Universal Software Radio Peripheral Research Articles

A Comparative Experimental Study of MIMO A&F and D&F Relay Nodes Using a Software-Defined Radio Platform

The relaying technologies in co-operative systems are considered a core element in actual and future wireless communications, assisting the network by enhancing its reliability and improving its capability through exploiting co-operativity. In this paper, a co-operative system testbed based on Software Defined Radio (SDR) through Universal Software Radio Peripherals (USRPs) and the MatlabTM software is presented. The main novelty in this development of the platform is the implementation of 4G signal features, such as Physical Downlink Shared Channel (PDSCH) and Downlink Shared Channel (DL-SCH) for transport channel coding, which is one of the main contribution of the paper. The developed Multi-Input and Multi-Output (MIMO) SDR co-operative platform is capable of developing prototypes for the Relay Nodes. More specifically, the Amplify-&-Forward (A&F)—with or without Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) Pre-Equalization—and Decode-&-Forward (D&F) protocols were implemented. Both Single-Input and Single-Output (SISO) and MIMO modes are supported by our testbed. The developed A&F and D&F MIMO co-operative systems in this paper utilize Orthogonal Space-Frequency Block Codes (OSFBCs) for the transmission of data symbols from the source to the destination. Our results show that relay nodes can substantially improve the Bit Error Rate (BER) and throughput in communications between the eNodeB (eNB) and User Equipment (UE). In particular, the maximum throughput achieved by conventional MIMO A&F is 9.3Mbps at SNR=16dB, which is 4Mbps higher than throughput of MIMO Non-Co-operative. It also shows the capacity improvement when considering the pre-equalization in the A&F schemes, compared to the conventional A&F Relay Node. For example, with MIMO A&F-MMSE pattern, a value of 11.8 Mbps is achieved for SNR=16dB, which is 84.8 % of the maximum system throughput (13.95 Mbps). On the other hand, the obtained results with D&F schemes far exceed those obtained with A&F strategies, achieving the maximum performance with the 2 × 2 MIMO D&F protocol from SNR=8dB. Furthermore, this work constitutes a first stage to the implementation of a 5G New-Radio Co-operative System platform.

An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users’ (PUs’) (or licensed users’) activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance; or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs’ activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry Pi3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner; and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs’ activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.

Experimentations on the Transmit Power of a Universal Software Radio Peripheral Using GNU Radio Framework and a Handheld RF Explorer

The Universal Software Radio Peripheral (USRP) and GNU Radio framework have a wide range of applications and are actively in use for teaching, research and for practical deployment of various trending wireless technologies. In this work, we explore the relationships between the power outputs of a USRP B200 device and its operating frequencies and USRP Hardware Driver (UHD) gains. These relationships are not precisely specified on the device datasheet, thereby hindering prompt design and experimentation decisions among Software Defined and Cognitive Radio (SDCR) researchers. A general purpose handheld spectrum analyser was engaged for recording the power outputs from the USRP B200 device within an experimental testbed that was setup for this study. The B200 device was driven by a GNU radio flowgraph designed for both wired and wireless modes and running on a general purpose host computer in the testbed. The results indicate that the output power decreases while the centre frequency increases, whereas the output power increases as the UHD gain increases from 65 dB until the device reaches the saturation point at 89.9 dB. These results provide a handy toolkit for SDCR researchers and practitioners.

Specific Emitter Identification Based on Software-Defined Radio and Decision Fusion

Specific emitter identification (SEI) uses the unintentional modulation information carried by the emitter waveform, i.e., radio frequency fingerprints, to realize the matching identification of the received signal and its corresponding emitter. We propose an emitter identification scheme based on deep learning (DL). The received signal is subjected to time-varying filtered empirical mode decomposition (tvf-EMD). The obtained intrinsic mode functions (IMFs) are subjected to Hilbert transformation to obtain a 3D-Hilbert spectrum, using amplitude frequency aggregation characteristics obtained by projection to express the nonstationary information of the signal and calculate its bispectral diagonal slice as the second feature. A squeeze-and-excitation block (SEB) is introduced to a convolutional neural network (CNN) to focus on the effective area in the feature map, and support vector machine (SVM) is used to fuse the decision information of the two types of features. We collect four types of steady-state signals on a software-defined radio (SDR) sensor platform based on GNU Radio and universal software radio peripherals (USRP), and study the algorithm performance. The results show that our scheme has strong generalization, high recognition accuracy, and broad application prospects compared with other SEI methods.

