Low-cost Software-defined Radio 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.

Frequency Instability Impact of Low-Cost SDRs on Doppler-Based Localization Accuracy.

In this paper, we explore several widely available software-defined radio (SDR) platforms that could be used for locating with the signal Doppler frequency (SDF) method. In the SDF, location error is closely related to the accuracy of determining the Doppler frequency shift. Therefore, ensuring high frequency stability of the SDR, which is utilized in the location sensor, plays a crucial role. So, we define three device classes based on the measured frequency stability of selected SDRs without and with an external rubidium clock. We estimate the localization accuracy for these classes for two scenarios, i.e., short- and long-range. Using an external frequency standard reduces the location error from 20 km to 30 m or 15 km to 2 m for long- and short-range scenarios, respectively. The obtained simulation results allowed us to choose an SDR with appropriate stability. The studies showed that using an external frequency standard is necessary for minimizing SDR frequency instability in the Doppler effect-based location sensor. Additionally, we review small-size frequency oscillators. For further research, we propose two location sensor systems with small size and weight, low power consumption, and appropriate frequency stability. In our opinion, the SDF location sensor should be based on the bladeRF 2.0 micro xA4 or USRP B200mini-i SDR platform, both with the chip-scale atomic clock CSAC SA.45s, which will allow for minor positioning errors in the radio emitters.

Practical frequency-hopping MIMO joint radar communications: design and experiment

Joint Radar and Communications (JRC) can implement two Radio Frequency (RF) functions using a single of resources, providing significant hardware, power and spectrum savings for wireless systems requiring both functions. Frequency-Hopping (FH) MIMO radar is a popular candidate for JRC because the achieved communication symbol rate can greatly exceed the radar pulse repetition frequency. However, practical transceiver imperfections can cause many existing theoretical designs to fail. In this work, we reveal for the first time the non-trivial impact of hardware imperfections on FH-MIMO JRC and model the impact analytically. We also design new waveforms and correspondingly develop a low-complexity algorithm to jointly estimate the hardware imperfections of unsynchronized receiver. In addition, using low-cost software-defined radios and Commercial Off-The-Shelf (COTS) products, we build the first FH-MIMO JRC experimental platform with simultaneous over-the-air radar and communication validation. Confirmed by simulation and experimental results, the proposed designs achieve high performance for both radar and communications.

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.

Low-cost software-defined radio for electrical engineering education

Low-cost software-defined radio for electrical engineering education

Comparison of Low-Cost 5G Electromagnetic Field Sensors

This paper compares different low-cost sensors that can measure (5G) RF-EMF exposure. The sensors are either commercially available (off-the-shelf Software Defined Radio (SDR) Adalm Pluto) or constructed by a research institution (i.e., imec-WAVES, Ghent University and Smart Sensor Systems research group (S³R), The Hague University of Applied Sciences). Both in-lab (GTEM cell) and in-situ measurements have been performed for this comparison. The in-lab measurements tested the linearity and sensitivity, which can then be used to calibrate the sensors. The in-situ testing confirmed that the low-cost hardware sensors and SDR can be used to assess the RF-EMF radiation. The variability between the sensors was 1.78 dB on average, with a maximum deviation of 5.26 dB. Values between 0.09 V/m and 2.44 V/m were obtained at a distance of about 50 m from the base station. These devices can be used to provide the general public and governments with temporal and spatial 5G electromagnetic field values.

Novel estimation technique for the carrier-to-noise ratio of wireless medical telemetry using software-defined radio with machine-learning

In this study, we developed a novel machine-learning model to estimate the carrier-to-noise ratio (CNR) of wireless medical telemetry (WMT) using time-domain waveform data measured by a low-cost software-defined radio. With automatic estimation of CNR, the management of the electromagnetic environment of WMT can be made easier. Therefore, we proposed a machine-learning method for estimating CNR. According to the performance evaluation results by 5-segment cross-validation on 704 types of measured data, CNR was estimated with 99.5% R-square and 0.844 dB mean absolute error using a gradient boosting regression tree. The gradient boosting decision tree classifiers predicted if the CNR exceeded 30 dB with 99.5% accuracy. The proposed method is effective for investigating electromagnetic environments in clinical settings.

