Software Defined Radio Platform Research Articles

DGRU based human activity recognition using channel state information

In this work, we have proposed a Deep Gated Recurrent Unit (DGRU) model for non-obtrusive human activity recognition using Channel State Information (CSI). Empirical model decomposition is used for de-noising, whereas discrete wavelet transforms and linear discriminant analysis are used for feature extraction and dimensionality reduction, respectively. For extensive experimental evaluation and comparative analysis, a Software Defined Radio (SDR) platform is used by implementing IEEE 802.11a on National Instruments’ Universal Software Radio Peripheral (USRP). The physical layer CSI is collected in an indoor environment to evaluate the performance for seven activities. 30 volunteers including both genders and of different age groups were involved in the data collection process. As demonstrated through experiments, the proposed scheme achieves promising results with an accuracy of 95–99% for all activities, outperforming the traditional benchmark approaches in the literature that use random forest and more advanced deep learning techniques, such as Long-Short Term Memory (LSTM).

Unauthorized Broadcasting Identification: A Deep LSTM Recurrent Learning Approach

Radio broadcasting plays an important role in our daily life. Meanwhile, with the development of wireless communications, the application of software-defined radio platforms gives rise to cheap and easy design of illegal broadcasting stations. These unauthorized broadcasting stations sometimes illegally occupy licensed frequency band, especially associated with amateur radios and unlicensed personal communication devices and services. These unauthorized broadcasting stations may severely interfere with the authorized broadcasting and further disrupt the management of spectrum resource in civil applications, such as emergency services and air traffic control. However, it still remains a challenging task to automatically and effectively identify the unauthorized broadcasting in complicated electromagnetic environments. Aiming at developing an intelligent and efficient unauthorized broadcasting identification system, in this article, a novel identification approach is proposed based on long short-term memory (LSTM) recurrent neural network (RNN), and LabVIEW software. In our approach, first, a series of LabVIEW applications are developed to drive USRP 2930s for the acquisition of broadcasting signals. Then, the LSTM identification network is proposed to recognize unauthorized broadcasting. Through the special gate structure inside, the proposed LSTM framework can effectively extract the distinguishing features, such as channel state information and RF device fingerprinting. Simulation results show that the proposed LSTM-based approach perform better than other contrastive methods, especially in identification accuracy. Implementation results also demonstrate that the proposed method has an outstanding unauthorized broadcasting identification performance with a high accuracy, i.e., identify the unauthorized broadcasting signals with 99.83% accuracy at the licensed frequency of 107.8 MHz, in realistic electromagnetic environments.

Wireless link pairing toward secured 6G networks.

Hybrid wireless networks are foreseen to play a major role in the visioning and planning of the sixth generation (6G) network. Most of the 6G applications are human-centric, and thus high security and privacy are key features. Recently, physical layer (PHY) security has become an emerging area of research. This work introduces a novel, to the best of our knowledge, PHY security approach called wireless link pairing (WiLP). In WiLP, signals received from both air interfaces in a hybrid radio frequency and optical network are required for successful signal reconstruction and processing at the receiver. The transmitted packets based on the IEEE 802.11 standards are redesigned, and improvements in performance are validated via simulations and experimental measurements using software-defined radio platforms. The obtained results demonstrate improvements in bit-error rate (BER) and the secrecy capacity for multiple modulation and coding schemes.

W-SHARP: Implementation of a High-Performance Wireless Time-Sensitive Network for Low Latency and Ultra-low Cycle Time Industrial Applications

Real-time Industrial applications in the scope of Industry 4.0. present significant challenges from the communication perspective: low latency, ultra-reliability, and determinism. Given that wireless networks provide a significant cost reduction, lower deployment time, and free movement of the wireless nodes, wireless solutions have attracted the industry attention. However, industrial networks are mostly built by wired means because state-of-the-art wireless networks cannot cope with the industrial applications requirements. In this article, we present the hardware implementation of wireless SHARP (w-SHARP), a promising wireless technology for real-time industrial applications. w-SHARP follows the principles of time-sensitive networking and provides time synchronization, time-aware scheduling with bounded latency and high reliability. The implementation has been carried out on a field-programmable gate array-based software-defined radio platform. We demonstrate, through a hardware testbed, that w-SHARP is able to provide ultra-low control cycles, low latency, and high reliability. This implementation may open new perspectives in the implementation of high-performance industrial wireless networks, as both PHY and MAC layers are now subject to be optimized for specific industrial applications.

