, four state-of-the-art algorithms were considered: Least mean square (LMS), normalized least mean square (NLMS), recursive least squares (RLS) and QR decomposition recursive least squares (QRDRLS). The prototype implementation is performed in real-world software-defined radio (SDR) devices using GNU-Radio, showing that the LMS outperforms all other algorithms considered (NLMS, RLS and QRDRLS), being the best choice to use in this situation (SI cancellation). It was also shown that it is possible to secure communication using only noise generation by the legitimate receiver, though a variation of the packet loss rate (PLR) and the bit error rate (BER) gaps is observed when moving from the fairest to an advantageous or a disadvantageous scenario. Finally, when noise generation was combined with the adapted scrambled coding for secrecy with a hidden key scheme, a noteworthy security improvement was observed resulting in an increased BER for Eve with minor interference to Bob.

A Clustering Approach for Jamming Environment Classification

A hierarchical clustering architecture is proposed to deal with the problem of jamming environment classification when multiple noise-like jammers are possibly present. Assuming the availability of clutter-free multichannel data, a two-level hierarchical procedure is devised to unveil the presence of clusters containing range cells experiencing the same jamming interference as the cell under test. Level 1 relies on the use of covariance smoothing and model-order selection rules to make inference on the number of jamming signals affecting each range bin within the radar range swath. Level 2 allows to discriminate among possible different interfering scenarios characterized by the same number of jammers via an unsupervised learning clustering fed by a suitable feature set. At the analysis stage, the performance of the devised architecture is investigated over simulated and measured data (via software-defined radio devices) to highlight the benefits of the approach.