A New RF Sensing Framework for Human Detection Through the Wall

A recent trend for human activity recognition, fall detection, health-care applications, etc., which is using the non-contact radio-frequency (RF) signals, is known as the RF sensing. State-of-the-art radar and Wi-Fi based human sensing systems utilize high bandwidth and require high computational burden. In this paper, a machine learning (ML) framework is proposed for device-free through-the-wall (TTW) human detection using narrow-band continuous RF signals that are transmitted and captured by low-cost software-defined radio (SDR) modules. To be more realistic the double TTW scenario, where both transmitter and receiver are placed outside the walls of the monitored area, is considered. Three different classes are defined as empty (human-free), moving human, and additionally the static human (only breathing, not moving) which addresses the unconscious or sleeping person. In this framework, the variance of the respiratory rate (RR) estimates of the subject and overall signal variances are used as distinguishing features. RR estimates are obtained using nonlinear least-squares (NLS) approach. It is shown in the experiments conducted with real measurements from different environments that the proposed ML framework achieves over 99% classification accuracy using a Decision Tree (DT) classifier. In addition, it is also shown that the proposed framework achieves over 95% accuracy in a completely different environment where the model is not trained. This indicates that the proposed ML framework can be applied generally without requiring specific re-training for different environments and conditions.

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.

Attack Detection and Mitigation in MEC-Enabled 5G Networks for AIoT

In recent years, AIoT (Artificial Intelligence of Things) applications have received lots of attention since it exploits widely deployed IoT devices to collect data and perform an action while leveraging AI to obtain knowledge and insights. When deploying AIoT in 5G networks, Multi-access Edge Computing (MEC) is appropriate for enabling local management of IoT devices and computation of machine learning (ML) algorithms. Facing the multitude of threats in the ML data layer, IoT service layer, and 5G communications layer, MEC should enable corresponding detection and mitigation schemes to protect AIoT applications. In this article, we examine the existing security solutions located in each layer and discuss the interrelated challenges. For example, a network-traffic-based IDS solution in MEC might capture IoT malware but fail to identify a growing file-less attack. We suggest that firmware emulation in IoT endpoint should be included to provide system-level behaviors to network-based detectors in MEC so that file-less attacks can be distinguished. The potential of a backdoor attack aiming to poison data or corrupt ML models cannot be ignored in AIoT applications, and a corresponding detector should be implemented in MEC. Due to the rise of low-cost Software-Defined Radio (SDR), malicious attacks using rogue base stations (BSs) have become more popular. It implies that security protection at MEC in the communications layer is necessary. This article, therefore, proposes a novel platform, M3Inspector, where inspectors located in mobiles and AIoT machines collect information from surrounding BSs and provide them to MEC. MEC determines the rogue BS and makes a notification to users subscribing to the local service. A realistic 5G experimental platform with rogue BSs is developed. The results demonstrate that attack detection and mitigation can be implemented in the MEC paradigm to significantly improve the security protection of AIoT from the perspectives of rogue BS attacks and file-less attacks.

Waveform-Defined Security: A Low-Cost Framework for Secure Communications

Communication security could be enhanced at physical layer but at the cost of complex algorithms and redundant hardware, which would render traditional physical layer security (PLS) techniques unsuitable for use with resource-constrained communication systems. This work investigates a waveform-defined security (WDS) framework, which differs fundamentally from traditional PLS techniques used in today's systems. The framework is not dependent on channel conditions such as signal power advantage and channel state information (CSI). Therefore, the framework is more reliable than channel dependent beamforming and artificial noise (AN) techniques. In addition, the framework is more than just increasing the cost of eavesdropping. By intentionally tuning waveform patterns to weaken signal feature diversity and enhance feature similarity, eavesdroppers will not be able to identify correctly signal formats. The wrong classification of signal formats would result in subsequent detection errors even when an eavesdropper uses brute-force detection techniques. To get a robust WDS framework, three impact factors, namely training data feature, oversampling factor and bandwidth compression factor (BCF) offset, are investigated. An optimal WDS waveform pattern is obtained at the end after a joint study of the three factors. To ensure a valid eavesdropping model, artificial intelligence (AI) dependent signal classifiers are designed followed by optimal performance achievable signal detectors. To show the compatibility in available communication systems, the WDS framework is successfully integrated in IEEE 802.11a with nearly no adding computational complexity. Finally, a low-cost software-defined radio (SDR) experiment is designed to verify the feasibility of the WDS framework in resource-constrained communications.

Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships

The Artificial Potential Field (APF) method is a classical path planning method for unmanned ships, relying on Global Positioning System (GPS) positioning information for path planning. Unfortunately, once the path planning algorithm uses inaccurate or even fake data, it will lead to ship collision, grounding, or deviation from the course, causing severe economic losses and causing significant security risks to other sailing ships. This paper aims to study the impacts of GPS spoofing on the path planning of unmanned ships. We propose a GPS attack and study GPS spoofing of path planning based on the APF method for an unmanned ship by a low-cost software-defined radio, which causes the unmanned ship to deviate from the course. Our simulation tests show that this method has significant impacts on the path planning results of the APF method.

GNSS Time Signal Spoofing Detector for Electrical Substations

This paper introduces a novel method of GNSS spoofing detection with applications in electrical substations. Time sensitive applications in electricity substations, including Phasor Measurement Units (PMU) and Merging Units (MU), rely on Global Navigation Satellite Signals (GNSS), often GPS, for time transfer. Recently, sophisticated ‘spoofing’ attacks have become feasible due to the availability of low cost Software Defined Radio (SDR) systems. The proposed method uses multiple GNSS receive antennas placed in close proximity at the electricity substation, such that it is not possible for an attacker to target a unique spoofing signal towards each antenna. In a system employing three or more receive antennas, during a spoofing attack two or more of the GNSS receive antennas will return an estimated position in impossible locations. This is sufficient to raise alarm that time sensitive applications should use an alternative time source or holdover clock. The contributions of this paper include a detailed description of the proposed method, an experimental assessment of GNSS receiver and substation clock position estimation variance to establish the minimum separation required between receive antennas, and a validation of the method by experimental demonstration. A further benefit of the authors’ method is that it may be put into practice immediately using commercial-off-the-shelf (COTS) substation clock equipment.

A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities.

The number of incidents between unmanned aerial vehicles (UAVs) and aircrafts at airports and airfields has been increasing over the last years. To address the problem, in this paper we describe a portable system capable of protecting areas against unauthorized UAVs, which is based on the use of low-cost SDR (software defined radio) platforms. The proposed anti-UAV system supports target localization and integrates effective jamming techniques with the generation of global positioning system (GPS) spoofing signals aimed at the drone. Real-life tests of the implemented prototype have shown that the proposed approach is capable of stopping the reliable reception of radionavigation signals and can also divert or even take control of unauthorized UAVs, whose flight path depends on the information obtained by the GPS system.

DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks

Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal regime. We note that digitally sampled RF frontends allow for the easy analysis of signals, and their delayed components. Low-cost Software-Defined Radio (SDR) modules enable Channel State Information (CSI) extraction across a wide spectrum, motivating the design of an enhanced AoA solution. We propose a Deep Learning approach for deriving AoA from a single snapshot of the SDR multichannel data. We compare and contrast deep-learning based angle classification and regression models, to estimate up to two AoAs accurately. We have implemented the inference engines on different platforms to extract AoAs in real-time, demonstrating the computational tractability of our approach. To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset. Our proposed method demonstrates excellent reliability in determining number of impinging signals and realized mean absolute AoA errors less than 2°.

Software defined radio for vector network analysis: Configuration, characterization and calibration

A portable vector network analyzer operating from 30 MHz to 850 MHz, using a commercial low-cost software-defined radio is described. The system can be used for transmission (S21) or, using a directional bridge, reflection measurements (S11). A complete software package is developed in order to define and operate the system as an automatic analyzer including the correction of systematic errors. The analyzer has heterodyne receivers with an specific intermediate frequency, digitizer sampling rate and block size chosen to correctly process the signals. Measurements to characterize the transmitters and receivers on the board are shown. System errors are determined by comparison with laboratory equipment measurements and manufacturer data.