Prototyping and Experimental Study of Non-Orthogonal Multiple Access in Wi-Fi Networks

NOMA is a promising way to increase spectral efficiency recently discussed in the IEEE 802.11 Working Group that develops WiFi standards. NOMA can be very beneficial in a WiFi network where the qualities of the channel between the access point and associated stations are significantly different. In such scenarios, NOMA allows the access point to transmit several data streams to various stations in parallel using one antenna and the same time-frequency resources. This article presents and evaluates the first-ever prototype of a WiFi device supporting NOMA, implemented in a software-defined radio platform. The prototype is backward compatible with legacy WiFi versions. Specifically, one of the multiplexed streams may be received by a legacy station not supporting NOMA. This feature is favorable for heterogeneous deployments with various generations of WiFi devices. This article provides a detailed description of the prototype as well as experimental results showing from 37 percent to 100 percent gain achieved by including NOMA in the future 802.11 standards.

Comparison of Baseband Processors in Terms of Realization SDR-Transceivers

Software-defined Radio is a programmable transceiver with the capability of operating various wireless communication protocols without the need to change or update the hardware. Consequently, Software-defined Radio has earned a lot of attention and is of great significance to both academia, military and aerospace industry. Components of Software-defined Radio (e.g. mixers, filters, amplifiers, modulators/demodulators, detectors, etc.) implemented by means of software on a personal computer or embedded system. Operation of signal processing are handed over to the baseband processor, rather than being done in special electronic circuits. Baseband processors are implemented through employing various types of hardware platforms, such as General Purpose Processors, Graphics Processing Units, Digital Signal Processors, and Field Programmable Gate Arrays. Each of these platforms is associated with their own set of advantages and disadvantages. In this paper was proposed a comparison of the state-of-the-art hardware platforms in the context of implementation Software-defined Radio transceivers. For comparison was determined as follow criteria: computational power of hardware platform, power consumption, complexity of developing, and cost of tools and equipment. First approaches to realizing baseband processors is using a General Purpose Processor and accelerating by Graphics Processing Units. But General Purpose Processor and Graphics Processing Units execute software instructions in the sequential order. For this reason, General Purpose Processors are not convenient for high-throughput computing with real-time requirements. Also this hardware platforms have increased power consumption. This aspect does not allow use General Purpose Processor and Graphics Processing Units in small and portable Software-defined Radio transceivers. In other hand, General Purpose Processors are preferable hardware platform by researchers and beginners due to their flexibility and programmability. Therefore, General Purpose Processors and Graphics Processing Units is highly recommended for prototyping Software-defined Radio platforms. Digital Signal Processor was reviewed as alternative approach for implementing baseband processors. Digital Signal Processors is a particular type of General Purpose Processors that is optimized to process digital signals. Digital Signal Processors have similar disadvantage with insufficient computational power, but some manufacturer sell energy optimized Digital Signal Processors. Consequently, Digital Signal Processor is commonly used in small and portable Software-defined Radio transceivers. Field Programmable Gate Arrays and System-on-Chips with Field Programmable Gate Array are strongly recommended for high-performance Software-defined Radio platforms. This hardware platforms combine the flexibility of processors and efficiency of small Digital Signal Processor. Field Programmable Gate Arrays can achieve a high level of parallelism in executing digital signal processing. However, the designers must have a high degree in digital electronics and good acknowledgement of hardware description languages. After the research, was proposed own flexible architecture Software-defined Radio transceiver and methods for development.

