Software Defined Radio Technology Research Articles

Sub-6 GHz beamforming with low-cost software-defined radio: Design, testing, and performance evaluation

The rapid evolution of mobile communication systems has led to the introduction of novel technologies and techniques that address existing challenges, such as interference caused by multiple users and transmission through the millimetre-wave band. In this paper, we present a novel approach that utilizes a low-cost software defined radio (SDR) unit to enable transmitting and receiving beamforming in the sub-6 GHz band. We provide a detailed account of the underlying concepts and background of beamforming and SDR, outline the design of multiple components, and report on the comprehensive testing and verification of a real-life prototype using over-the-air (OTA) methods. Our results demonstrate that the proposed approach achieves outstanding performance, with measurements of bit error rate (BER) that outperform existing solutions. In summary, this work contributes to the ongoing efforts to enhance mobile communication systems by providing an innovative, high-performance solution that is cost-effective and reliable using SDR technology in the sub-6 GHz band.

A complete artificial intelligence pipeline for radio frequency energy prediction in cellular bands for energy harvesting systems

Radio Frequency (RF) energy harvesting has been used to power wireless and low-powered devices. However, RF energy harvesting has limitations in terms of the amount of power that can be collected based on signal availability. Hence, energy prediction is essential to improve energy harvesting circuits' performance. Previous research has mainly focused on improving power harvesting policies or theoretically estimating the harvested energy. Very few works have considered the prediction of the RF signal as time series data using real RF measurements. Moreover, challenges such as the power consumed by the circuit's harvesting decisions and the impact of outliers on the model performance haven't been addressed yet. This paper presents a complete pipeline for developing the best predictive model for RF energy in cellular frequency bands. Real-time measurements are taken in different frequency bands using software-defined radio technology. We use four artificial intelligence techniques to model the RF energy signal. Additionally, we propose an optimized model with an enhanced loss function, which makes the model more resilient to anomalies, saving computational power and time consumed in cleaning the data. The four algorithms are investigated, and their prediction accuracies are compared. The average power of a period of 5 min is accurately forecasted. Numerical results in the 1960 MHz band show that long short-term memory has the best performance, followed by the DeepAR algorithm with prediction accuracies of 95.76% and 95.02%, respectively. Moreover, the proposed optimized model showed a 32.2% lower prediction error than the traditional models.

Preliminary results of scintillation monitoring at KLEF-Guntur low latitude station using GNSS software defined radio

Abstract Global Navigation Satellite Systems (GNSS) have become an integral part of modern life, supporting various applications, from precise positioning and navigation to timing and synchronization. However, GNSS signals are vulnerable to natural interferences including various atmospheric disturbances, with ionospheric scintillations being a significant challenge. Ionospheric scintillations, caused by irregularities in the Earth’s ionosphere, introduce rapid fluctuations in the amplitude and phase of GNSS signals. These fluctuations can severely degrade the accuracy and reliability of GNSS receivers, leading to positioning errors and navigation failures. Hence, it is crucial to develop effective mitigation strategies. One of the promising approaches to mitigate ionospheric scintillations is the utilization of Software Defined Radio (SDR) technology in GNSS receivers. SDR allows for real-time adaptation to changing signal conditions, enabling the receiver to detect scintillations and adjust its signal processing accordingly. This adaptability enhances the receiver’s stability against ionospheric disturbances, ensuring more robust and accurate positioning and navigation. In this paper, preliminary results of GNSS SDR (Make: iP-Solutions, Japan) installed at Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram (Guntur) (16.44° N, 80.62° E) are presented. Amplitude scintillation index (S4) variations for different PRNs and subsequent positioning results are interpreted from April to September 2023. The results are compared and validated with those of the co-located Novatel GNSS receiver and NAVIC receiver. Most of the S4 variations correlate well with the S4 values from the Novatel and NAVIC receivers. S4 observations from the Septentrio receiver at Daytona Beach (Florida) are also presented. The results of SDR will be extended further for the development of scintillation mitigation algorithms. We plan to install an SDR and employ similar mitigation strategy at this location in the near future.