A Conformance Testing Methodology and System for Cognitive Radios

The fifth generation (5G) of mobile networks has started its operation in some countries and is aimed at meeting demands beyond the current system capabilities such as the huge amount of connected devices from IoT applications (e.g., smart cities), explosive growth of high‐speed mobile data traffic (e.g., ultrahigh definition video streaming), and ultrareliable and low latency communication (e.g., autonomous vehicle). To attend to these needs, the electromagnetic spectrum must be made available, but the static spectrum allocation policy has caused a spectrum shortage and impaired the employment/expansion of the wireless systems. To overcome this issue, the dynamic spectrum access (DSA) has been promoted in 5G/6G networks, which is enabled by the cognitive radio (CR) technology. Although diverse mechanisms have been developed to tackle the challenges that emerge in different CR layers/functionalities, a standardized testing methodology and system for CR is still immature. Existing standards or methodologies and systems for CR only focus on the definition of network technologies (e.g., IEEE 802.22 and IEEE 802.11af), performance evaluation of CR algorithms/mechanisms, or definition of the device cognition level via performance results or psychometric approaches, not covering systems/methodologies to verify if the device meets the CR capabilities and regulatory policies, neglecting the conformance testing. In this respect, this paper proposes a flexible methodology and system for CR conformance testing under two perspectives, functionalities and limits. We instantiate it by using the Universal Software Radio Peripheral (USRP) software‐defined radio platform and present a proof‐of‐concept with a conformance metric. The results show the feasibility of our proposal.

A USRP-Based Testbed for Wideband Ranging and Positioning Signal Acquisition

For validation and demonstration of high accuracy ranging and positioning algorithms and systems, a wideband radio signal generation and acquisition testbed, tightly synchronized in time and frequency, is needed. The development of such a testbed requires solutions to several challenges. Tight time and frequency synchronization, derived from a centrally distributed time-frequency reference signal, needs to be maintained in the hardware of the transmitter and receiver nodes, and wideband signal acquisition requires sustainable data throughput between the receiver and host PC as well as data storage at GB level. This article presents a testbed for wideband radio signal acquisition, for validation and demonstration of high accuracy ranging and positioning. It consists of multiple Ettus X310 universal software radio peripherals (USRPs) and supports high accuracy (<; 100 ps) time-deterministic, sustainable signal transmission and acquisition, with a bandwidth up to 320 MHz (in dual channel mode) and frequencies up to 6 GHz. Generation and processing of wideband arbitrary signal waveforms is done offline. To realize these features, radio frequency on chip (RFNoC) compatible HDL units were developed for integration in the X310 SDR platform. Wideband transmission and signal acquisition at a lower duty cycle is applied to reduce the data offloading throughput to the host's personal computer (PC). Benchmarking of the platform was performed to demonstrate sustainable long duration dual channel acquisition. Indoor range measurements with the synchronous operation of the testbed show a decimeter-level accuracy.

A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models.

In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi–Williams distribution (CWD) transformed. Aiming at the features of the multipulse radar signal TFI, we designed a distinguishing feature fusion extraction module (DFFE) and proposed a new HRF-Net deep learning model based on this module. The model has relatively few parameters and calculations. The experiments were carried out at the signal-to-noise ratio (SNR) of −14 ∼ 4 dB. In the case of −6 dB, the recognition result of HRF-Net reached 99.583% and the recognition result of the network still reached 97.500% under −14 dB. Compared with other methods, HRF-Nets have relatively better generalization and robustness.

