Universal Software Radio Peripheral Research Articles

Implementation of preamble based GFDM prototype for robust 5G systems

AbstractGeneralized frequency division multiplexing (GFDM) is a flexible block‐structured multi‐carrier scheme recently proposed for next‐generation wireless communication systems. There are various approaches suggested for its analysis and implementation via simulations but testing in real‐time environments is not heavily investigated. This paper carries out the real‐time implementation of the GFDM system utilizing software‐defined radio (SDR) by emphasizing mainly channel estimation and synchronization. Symbol timing, frequency offset, and channel estimate algorithms are applied using a windowed preamble with two identical halves to satisfy low egress noise requirements. Time and frequency estimation is evaluated in terms of residual offsets along with symbol error rate over frequency selective channels. This algorithm is extended to a preamble composed of multiple identical parts. This facilitates a large frequency estimation range at the cost of complexity. For practical validation of the above concepts, the National Instruments (NI) universal software radio peripheral (USRP) 2953R is employed as hardware and it is interfaced with LabVIEW.

LS-based multipath channel measurement method using software defined radio platform

Multipath channel measurement is the foundation of multipath channel modeling and analysis. Recently, the software defined radio (SDR) has provided new ideas for the design of multipath channel measurement systems due to universality and reconfigurability. Therefore, this paper introduces a multipath channel measurement method applicable to various SDR platforms. At first, to enhance the resolution of multipath measurements, a signal processing approach combining the least squares (LS) estimation algorithm within the SDR framework is proposed. Furthermore, to mitigate LS estimation errors stemming from high correlation among baseband signals, a scrambled measurement signal is introduced and its effectiveness is demonstrated through eigenvalue decomposition. Next, the systematic errors of In-phase and Quadrature(IQ) imbalance and bandwidth limitation are further analyzed and addressed within the SDR framework. Finally, the proposed measurement method is implemented based on a practical universal software radio peripheral (USRP) device and validated under simulated and real channels. The proposed measurement method mainly focuses on the baseband signal processing within the software component of SDR, making it applicable to most general USRP devices. The experiment results demonstrate that multipath channel measurement results with high accuracy can be obtained.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm.

Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.

Direction-finding for unmanned aerial vehicles using radio frequency methods

Nowadays, drones, also known as unmanned aerial vehicles (UAVs), are used for a wide range of purposes in the civil, industrial, and military domains. The greater the number of UAVs, the more serious the potential security risks correlated to them. To address these risks, it became necessary to develop systems that can detect, locate, and possibly neutralize such an object. The detection can be performed using different sensors such as imaging, radar, acoustic, and radio frequency (RF) sensors. The localization of the target drone, which can be done using direction finding (DF) methods, is an essential part of such a system. Thus, an RF-based DF testbed is designed and experimentally assessed in this paper. The testbed is implemented using software defined radio (SDR) platforms from the Universal Software Radio Peripheral (USRP) family as hardware equipment, and the MUSIC algorithm for processing the signals received on four different phase-coherent RF channels. A DJI Mavic Air UAV is used as target. For assessing the DF estimation performance, the direction obtained from the drone flight record is compared with the one estimated using the proposed DF testbed. Two different scenarios, a static one and a dynamic one, are considered to highlight the effectiveness of the proposed DF testbed across various conditions. The obtained results indicate an average estimation error of 1.15 degrees from the drone's actual position, in the case of the static scenario, and of 1.86 degrees in the case of the dynamic scenario, results which are on parity with or surpassed those of other drone DF systems described in the literature. The paper also outlines the benefits and challenges of the current approach.

A machine learning approach for package size estimation using UHF RFID interrogation signature

This paper introduces a new approach for performing package classification and sizing using Radio-Frequency Identification (RFID) systems. This technique is applicable when packages are labeled with or contain multiple RFID-tagged items. During the interrogation of the tags, received signal strength (RSS) statistics and other information, such as the frame count or the reading time, are collected by the reader and used to predict the package type from a set of candidate classes using an Artificial Neural Network (ANN). The primary challenge lies in acquiring sufficient training data for a target scenario to ensure reliable predictions. To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector.