Spectrum shaping using NC‐OFDM for cognitive radio applications

Cognitive radio (CR) is an innovative technology that supports dynamic spectrum access to utilise the spectrum more efficiently in an opportunistic manner without interfering with the licensed users. Multi-carrier modulation techniques such as orthogonal frequency division multiplexing (OFDM) is identified as one of the suitable candidates for the design of CR systems. To provide high data rates while avoiding interference with licensed users, a variant of OFDM called non-contiguous OFDM (NC-OFDM) is used in CR. NC-OFDM is useful for dynamic shaping of spectrum based on the channel impairments, interference and jamming threats. In this work, the customised NC-OFDM transceiver link is simulated and compared with the customised OFDM link in the MATLAB environment. It is implemented in Zynq-7000 System-on-Chip (SoC) 7Z020 with radio-frequency front-end board, AD-FMCOMMS3-EBZ Software Defined Radio platform. Implementation of this link is carried out on Spartan-3A FPGA and compared the hardware resource utilisation with Zynq platform. It is observed that the Zynq platform utilises less hardware resources for the implementation. The transmitter spectrum, spectrum after de-activating unwanted sub-carriers, the spectrum shaping using raised cosine filtering, spectrum sensing using the correlation of known pattern and FFT-based spectrum sensing are also simulated in the MATLAB.

Performance evaluation and implementation complexity analysis framework for ZF based linear massive MIMO detection

This paper discusses a framework for algorithm-architecture synergy for (1) performance evaluation and (2) FPGA implementation complexity analysis of linear massive MIMO detection techniques. Three low complexity implementation techniques of the zero-forcing (ZF) based linear detection are evaluated, namely, Neumann series expansion (NSE), Gauss–Seidel (GS) and a proposed recursive Gram matrix inversion update (RGMIU) techniques. The performance analysis framework is based on software-defined radio platform. By extrapolating the real data measured average error vector magnitude versus a number of served single-antenna user terminals, GS and RGMIU are showing no performance degradation with respect to ZF with direct matrix inversion. It is shown that under high load regime NSE and GS require more processing iterations at the expense of increased processing latency. We, therefore, consider a unified approach for field-programmable gate array based implementation complexity analysis and discuss the required baseband processing resources for real-time transmission. Due to the wide differences of NSE, GS and RGMIU in terms of performance, processing complexity and latency, practical deployment and real-time implementation insights are derived.

A Novel Algorithm to Design Rate-Adaptive Irregular LDPC Codes

This paper proposes an algorithm to design rate-adaptive irregular LDPC codes with improved Bit error rate (BER) performance. It focuses on achieving better BER results for higher order coding rates which are in high demand for 5G mobile communications. The algorithm generates a parity check matrix based on the message block size. The encoding complexity of the irregular LDPC code is comparatively reduced due to the proposed parity check matrix. Simulation results compare the BER performances of irregular LDPC codes for various code rates and modulation schemes with the Parity check matrix (PCM) generated by the proposed algorithm. The BER performance of improved irregular LDPC code is analyzed in a real-time scenario using Software Defined Radio platform as well.

Capture of UAVs Through GPS Spoofing Using Low-Cost SDR Platforms

The increased use of unmanned aerial vehicles (UAVs), better known as drones, by civilians has grown exponentially and their autonomous flight control systems have improved significantly, which has resulted in a greater number of accidents and dangerous situations. To help resolve this problem, in this paper, we address the use of low-cost Software Defined Radio (SDR) platforms for simulating a global navigation satellite system (GNSS), more specifically the global positioning system (GPS), in order to transmit false signals and induce a location error on the targeted GPS receiver. Using this approach, a defensive system can be implemented which can divert, or even take control of unauthorized UAVs whose flight path depends on the information obtained by the GPS system.

A novel carrier frequency offset algorithm based on a double Barker code in VDE-TER

The Maritime Very High Frequency (VHF) Data Exchange System (VDES) is a basic method of data exchange in the e-Navigation strategy proposed by the International Maritime Organization (IMO). Regarding the synchronization of the VDE-terrestrial (VDE-TER) communication carrier frequency, using the special structure of the training sequence in a VDE message frame, we propose a novel carrier frequency offset (CFO) estimation algorithm based on the phase difference between the Barker code and its inverse code. We derive the principle model for the Barker code and anti-Barker code algorithm and analyze the frequency offset estimation performance of the proposed algorithm with those of the Fitz, L&R and M&M algorithms. We also present a real-world experiment conducted using a software-defined radio (SDR) platform. The results show that in the VDE-TER channel, the double Barker code (DBC) carrier frequency offset estimation algorithm has good estimation performance and estimations range within the visible range.

Software-defined optoacoustic tomography.

