Universal Software Radio Peripheral Platform Research Articles

Performance evaluation of new blind OFDM signal recognition based on properties of the second-order statistics using universal software radio peripheral platform

<div>In the context of cognitive radio (CR) or various military and civilian applications, modulation recognition (MR) is one of the most popular technical processes in the field of communication system recognition, by which the modulation type of the unknown received signal can be identified automatically by estimating one or more parameters of the modulated signal. This paper presents the performance evaluation of the new proposed blind system recognition method using only a particular property of the second-order statistics of the orthogonal frequency division multiplexing (OFDM) modulated signal. The effectiveness of the proposed method is illustrated using the implementation on universal software radio peripheral (USRP) platform. A comparison with computer simulations using MATLAB software is also performed, emphasizing the good performances of the method while the results obtained are close. We show the efficiency and behavior of the proposed method in the context of wireless communication systems based on OFDM modulation (3GPP/LTE, WiMAX, DVBT-2K, IEEE 802.22-1K,IEEE 802.22-2K, IEEE 802.22-4K). The proposed method can detect OF DM signals among other digital signals in a systematic and intelligent way even with low SNR values (when approaching to SNR=-2dB, the decision criteria tends towards 0).</div>

Clone Detection Based on BPNN and Physical Layer Reputation for Industrial Wireless CPS

Industrial wireless cyber–physical systems are vulnerable to malicious node attacks, for example, clone node attack. The existing clone detection schemes are either based on upper layer observations or physical layer channel state information. The schemes based on upper layer observations are vulnerable to defamation while the schemes based on channel state information perform better against defamation, but are badly affected by channel conditions. This article applies physical layer reputation and back propagation neural network to clone detection, aiming at improving the detection accuracy. The proposed scheme accumulates the physical layer reputations by channel state information and input them to the neural network. The cloud server performs attack detection by group detection first. If a certain group is classified as attacked, the corresponding edge processor will perform attack tracing to identify the specific clone nodes. During the attack tracing stage, multiple reputations of each node is adopted for a comprehensive inspection. Extensive experiments are conducted on the Universal Software Radio Peripheral platform. The numerical results show that the proposed scheme significantly improves the detection accuracy.

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.

Analysis study and SDR implementation of GoF‐based spectrum sensing for cognitive radio

Cognitive radio (CR) technology is getting extensive attention to eliminate the problem of spectrum scarcity. This technology is based on Software Defined Radio (SDR) and some integrated intelligent functions such as spectrum sensing. This function is one of the crucial elements and its hardware implementation is important for the practical realization of the CR. This paper presents experimental results of the hardware implementation of the Goodness of Fit (GoF) based sensing for CR. Universal Software Radio Peripheral platform (USRP) is used for the experiments conducted. The choice of GoF based sensing for detecting spectrum holes is motivated by the advantage of requiring fewer samples to achieve the same sensing performance compared to Energy Detector (ED). Besides, the analytical expressions for the theoretical detection performance are derived. Computer simulations are carried out to evaluate the impact of fading channels on GoF based sensing. Numerical results confirm that the proposed GoF based sensing method outperform ED under fading channels. Furthermore, theoretical results of GoF based spectrum sensing are validated experimentally taking into account some parameters such as sample rate and sensing time. Finally, several scenarios of wideband GoF based sensing are considered to demonstrate the strengths of the considered method.

Integrated Communication and Localization System With OFDM-Chirp Waveform

This paper proposes an integrated communication and localization system scheme using orthogonal frequency division multiplexing (OFDM) chirp waveform. A new and efficient generation method is proposed to produce the OFDM-chirp waveform. The OFDM-chirp waveform is quantitatively compared with conventional OFDM waveform with respect to complementary cumulative distribution function of the peak-to-average power ratio. The OFDM-chirp waveform is analyzed by the ambiguity function together with its matched filtering performance. Furthermore, we implemented the OFDM-chirp waveform for integrated communication and localization with the universal software radio peripheral platform. Experimental results show that it outperforms conventional OFDM waveform for integrated communication and localization applications.

Towards High-Performance Wireless Control: $10^{-7}$ Packet Error Rate in Real Factory Environments

To meet the extremely low latency constraints of industrial wireless control in critical applications, the wireless high-performance scheme (WirelessHP) has been introduced as a promising solution. The proposed design showed great improvements in terms of latency, but its performance in terms of reliability have not been fully tested yet. While traditional wireless systems achieve high reliability through packet retransmissions, this would impair the latency, and an approach based on channel coding is preferable in industrial applications. In this paper, a set of packet error rate (PER) tests is performed by applying concatenated Reed Solomon and convolutional codes to the WirelessHP physical layer, using a demonstrator based on a universal software radio peripheral platform. The effectiveness of channel coding to achieve $10^{-7}$ level PER without retransmissions is shown in typical laboratory and factory environments.

Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme

Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure, carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure, are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio at the classification stage. We construct a testbed using universal software radio peripheral platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and nonline-of-sight scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices.