Design of a Scalable 4G Portable Network Using Low Cost SDR And Raspberry Pi

Of late, Software Defined Radio (SDR) approach has become an effective means to design high data rate wireless systems for a range of applications. There are methods with which low cost SDR based 4th generation (4G) or long term evolution (LTE) systems can be designed. Using low cost Raspberry Pi systems, the SDR aided 4G systems can be designed for high data rate communication. The work is related to the design of a 4G wireless system using low cost SDR solutions and integrated to a programmable controller based on a Raspberry Pi. Experimental results show that the system is effective in a range of conditions

RF-Wri: An Efficient Framework for RF-Based Device-Free Air-Writing Recognition

Machines are becoming indispensable in our lives and the requirements of the human-machine interactions are increasing. Conventional devices, such as a keyboard or touch screen, may not be preferred in future’s entertainment, home, and industrial applications. Device-free (non-contact) solutions will be even more popular. These solutions often use visual and acoustic technologies which have some disadvantages. The use of radio frequency (RF) waves for human-machine interaction such as air-writing (Wri), is a new and challenging problem. We propose a device-free machine learning-based air-writing recognition framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RF-Wri</i> which can effectively distinguish 26 capital letters. Two-channel low-cost software-defined radios (SDR) and oppositely polarized antennas are used to provide polarization diversity which makes the accuracy of classification superior. Another critical novelty is the usage of Discrete Cosine Transform (DCT) coefficients as new features to represent RF waveform which provides writing speed and user invariant recognition. Discrete Wavelet Transform (DWT) filters and letter segmentation algorithm are used for signal de-noising and separating the air-writing activities, respectively. It is shown that Support Vector Machine (SVM) can successfully classify the measured RF waves of air-written letters. It is verified with various real measurements that the proposed framework, RF-Wri, achieves 95.15% accuracy in the classification of all 26 air-written letters and outperforms the fairly new WiFi-based air-writing recognition approaches.

A Local Oscillator Phase Compensation Technique for Ultra-Wideband Stepped-Frequency Continuous Wave Radar Based on a Low-Cost Software-Defined Radio.

The paper discusses a way to configure a stepped-frequency continuous wave (SFCW) radar using a low-cost software-defined radio (SDR). The most of high-end SDRs offer multiple transmitter (TX) and receiver (RX) channels, one of which can be used as the reference channel for compensating the initial phases of TX and RX local oscillator (LO) signals. It is same as how commercial vector network analyzers (VNAs) compensate for the LO initial phase. These SDRs can thus acquire phase-coherent in-phase and quadrature (I/Q) data without additional components and an SFCW radar can be easily configured. On the other hand, low-cost SDRs typically have only one transmitter and receiver. Therefore, the LO initial phase has to be compensated and the phases of the received I/Q signals have to be retrieved, preferably without employing an additional receiver and components to retain the system low-cost and simple. The present paper illustrates that the difference between the phases of TX and RX LO signals varies when the LO frequency is changed because of the timing of the commencement of the mixing. The paper then proposes a technique to compensate for the LO initial phases using the internal RF loopback of the transceiver chip and to reconstruct a pulse, which requires two streaming: one for the device under test (DUT) channel and the other for the internal RF loopback channel. The effect of the LO initial phase and the proposed method for the compensation are demonstrated by experiments at a single frequency and sweeping frequency, respectively. The results show that the proposed method can compensate for the LO initial phases and ultra-wideband (UWB) pulses can be reconstructed correctly from the data sampled by a low-cost SDR.

Dual-Comb Spectrometer Based on Gain-Switched Semiconductor Lasers and a Low-Cost Software-Defined Radio

Dual-comb spectroscopy has become a topic of growing interest in recent years due to the advantages it offers in terms of frequency resolution, accuracy, acquisition speed, and signal-to-noise ratio, with respect to other existing spectroscopic techniques. In addition, its characteristic of mapping the optical frequencies into radio-frequency ranges opens up the possibility of using non-demanding digitizers. In this paper, we show that a low-cost software defined radio platform can be used as a receiver to obtain such signals accurately using a dual-comb spectrometer based on gain-switched semiconductor lasers. We compare its performance with that of a real-time digital oscilloscope, finding similar results for both digitizers. We measure an absorption line of a H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> N cell and obtain that for an integration time of 1 s, the deviation obtained between the experimental data and the Voigt profile fitted to these data is around 0.97% using the low-cost digitizer while it is around 0.84% when using the high-end digitizer. The use of both technologies, semiconductor lasers and low-cost software defined radio platforms, can pave the way towards the development of cost-efficient dual-comb spectrometers.