In this work we present what we believe is the first application of software-defined optoelectronics (SDO) for bidimensional optoacoustic tomography (OAT). The SDO concept refers to optoelectronic systems where the functionality associated with the conditioning and processing of optical and electrical signals are digitally implemented and controlled by software. This paradigm takes advantage of the flexibility of software-defined hardware platforms to develop adaptive instrumentation systems. We implement an OAT system based on a heterodyne interferometer in a Mach-Zehnder configuration and a commercial software-defined radio platform (SDR). Here the SDR serves as a function generator and oscilloscope, while at the same time providing perfect carrier synchronization between its transmitter and receiver in a coherent baseband modulator scheme. This carrier synchronization enables us to have much better phase recovery. We study the performance of the OAT SDO system using different bidimensional phantoms and carry out an analysis of the reconstructed images.

Using Open-Source Software Defined Radio Platforms for Empirical Characterization of Man-Made Impulsive Noise

The paper discusses the use of affordable software-defined radio (SDR) platforms for measuring and characterizing man-made noise with impulsive components using Middleton noise models. For illustration, numerical results from a case study are presented, in which man-made noise is measured at three different locations corresponding to three distinct types of radio-frequency (RF) environments, and for which the parameters of the underlying Middleton Class A impulsive noise model are derived. The study demonstrates one of the many applications of SDR platforms, whose versatility enables dedicated noise measurements systems that can be configured through software.

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

In the past decades, cognitive computing and communication densely used in lots of networking areas. Current improvement in deep learning (DL) and big data analysis create great potential to analyze cognitive intelligence (CI) for many applications such as human activity monitoring and recognition through wireless communication. Cognitive intelligence and wireless communication are using to establish smart healthcare systems. Healthcare monitoring systems turn into interesting research subjects where monitoring post-operative surgical patients are the current focal point to the researcher. In this paper, we argue that deep learning along with the wireless communication technique introduces cognitive intelligence for the healthcare monitoring system. We present a deep learning based convolutional neural network (CNN) model to classify image data and a convenient and multi-functional software-defined radio (SDR) platform to detect movement of the ankle of patients who underwent ankle fracture surgery. Capturing wireless channel state information (WCSI) in the presence of the human body and classifying using CNN to observe distinct movements is the key idea of this study. A universal software radio peripheral (USRP) platform used to capture WCSI data and used for classification. AlexNet and ZFNet both are the famous architecture of CNN and used in a parallel way to classify captured WCSI-based images that converted from numeric data. The classification established on the ankles movements after surgery and classification results show that CNN provides satisfying results where test accuracy is 98.98%.

Real Time Receiver Baseband Processing Platform for Sub 6 GHz PHY Layer Experiments

Wireless communication is rapidly evolving to fulfill diverse requirements in a number of application areas. Researchers are experimenting with novel ideas to improve different aspects of communication systems and performance metrics. While computer simulation is the first step to validating these approaches, testing platforms are needed to transform them to implementations for further validation and experimentation. Software Defined Radios (SDR) and System on Chip (SoC) offer a great deal of flexibility and versatility allowing researchers to experiment with wireless algorithms. However there are challenges; noise, channel effects, and synchronization errors have to be dealt with, and measures to mitigate their effects on received symbols should be implemented. Existing research using state of the art SDR platforms has not leveraged the powerful processing capabilities of Field Programmable Gate Arrays (FPGAs) in SoCs for receiver backend operations. In this work, we use high level tools to describe hardware, and automatically synthesize and implement receiver baseband wireless signal processing algorithms for FPGA targets. We demonstrate the ability to use such platforms for real world applications using over the air waveforms. We use Xilinx ZC706, Zedboard, ADI's FMComms3, and NXP's BGA7210 variable gain amplifier (VGA) for the experiments presented in this paper. Use cases considered include testing the performance of higher order modulation schemes, adjacent channel interference, power amplifier (PA) gain compression and effects on the bit error rate (BER) performance.