Adaptive wideband equalization for frequency dispersion correction in HF band considering variations in interference characteristics and ionosphere parameters

Paper presents the outcomes of the studies into the adaptive wideband equalization for frequency dispersion correction in HF band considering variations in interference characteristics and ionosphere parameters. The subject matter of the research were megahertz-bandwidth channels with single-hop F layer propagation mode. There are presented data on variations in the channel amplitude frequency response that are caused by the interference of magneto-ionic components (intramodal multipath). Test facility for carrying out full-scale experiments was developed with the use of Universal Software Radio Peripheral platform supported by the groundbreaking software-defined radio technology. Verification of the developed methods and algorithms was performed in the experiments on oblique sounding over the Cyprus-to-Yoshkar-Ola propagation path by the linearly frequency modulated continuous wave signal.

Angle-of-Arrival Estimation in Formation Flying Satellites: Concept and Demonstration

One of the challenges of future space missions is the deployment of instruments distributed across multiple satellites that fly in an autonomous formation. This type of mission requires that the spacecraft communicate using inter-satellite links (ISL) and collaborate to fulfil the role of a large-scale, complex scientific instrument. In this context, precise information regarding the relative positioning of the satellites in terms of distance and orientation is essential. In radio frequency (RF) based techniques, obtaining the relative orientation between the satellites starts with estimating the angle-of-arrival (AoA) of the radio signal. This paper proposes an AoA estimation technique that can be used in Orthogonal Frequency-Division Multiplexing (OFDM) communications. Our approach consists in obtaining the AoA using the ISL, specifically the preamble whose primary roles are data link synchronization and channel estimation. Our technique allows a more accurate measurement of the AoA due to an improved ambiguity resolution of phase measurements. We give theoretical results regarding the accuracy of the AoA estimator and we validate them by tests on a real-time functioning testbed consisting in multiple National Instruments Universal Software Radio Peripheral platforms. We show that the carrier frequency offset introduces only a negligible bias in the AoA estimation and we discuss the effect of multipath propagation.

2-D Direction-of-Arrival Estimation System Using Circular Array With Mutually Coupled Reference Signal

Direction-of-arrival (DOA) systems rely on proper phase measurements acquired from receiving antennas for optimal performance. Unfortunately, imperfections in any antenna and receiver system may present false phase measurements, the major cause of DOA estimation error. This paper discusses experimental results from an actual implementation of synchronizing a DOA circular array receiver system to mitigate these imperfections. A reference signal located at the center of the DOA array was coupled to array elements such that both the coupled reference signals and signals from a target were captured simultaneously in the receiver. This coupled reference signal was used to compensate for any phase error offsets produced by receiver system imperfections on cable length, phase of local oscillators, and phase delays in active devices. Furthermore, this approach did not necessitate an off-line calibration process of each receiver channel requiring accurate measurements. We implemented a 16-element circular array DOA estimation system using the Universal Software Radio Peripheral platform operating in the $S$ - and $C$ -bands. Results show that this implementation was able to combat receiver phase offset errors and estimated the DOA angles with an rms error of 0.92°.

Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.

OFDMA-Based Device-to-Device Communication Frameworks: Testbed Deployment and Measurement Results

Device-to-device (D2D) communication exploits the proximity between two devices to enhance the overall spectrum utilization of a crowded cellular network. Two conventional frameworks that are generally used to realize a D2D communication are underlay D2D (U-D2D) and cooperative D2D (C-D2D) communications. Both frameworks have their own merits and demerits in terms of interference management, implementation complexity, and achievable spectral efficiency (SE). For instance, the performance of U-D2D is limited by the amount of interference from the D2D to the cellular user (CU), whereas for C-D2D the performance suffers due to a division of transmission into two orthogonal time phases. In this paper, we propose two novel extensions of conventional D2D communication frameworks, which alleviate their drawbacks and also improves the performance of the overall cellular network. The first proposed framework is an adaptive interference aware U-D2D communication framework. Through this framework, the CU can be protected from the excessive interference of the D2D transmitter (DT), thus maintaining the desired quality of service at CU. The second proposed framework is an adaptive C-D2D communication framework. This framework employs adaptive modulation and coding and bit error rate-based selection and combining to enhance the performance of cellular and D2D users. As a proof-of-concept demonstration and performance assessment, both the frameworks have been implemented on a National Instruments Universal Software Radio Peripheral platform. The measurement results and on-field trials show that the proposed frameworks improve the bit error rate (BER) and the SE of the overall system. Results also interpret that the adaptive C-D2D outperforms the adaptive interference aware U-D2D in terms of BER and SE.

SDR Implementation of a Testbed for Real-Time Interference Detection With Signal Cancellation

Interference greatly affects the quality of service of wireless and satellite communications, having also a financial impact for the telecommunication operators. Therefore, as the interfering events increase due to the deployment of new services, there is an increasing demand for the detection and mitigation of interference. There are several interference detectors in the literature, evaluated by using extensive simulations. However, this paper goes one step further, designing, implementing, and evaluating the performance of the developed interference detection algorithms experimentally using a software defined radio, and particularly the universal software radio peripheral platform. A realistic communication system is implemented, consisting of a transmitter, a channel emulator, and a receiver. Based on this system, we implement all the appropriate communications features, such as pulse shaping, synchronization, and demodulation. The real-time system implementation is validated and evaluated through signal and interference detection. We observe that the interference detection threshold is critical to the functioning of the system. Several existing interference detection techniques fail in practice due to this fact. In this paper, we propose a robust and practically implementable method, the selection of threshold. Finally, we present real-time experimental results for the probabilities of false alarm and detection in order to verify the accuracy of our study and reinforce our theoretical analysis.

Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment

Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas.

An Experimental Evaluation of a Cooperative Communication-Based Smart Metering Data Acquisition System

Smart meters are being deployed globally on a trial basis and are expected to enable remote reading and demand response among other advanced functions, by setting up a two-way communication network. However, it remains to be determined as to how these meters will transmit their data to an aggregation point. An elegant solution to this problem is the use of cooperative communication in a neighborhood area network. This work experimentally compares cooperative networks, deployed in disparate environments, in terms of range extension and energy consumption of the overall network. Data transmissions take place through the universal software radio peripheral platforms. The method has been implemented in both indoor and outdoor environments, with cooperative transmission (CT) taking place over a multihop network, employing the binary phase shift keying scheme. The results indicate that CT can be used to effectively and reliably relay data in a network such as that in a smart grid.

선형편파를 갖는 S-대역 SDR용 SIW 안테나 설계

Abstract In this paper, the SIW(Substrate Integrated Waveguide)-based feeding antenna for the application of SDR(Software Defined Radar) is designed and manufactured. It is usually well-known that SIWs are easily integrated on PCB and have low transmission loss toward high powered input signal. Also, it is recommended that SIWs are strongly immunized to Electromagnetic Interferences(EMI). In parti-cular, the manufactured antennas are loaded on the USRP(Universal Software Radio Peripheral) platform and employed to detect target RCS as an experiment in this paper. The operating frequency of the proposed antenna is in ISM(Industrial, Scientific and Medical) band(2.4Y2.48 GHz) and the measured gain is over 8 dBi at 2.44 GHz. Key words: Substrate Integrated Waveguide, Software Defined Radar, Slot Antenna, S-Band^_`ab .cdef]g5'h_U ijk)f]g5l9_m nop[14911-01004, qr2,s$jSDR KLM'hmn]) tu vVw.x Byz{7c|z{2Cf]{c.(Department of Electronics and Information Engineering, Korea Aerospace University) Manuscript received October 2, 2015 ; Revised December 11, 2015 ; Accepted February 2, 2016. (ID No. 20151002-22S)Corresponding Author: Jae-Wook Lee (e-mail: jwlee1@kau.ac.kr)

Blind spectrum sensing using symmetry property of cyclic autocorrelation function: from theory to practice

Abstract Spectrum sensing has been identified as the key step of the cognition cycle and the most important function for the establishment of cognitive radio. In this paper, a blind cyclostationary feature detector, which is based on the symmetry property of cyclic autocorrelation function (SP-CAF), is implemented and tested using universal software radio peripheral platform and GNU Radio open-source software development toolkit. Performance of the SP-CAF is compared to the classical energy detector via various tests conducted in real scenarios where both detection algorithms are employed to blindly sense the spectrum for opportunistic access. This study shows that the blind cyclostationary feature detector outperforms the classical energy detector while guaranteeing acceptable complexity and low sensing time. Moreover, different experimental results indicate that the blind sensing detector can achieve high detection probability at a low false alarm probability under real channel conditions and low signal-to-noise ratio.

POTENTIALITIES OF USRP-BASED SOFTWARE DEFINED RADAR SYSTEMS

Software Deflned Radar is the latest trend in radar development. To handle enhanced radar signal processing techniques, advanced radars need to be able of generating various types of waveforms, such as frequency modulated or phase coded, and to perform multiple functions. The adoption of a Software Deflned Radio system makes easier all these abilities. In this work, the implementation of a Software Deflned Radar system for target tracking using the Universal Software Radio Peripheral platform is discussed. For the flrst time, an experimental characterization in terms of radar application is performed on the latest Universal Software Radio Peripheral NI2920, demonstrating a strongly improved target resolution with respect to the flrst generation platform.

Physical layer capture aware MAC for WLANs

The physical layer capture (PLC) effect occurs frequently in the real wireless deployment; when two or more nodes transmit simultaneously, a receiver can successfully decode the collided frame if the signal strength of one frame is sufficiently high enough. Although the PLC effect increases the channel utilization, it results in an unfair channel access among the wireless nodes. In this paper, we propose a PLC-aware media access control (MAC) algorithm that employs the average waiting time as a common control reference. It enables the nodes to converge to a fair channel access by changing one of the IEEE 802.11 enhanced distributed channel access parameters: contention window, arbitration interframe space, or transmission opportunity. We then find multiple control references that meet the fair channel access constraint and obtain the near-optimal reference that maximizes the overall throughput. Through ns-2 simulations and real in-door experiments using the universal software radio peripheral platform, we evaluate the fairness and throughput performance of the PLC-aware MAC algorithm.