Reconfigurable graphene antenna for a network cognitive radio: A novel solution for X-band satellite communications

This study introduces a graphene-based antenna tailored for X-band satellite communication within a cognitive radio network. Leveraging graphene's distinctive traits high conductivity and mechanical flexibility, the antenna design achieves reconfigurability in frequency, polarization, and radiation pattern. This adaptability enables dynamic adjustments to environmental shifts, bolstering the efficiency and dependability of satellite communication. Operating on software-defined radio technology, the cognitive radio system monitors and allocates the available frequency spectrum, allowing real-time adjustments to antenna parameters. Given the widespread usage of the X-band in satellite communications, this antenna proposal stands as an appealing solution for such systems. Its potential applications encompass providing broadband access in remote regions, supporting emergency communication networks, facilitating maritime and aeronautical communications, and enabling self-sustaining wireless networks. The approach involves a novel graphene antenna with slots to facilitate frequency reconfigurability. At 9 GHz, the graphene antenna achieves a gain of 6 dBi, surpassing the 4.3 dBi gain of a copper antenna. By varying the chemical potential of graphene from 0 to 4 eV, we achieved a high gain of 6.4 dBi at 9 GHz and an excellent reflection coefficient of −42 dB at 11.63 GHz. The graphene antenna's improved gain and tuning capabilities surpass traditional materials, showcasing adaptability across diverse frequencies, promising versatility for varied communication needs, and enhancing overall system reliability and effectiveness.

Software Platform for the Comprehensive Testing of Transmission Protocols Developed in GNU Radio

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring.

Software Defined Radio, a perspective from education

The evolution of communication systems has brought about a paradigm shift, particularly in radiocommunications, where software has increasingly taken precedence over hardware. This transition has not only reduced implementation costs but has also significantly enhanced the flexibility of equipment architecture. A prime example of this trend is the emergence and consolidation of software-defined radio (SDR) technology in recent decades. This study provides a comprehensive contextualization of SDR technology, offering insights into its current state in terms of development tools and market equipment. Additionally, two learning scenarios are presented that employ different teaching methodologies. In one of these scenarios, communication theory is exclusively approached from a theoretical perspective. In the second scenario, knowledge acquisition is encouraged through the implementation of low-cost laboratories that incorporate SDR technology. The study indicates that implementing SDR technology boosts student motivation and learning, with 73.13% believing it enhances engineering education and 96% showing increased motivation. Those using SDR in practical laboratories perform better on knowledge tests, but statistical analysis shows that the difference is not statistically significant.

An Overview of Software-Defined Radio Technology in CubeSat Communications

Small satellites are becoming more popular because they are cost-effective, easy to assemble, and use commercially available parts. However, traditional ground stations that communicate with small satellites require a lot of hardware and are expensive to build. This makes it challenging to get the most out of small satellites' information. Software Defined Radios (SDRs) have been developed to reduce the cost of these ground stations by doing many of the tasks in software. Many universities and organizations are developing SDR ground stations to communicate with satellites in different orbits. Communication with satellites is critical to their development and operation. This paper proposes a ground station that uses SDR technology to communicate with one or more small satellites in Low Earth Orbit (LEO). The station is cost-effective, portable, and easily scaled, allowing it to acquire data from satellites.

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.

Software defined radio frequency sensing framework for Internet of Medical Things

The escalating demand for biomedical systems that can precisely diagnose and manage critical diseases underscores the need for innovative solutions. A non-invasive and intelligent Internet of Medical Things (IoMT) system emerges as a promising technology, potentially enabling physicians to assess patients with reduced health risks. The respiratory rate is a pivotal vital sign among the primary clinical assessments. The allure of Radio Frequency (RF) sensing lies in its ability to monitor respiratory patterns without direct contact. However, the practical implementation of such systems often necessitates supplementary hardware to manage the extensive data and radio functionalities, leading to concerns related to cost and feasibility. Software-Defined Radio (SDR) technology presents itself as a viable solution to these challenges. This research introduces a comprehensive framework for the IoMT system, aiming to diagnose respiratory abnormalities early through RF sensing and SDR technology. We employ a deep learning framework and compare its performance with traditional machine learning models to ensure reliable and precise classification of respiratory abnormalities. The achieved results underscore the superiority of deep learning frameworks over conventional machine learning models in classifying respiratory anomalies. Specifically, the deep learning framework exhibits exceptional performance in discerning the temporal dependencies and patterns inherent in respiratory abnormalities, achieving an average accuracy exceeding 98% for each respiratory abnormality classification.

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.

A Low-Cost High-Throughput C-Band Communication System for CubeSats

This paper presents the physical layer of a proprietary broadband communication system for CubeSats. The system operates in the C band (5.8 GHz), delivering at least 10 Mbps of the net user throughput. Operation at low elevation angles (and therefore low SNRs) and high Doppler shifts is made possible thanks to a sophisticated synchronization subsystem. The system can be adapted to propagation conditions experienced during a given visibility window by changing the signal bandwidth and coding rate. It is implemented using Software Defined Radio (SDR) technology. The system will be used in two missions that are scheduled for 2023 and 2024 and are planned in cooperation with the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” and SatRev S.A.