An SDR-Based Satellite Gateway for Internet of Remote Things (IoRT) Applications

Internet-of-Things (IoT) represents a breakthrough for the current ICT market. In many IoT applications, sensors and actuators are distributed over very wide areas, sometimes not reached by terrestrial networks. In such scenarios, the satellite plays a significant role. In this paper, a Software-Defined Radio (SDR) - based satellite gateway for Internet-of-Remote-Things (IoRT) is proposed. The use of SDR allows to decrease equipment cost and provides higher flexibility. The proposed architecture has been implemented by using a standalone SDR platform and Commercial Off-The-Shelf (COTS) modules for covering the main terrestrial IoT standards. Extensive proof-of-concept results are presented and discussed. Uplink and downlink tests showed the correct functionality implementation and transmitted signal generation, while the integration tests allowed to assess the reliability of the end-to-end information processing. Reconfigurability tests confirmed the capability of the gateway of dynamically updating in real-time its protocol settings. The overall test results showed the validity of the proposed SDR-based gateway for IoRT applications.

A Real-Time Modulation Recognition System Based on Software-Defined Radio and Multi-Skip Residual Neural Network

Communication signal modulation recognition has important research value in the fields of cognitive electronic warfare, communication countermeasures and non-collaborative communication. However, traditional signal recognition methods usually suffer some drawbacks, such as low accuracy, poor scalability, dependence on expert characteristics, and poor applicability to real-world environments. Therefore, in this article, a real-time modulation recognition system based on deep learning and software-defined radio (SDR) technology is designed. In the first step, an improved residual neural network is designed. A multi-skip residual stack (MRS) is designed to preserve more initial residuals information on the multi-scale feature map, which can simultaneously learn the deep and shallow characteristics of the signal. Then, a multi-skip residual network is designed with the MRS as the basic unit, and the network is trained using an adaptive moment estimation optimization algorithm. Finally, the network is tested on public datasets. In the second step, the network is embedded in a SDR platform composed of a GNU Radio and an universal software radio peripheral to realize real-time recognition of the input signal. Experiments show that this system has strong real-time capabilities, high recognition accuracy and considerable robustness.

Design and verification of a testing platform for implementation of software defined radio communications systems

The software-defined radio platforms that currently exist are mainly focused on the development of software communication blocks, leaving aside the hardware development, so the need arises to have a testing platform for hardware blocks which also allows us to interact with a radio frequency stage to perform blocks tests of complete communication systems in the air such as WiFi, Bluethoot, Zig Bee among others. This article describes the design of a platform based on a Field-Programmable Gate Array (FPGA) technology and microprocessor, which allows having multiple blocks and algorithms in hardware and software and interacting between them. In the same way there is a significant number of general purpose interfaces for testing the signals, as well as having specific interfaces which allow us to interconnect with different radio frequency platforms and communication cards with the computer. This allows you to work with different radio technologies according to your needs. A use case is presented in which a Bluethoot transmitter is tested with the use of the platform.

Measurements of Signal Delays in Software Defined Radio with Use of GNSS Modules

Abstract In the work a method of latency measurement in software defined radio (SDR) is proposed and validated. The test setup uses customer grade GNSS modules as reference time sources and enables relative delay calculation between signals received directly and those bypassed through SDR platform. The method is hardware agnostic in a sense, that it does not involve any custom software or hardware modifications. Tests that compare reported carrier-to-noise ratio and positioning errors were performed to prove functionality of such system. Additionally, authors measured several gnuradio blocks with respect to their impact on total latency introduced into signal path. All tests were performed on a bladeRF low-cost RF front-end. Minimum observed latency for the signal was below 10 ms.

PoM: Power-Efficient Multi-View Video Streaming Over Multi-Antenna Wireless Systems

Multi-view video streaming is becoming more popular, with increased potential for immersive applications. However, it poses a challenge in terms of energy consumption on the transmission devices with limited battery power due to the vast traffic volume. Previous literature has considered efficient video transmission but not exploited the full potential of joint design of 3D video coding and MIMO physical layer. In this paper, we present the first study on power consumption minimization for multi-view video streaming with a quality guarantee in 802.11 MIMO-OFDM systems. We propose an efficient algorithm to perform antenna assignment and transmission power allocation, by exploiting both the source coding characteristics of MVC and channel diversity of multiple antennas. A proof-of-concept system, namely PoM , is designed on the software-defined radio platform and is evaluated in realistic indoor environments. To the best of our knowledge, this is the first practical system for energy-efficient multi-view video streaming. Experimental results show that PoM can significantly save energy in the transmission by 88% ~ 26% on average for the PSNR requirement ranging from 35dB to 45dB, and 85% ~ 12% for SSIM from 0.80 to 0.95.