Real-Time In-Lab Test of Eigenvalue-Based Spectrum Sensing Using USRP RIO SDR Boards

The diversity of prevailing and emerging cognitive radio (CR) applications makes CR an attractive domain to the researchers as well as the industry. The spectrum sensing (SS) is considered as the backbone of any autonomous CR. Thus, in this letter, we go one step further towards industrial integration of such CR feature by conducting in-lab test of a real communication scenario using a software defined radio (SDR) which is the universal software radio peripheral (USRP) platform. The eigenvalue-based SS (ESS) approaches have been, consistently, assessed in literature. Accordingly, in this work, we have proposed and tested a prototype integrating an ESS algorithm with the USRP (NI-2954R) reconfigurable I/O (RIO) devices. We have conducted real-time in-lab experiments. The proposed testbed has empirically demonstrated its capability to carry out a realistic communication scenario without loss of detection performances compared to software-based simulations.

A Radio Frequency Magnetoelectric Antenna Prototyping Platform for Neural Activity Monitoring Devices with Sensing and Energy Harvesting Capabilities

This article describes the development of a radio frequency (RF) platform for electromagnetically modulated signals that makes use of a software-defined radio (SDR) to receive information from a novel magnetoelectric (ME) antenna capable of sensing low-frequency magnetic fields with ultra-low magnitudes. The platform is employed as part of research and development to utilize miniaturized ME antennas and integrated circuits for neural recording with wireless implantable devices. To prototype the reception of electromagnetically modulated signals from a sensor, a versatile Universal Software Radio Peripheral (USRP) and the GNU Radio toolkit are utilized to enable real-time signal processing under varying operating conditions. Furthermore, it is demonstrated how a radio frequency signal transmitted from the SDR can be captured by the ME antenna for wireless energy harvesting.

Distributed beamforming based wireless power transfer: Analysis and realization

This paper presents a new Wireless Power Transfer (WPT) approach by aligning the phases of a group of spatially distributed Radio Frequency (RF) transmitters (TX) at the target receiver (RX) device. Our approach can transfer energy over tens of meters and even to targets blocked by obstacles. Compared to popular beamforming based WPTs, our approach leads to a drastically different energy density distribution: the energy density at the target receiver is much higher than the energy density at other locations. Due to this unique energy distribution pattern, our approach offers a safer WPT solution, which can be potentially scaled up to ship a higher level of energy over longer distances. Specifically, we model the energy density distribution and prove that our proposed system can create a high energy peak exactly at the target receiver. Then we conduct detailed simulation studies to investigate how the actual energy distribution is impacted by various important system parameters, including number/topology of transmitters, transmitter antenna directionality, the distance between receiver and transmitters, and environmental multipath. Finally, we build an actual prototype with 17 N210 and 4 B210 Universal Software Radio Peripheral (USRP) nodes, through which we validate the salient features and performance promises of the proposed system.

Patch-Antenna-Based Structural Strain Measurement Using Optimized Energy Detection Algorithm Applied on USRP

A new structural health monitoring (SHM) program using spectrum sensing and radio-frequency identification technology is presented to measure structural strain. The proposed program involving universal software radio peripheral (USRP) provides an economical alternative to these type of measurements as compared to high-end equipment while this method can achieve more flexible data processing. The overall system for strain detection consists of three main parts: 1) a patch antenna as the sensor for strain; 2) an USRP as a measuring instrument; 3) a computer for data processing. The patch antenna can be used to measure structural strain by interrogating resonant frequency shift due to changes in antenna length. In this article we use the optimized energy detection algorithm applied on USRP to detect the spectrum of the patch antenna, the final measurement results show that the patch antenna sensor has a strain sensitivity 1.7678 kHz/ $\boldsymbol{\mu } \boldsymbol{\varepsilon }$ while the error between the measured value and the true value of the stress is 2.443% after corrected by machine learning.

DeepWiPHY: Deep Learning-Based Receiver Design and Dataset for IEEE 802.11ax Systems

In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network’s architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.