Bluetooth Low Energy Device Identification Based on Link Layer Broadcast Packet Fingerprinting

With the rapid development of the Internet of Things (IoT), wireless technology has become an indispensable part of modern computing platforms and embedded systems. Wireless device fingerprint identification is deemed as a promising solution towards enhancing the security of device access authentication and communication process in the IoT scenario. However, the extraction of features from the network layer and its upper layers often confront restrictions from specific devices: the association with a certain wireless network and the access to the plaintext of the payload. Meanwhile, Bluetooth Low Energy (BLE) packets have been encrypted above the link layer, which makes those features difficult to extract. To tackle these problems, we introduce a novel method to identify BLE devices based on the fingerprint features in the data link layer. Initially, the BLE packets are collected through a receiver based on software-defined radio technology. Then, fields that reflect device differences in BLE broadcast packets are extracted through traffic analysis. Finally, a MultiLayer Perceptron (MLP) model is employed to recognize the category of BLE devices. An experimental result on a dataset with 15 types of BLE devices shows that the identification accuracy of the proposed method can reach 99.8%, which accomplishes better performance over previous work.

Real-Time Statistical Measurement of Wideband Signals Based on Software Defined Radio Technology

The increase in channel bandwidth and peak-to-average power ratio (PAPR) of modern communication standards poses a serious challenge to performing channel power (CP) and complementary cumulative distribution function (CCDF) measurements in real-time using standard measurement solutions based on spectrum analyzers (SA). Recently, Software Defined Radio (SDR) technology has become a viable alternative to the conventional real-time spectrum monitoring approach based on SA due to its reduced cost. Therefore, in this paper, we propose a novel, innovative, agile and cost-effective solution to enable both CP and CCDF measurements on a state-of-the-art SDR platform. The proposed solution exploits the ability of the SDR equipment to access signal samples in the time domain and defines both CP and CCDF-type measurements. The two measurement functions are software implemented in GNU Radio by designing customized blocks and integrated into a graphical user interface. The proposed system was first tested and parameterized in a controlled environment using emitted signals specific to the IEEE 802.11ax family of wireless local area networks. After parameterization, the SDR-based system was used for over-the-air measurements of signals emitted in the 4G+, 5G and 802.11ax communication standards. By performing the measurement campaign, we have demonstrated the capabilities of the measurement system in performing real-time measurements on broadband channels (up to 160 MHz for IEEE 802.11ax). Altogether, we have proved the usability of CP and CCDF measurement functions in providing valuable insights into the power distribution characteristics of signals emitted by the latest communication standards. By exploiting the versatility of SDR technology, we have enabled a cost-effective solution for advanced real-time statistical measurements of modern broadband signals.

A tri-band terrestrial beacon digital receiver system for TEC measurements

The ionosphere is an important component of near-Earth space that has a significant impact on communication and navigation. Furthermore, the total electron content (TEC) of the ionosphere is a widely used parameter in near-Earth plasma environment studies. A design of a digital beacon receiver system for automatically measuring TEC is presented. Universal Software Radio Peripheral (USRP) hardware equipment with software-defined radio technology was used in the tri-band beacon digital receiver to receive and rapidly preprocess beacon signals from satellites in different frequency bands. A software system is built to implement the automatic control of the receiver and to enable data processing of the preprocessed data in order to obtain its phase information and derive the TEC. The tri-band digital beacon receiver system is experimentally proven to automatically detect and archive transit satellites in real time, automatically receive and process satellite beacon signals with recording, and automatically generate TEC measurement results.

A 94 GHz Pulse Doppler Solid-State Millimeter-Wave Cloud Radar

A 94 GHz pulse Doppler solid-state millimeter-wave cloud radar (MMCR), Tianjian-II (TJ-II), has been developed. It reduces the size and cost using a solid-state power amplifier (SSPA) and a single antenna. This paper describes the system design, including hardware and signal processing components. Pulse compression, segmented pulse, and dual pulse repetition frequency (PRF) technologies are employed to overcome the limitations imposed by the low power of the SSPA and the high frequency of 94 GHz. The TJ-II also features a dual-polarization, high-gain antenna for linear depolarization ratio detection and a time-division receive channel to improve channel consistency and save on costs. To achieve high flexibility and low interference in signal transmission and reception, the TJ-II uses software-defined radio technology, including direct digital synthesis, digital downconversion, and bandpass sampling. A series of Doppler power spectrum processing methods are proposed for detecting weak cloud signals and improving scene adaptability.

Advanced Military Radio Channels Monitoring System Based on Software Defined Radio Platform: Preliminary Analysis

Abstract The use of electromagnetic spectrum monitoring for military emissions is essential due to the mobility and diversity of military operations. A tactical communications system used in the military domain includes encrypted and unencrypted data, voice and video communications using wideband and narrowband waveforms. This research focuses on the development of a high capability monitoring system based on Software-Defined Radio (SDR) technology for monitoring military waveforms. The implemented system has been tested for its ability to detect both wideband military waveforms and narrowband frequency hopping waveforms. The proposed SDR-based system provides a cost-effective solution for military waveform monitoring that can be used for real-time monitoring and analysis of the electromagnetic spectrum.