Air‐interface virtualization using filter bank multicarrier and orthogonal frequency division multiplexing configurations

AbstractMobile networks have to cater for diverse services that have distinct requirements in terms of bandwidth, latency, and so on. Network virtualization is a key technology that efficiently utilizes the network resources to meet these service requirements. In this work, we investigate radio virtualization for creating fine‐grained network slices using orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) combinations; the two are individually referred to as virtual radios (VRs). Using universal software radio peripherals (USRPs), we experimentally analyze multiple configurations of VRs including OFDM‐OFDM, OFDM‐FBMC, and FBMC‐FBMC. An extensive performance comparison is done on the basis of error rate, spectral efficiency, interference power, and computational complexity of the VR configurations that are confined within an operational bandwidth. An increase in transmit power of the VRs is theoretically expected to decrease the error rate, but there is also an increase in adjacent channel interference. Therefore, after a certain decrease in error rate with increase in transmit power, an inflection point is reached after which the error rate starts to increase. Of the three combinations, FBMC‐FBMC gives the lowest error rate (at the highest transmit power), that is, 10% and 18% lower than OFDM‐FBMC and OFDM‐OFDM, respectively. However, FBMC‐FBMC also has the highest complexity. We conclude that this air‐interface virtualization framework allows the network to use the waveform configuration that is best suited to a particular set of services, while considering the pros and cons of the individual waveforms.

Multi-longitudinal mode fiber laser digital vibration-sensing system based on a multi-carrier modulation/demodulation technique.

A multi-longitudinal mode fiber laser sensor (MLMFLS) system based on a digital modulation/demodulation technique is proposed. To the best of the authors' knowledge, this is the first digital vibration-demodulation system ever reported by a MLMFLS system. Multiple beat frequency signals (BFS) generated by the MLMFLS work as signal carriers of applied vibration signals. The vibration signals modulated on several BFS at different frequencies are simultaneously demodulated based on a multi-channel digital down-conversion technique by utilizing the digital universal software radio peripheral (USRP). The demodulated vibration signals from different signal channels are superposed by the USRP. Signal-to-noise ratio (SNR) of output vibration signal is highly improved, which increases sensing stability and accuracy of the system. This is really important for sensing of vibration signals that have an extremely low frequency or weak energy. Measurement results demonstrate that the sensing system has an excellent low frequency vibration-sensing capability, simple structure, high SNR, stability and accuracy.

Data-Limited Modulation Classification With a CVAE-Enhanced Learning Model

The modulation classification problem can be effectively handled by modern machine learning techniques, however, the available data for training is usually limited due to costly measurements in practice. Concerning the problem of insufficient training data, we propose a conditional variational auto-encoder (CVAE)-enhanced learning model for modulation classification. Different from the conventional data augmentation approaches where the data is independently generated, we introduce a feedback unit from the classifier to the CVAE generative network, to ensure that the generated data helps improving the classification accuracy. A two-stage generating-training algorithm is proposed. Via both simulation and practical implementation on a universal software radio peripheral (USRP) platform, it is shown that the proposed method effectively improves the classification accuracy in realistic propagation environment.

Performance Analysis Under Double Sided Clipping and Real Time Implementation of DCO-GFDM in VLC Systems

In the context of visible light communications (VLC), DC biased optical generalized frequency division multiplexing (DCO-GFDM) is a recently emerged waveform relying on block based transmission and employs pulse shaping using a circularly rotating prototype filter. In this work, we analyze the bit error rate (BER) performance of DCO-GFDM under double sided clipping induced by front end light emitting diode (LED) transmitters. The effect of clipping on BER performance is studied under different biasing conditions for different prototype filters. Additionally, we experimentally verify the real time performance of DCO-GFDM using different pulses. Simulations are performed in MATLAB software and experiments are conducted in a Lab-view environment using hardware. Two independent universal software radio peripherals (USRP)s are utilized as transmitter and receiver boards. It is observed that the simulation results match well with the corresponding theoretical results. Meanwhile, the experimentally achieved results for error vector magnitude (EVM), the received constellations, and the received spectrum along with BER in different cases are presented for the validation of DCO-GFDM waveform and are compared with DCO orthogonal frequency division multiplexing (DCO-OFDM).