Multi-Agent System for Rogue Drone Interception

A multitude of applications based on unmanned aircraft vehicles (UAVs), in conjunction with the UAVs' diminishing costs and increasing capabilities, present a major threat to public safety as well as critical infrastructure security, leading to the need for versatile and robust counter-drone solutions. This work presents a novel multi-agent jamming system (MUAV-JS), where a group of cooperative autonomous agents employ various algorithms (detection, tracking, jamming, and self-localization) to counter unauthorized drone operations. The proposed system employs a joint wireless jamming and cooperative positioning framework to best track and intercept the moving rogue drone, utilizing a relative positioning system based on signals of opportunity in conjunction with inertial and vision measurements. Software-defined radio technology is incorporated on the pursuer agent to achieve rogue drone GPS disruption, while at the same time the autonomous agents cooperate to compute the location estimate of the pursuer agent. In the presence of jamming, to improve the wireless communication performance of the autonomous agents, a cellular communication architecture is also used as an additional channel for control and information exchange. For evaluation purposes, a prototype multi-agent system is designed, implemented, and tested in a real-world environment to demonstrate its enhanced localization and jamming performance, when compared to single-agent approaches.

Snout: A Middleware Platform for Software-Defined Radios

The plethora of Internet of Things (IoT) protocols and the upcoming availability of new spectrum bands for wirelessly connected devices have made software-defined radio (SDR) technology increasingly useful to interact with radio-based communication. While SDR-based tools have grown in popularity in recent years due to their flexibility and adaptability towards new protocols, SDR software interfaces remain highly complex and technical, and inherently require specialized skillsets in digital signal processing (DSP) to operate. To address this problem, we present Snout, an SDR middleware platform that encapsulates and abstracts much of the current complexity in SDR toolchains. This allows SDR developers to create wireless networking applications usable by a wide range of users. For instance, network security professionals can monitor the IoT landscape across multiple protocols without needing to interact with the underlying software-defined DSP. Snout implements interfaces with common network analysis tools to allow for integration with traditional network security solutions, facilitating use cases such as traffic analysis or rogue device detection. Its software architecture enables scalability in terms of protocols and processing by modularizing the signal processing pipeline. To demonstrate Snout’s capabilities, we show how it can encapsulate GNU Radio flowgraphs, facilitate simultaneous multi-protocol scanning, and convert existing SDR-based protocol implementations into fully contained applications. We further demonstrate how Snout can handle GNU Radio flowgraphs with other signal processing software simultaneously. Through extensive experiments, we demonstrate that Snout incurs limited CPU performance overhead below 4% and a memory footprint below 100MB, and handles large amounts of events with sub-millisecond latency.

A Feasibility Study of Remote Non-Contact Vital Signs (NCVS) Monitoring in a Clinic Using a Novel Sensor Realized by Software-Defined Radio (SDR)

The COVID-19 outbreak has caused panic around the world as it is highly infectious and has caused about 5 million deaths globally. A robust wireless non-contact vital signs (NCVS) sensor system that can continuously monitor the respiration rate (RR) and heart rate (HR) of patients clinically and remotely with high accuracy can be very attractive to healthcare workers (HCWs), as such a system can not only avoid HCWs' close contact with people with COVID-19 to reduce the infection rate, but also be used on patients quarantined at home for telemedicine and wireless acute-care. Therefore, we developed a custom Doppler-based NCVS radar sensor system operating at 2.4 GHz using a software-defined radio (SDR) technology, and the novel biosensor system has achieved impressive real-time RR/HR monitoring accuracies within approximately 0.5/3 breath/beat per minute (BPM) on student volunteers tested in our engineering labs. To further test the sensor system's feasibility for clinical use, we applied and obtained an Internal Review Board (IRB) approval from Texas Tech University Health Sciences Center (TTUHSC) and have used this NCVS monitoring system in a doctor's clinic at TTUHSC; following testing on 20 actual patients for a small-scale clinical trial, we have found that the system was still able to achieve good NCVS monitoring accuracies within ~0.5/10 BPM across 20 patients of various weight, height and age. These results suggest our custom-designed NCVS monitoring system may be feasible for future clinical use to help combatting COVID-19 and other infectious diseases.

MELODY: A Platform-Agnostic Model for Building and Evaluating Remote Labs of Software-Defined Radio Technology

MELODY: A Platform-Agnostic Model for Building and Evaluating Remote Labs of Software-Defined Radio Technology