Primary user emulation attack mitigation using neural network

The spectrum sensing scheme suffers from a physical layer attack of Primary User Emulation Attack (PUEA). The resolution is to mitigate the cognitive radio user from the PUEA under the physical layer. Detecting the PUEA attack in real-time is a challenging one. The traditional Location-based PUEA detection requires the primary user's location knowledge, which may not be possible practically. This research focuses on developing a reliable spectrum sensing mechanism in the presence of PUEA attack and rapid change in the wireless channel. This reliable spectrum sensing framework is developed using the neural network-based PUEA detector excluding the location information. The Software-Defined Radio (SDR) called Universal Software Radio Peripheral (USRP) 2943R is used to implement the proposed mechanism for analyzing performance in real-time. The real-time experimental results show that PUEA detection can be achieved with 97% accuracy.

Measuring scattering function of HF NVIS channel by sounding with the use of a noise-like BPSK signal

Measuring parameters of the scattering function of near vertical incidence skywave (NVIS) radio links employed for data transmission is topical and fosters better understanding signal propagation as well as developing state-of-the-art cognitive radio systems. In this regard, the paper presents the findings of the research into the application of a noise-like phase-shift keying (PSK) signal for vertical sounding of NVIS radio links. There are presented the developed algorithms for processing the PSK signal to measure the scattering function and its parameters (signal-to-noise ratio, delay spread and Doppler spread) for multiple communication channels in the high frequency (HF) band. To carry out experiments on measuring scattering function of radio channels with bandwidth of 24 kHz and different mid-band frequencies, we employed developed ionosonde based on the universal software radio peripherals (USRP) platform.

Servicing delay sensitive pervasive communication through adaptable width channelization for supporting mobile edge computing

Over the last fifteen years, wireless local area networks (WLANs) have been populated on large variety of pervasive devices hosting heterogeneous applications. Pervasive edge computing has accelerated more distributed network applications for these devices, eliminating the round-trip to help in achieving zero latency dream. However, These applications require significantly variable data rates for effective functioning, especially in pervasive computing. The static bandwidth of frequency channelization in current WLANs strictly restricts the maximum achievable data rate by a network station. This static behavior spawns two major drawbacks: under-utilization of scarce spectrum resources and less support to delay sensitive applications such as voice and video.To this point, if the computing is moved to the edge of WLANs to reduce the frequency of communications, the pervasive devices can be provided with better services during the communication and networking. Thus, we aim to distribute spectrum resources among pervasive devices based upon delay sensitivity of applications while simultaneously maintain the fair channel access semantics of medium access control (MAC) layer of WLANs. Henceforth, ultra-low latency, efficiency and reliability of spectrum resources can be assured.In this paper, two novel algorithms have been proposed for adaptive channelization to offer rational distribution of spectrum resources among pervasive edge nodes based on their bandwidth requirement and assorted ambient conditions. The proposed algorithms have been implemented on a real test bed of commercially available universal software radio peripheral (USRP) devices. Thorough investigations have been carried out to enumerate the effect of dynamic bandwidth channelization on parameters such as medium utilization, achievable throughput, service delay, channel access fairness and bit error rate. The achieved empirical results demonstrate that we can optimally enhance the network wide throughput by almost 30% by using channels of adaptable bandwidths.

A Novel Detection Method Based on Maximum-Likelihood Estimation Decoding of a 6-Bit Chipless Radio Frequency Identification Coded Tag

The detection of chipless radio frequency identification (RFID) tags is mostly realized by vector network analyzers. In actual detection, this method is expensive and not flexible enough. A flexible new detection method implemented on the Universal Software Radio Peripheral (USRP) is proposed in this article. The measurement object is a 6-bit chipless RFID coded tag sensor based on a spiral resonator trap circuit. The power detection function of the frequency band is designed and implemented on USRP's open-source platform GNU Radio. On this basis, for chipless RFID tags with spectrum coding, a decoding identification method based on maximum-likelihood estimation is designed. The frequency-domain curve of the scattered signal of the sample label is detected by the power detection method, and the decoding and identification test of the sample label is performed by the maximum-likelihood estimation decoding identification method. It is shown that the maximum-likelihood estimation decoding algorithm based on the USRP platform proposed can be effectively implemented in passive wireless networks through experimental results, which provides a new idea for the flexible and economical RFID tag detection method.