A real time digital vibration acceleration fiber sensing system based on a multi-carrier modulation/demodulation technique

A multi-longitudinal mode fiber laser sensor (MLMFLS) system based on digital modulation/demodulation technique for vibration acceleration detection is proposed. To the best of the authors’ knowledge, this is the first digital vibration acceleration demodulation system ever reported by a MLMFLS system. A few beat frequency signals (BFS) generated by the MLMFLS are used as signal carriers of applied vibration signals. Based on multi-channel digital down-conversion technique, the weak vibration signals modulated on the BFS are superposed by utilizing the digital universal software radio peripheral (USRP). The signal-to-noise ratio (SNR) of the output vibration signal is greatly enhanced. A spectral subtraction method is also utilized to improve the SNR of the demodulated vibration signal. The acceleration of the vibration signal is obtained after second order differential of the time domain vibration signal. By simply changing the position of window function of short-time Fourier transform, a real-time vibration frequency and acceleration sensing system is achieved. The measurement results demonstrated a minimum detectable acceleration of 0.03 g at vibration frequency of 1.0 Hz by the designed sensing system. It also has a simple structure, high signal-to-noise ratio and stability.

Path Loss Characterization in an Outdoor Corridor Environment for IoT-5G in a Smart Campus University at 850 MHz and 3.5 GHz Frequency Bands.

The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios.

Longer Term Time Dynamics of Bit Error Rates

n this paper an analysis is conducted to study the properties of indoor and outdoor wireless channel conditions over long time scales (several minutes). It shows that long term time dynamics exist in many different situations even when transmitters and receivers are stationary and the distance between them is only a few meters. We use software-defined radio (SDR) that implements radio communication functionalities in software. Transmissions have been achieved using the Universal Software Radio Peripheral (USRP), with the signal processing and time synchronization occurring in the GNU Radio environment on the LINUX (Ubuntu 14.04 LTS) platform. The work has been implemented in a typical school environment i.e., indoor conditions including a lab environment along with a hallway and outdoor conditions comprising a quadrangle environment. We used the benchmarking tool in the GNU Radio that defines and modifies modulation schemes, transmitter and receiver gains, operating frequencies, and other channel properties. Per-packet bit error rate is computed, visualized, and compared across multiple scenarios. We find a series of interesting time dynamics of the BER over several minutes that are not commonly studied in the literature.

Hand-Breathe: Noncontact Monitoring of Breathing Abnormalities From Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method.

Robust OFDM System for Drilling Data Transmission Using Acoustic Wave

Drilling data communication mainly includes mud pulse telemetry and electromagnetic wave telemetry. These methods have the advantages of economy and convenience, but they also have the disadvantages of low data transmission rate and large signal attenuation. A data transmission system while drilling was designed based on the key technologies and basic principles of Orthogonal Frequency Division Multiplex (OFDM). The hardware of the system includes the Universal Software Radio Peripheral (USRP) motherboard and daughter board, power amplifier circuit and piezoelectric transducer. The software used in the system is C and Python. Compared with traditional methods, new methods using acoustic signals have many advantages. This new wireless transmission system was designed by combining down-hole data transmission with wireless communication. This method has advantages in oil exploration and development.

LTE Device Radio Frequency Fingerprints Blind Extraction Based on Temporal-Frequency Domain PRACH Signals

A frequency-domain cepstrum correlation and temporal transient filtered radio frequency fingerprints (RFF) blind extraction method is proposed for long term evolution uplink device authentication in this work. With the merits of the constant-modulus nature of the Zadoff-Chu (ZC) sequence and the cyclic prefix (CP) structure of the physical random access channel (PRACH) frame, the device RFF integrated in both the frequency-domain ZC sequence and the transient ramp-on/ramp-off sequences are extracted. For the frequency-domain RFF extraction, the cepstrum of the ZC sequence is calculated to enlarge the gap between the constant RFF and the random fading effects, and the correlation between two cepstrum fragments is then calculated to mitigate the random fading components. For the temporal RFF extraction, the ramp-on and ramp-off features induced by the device-specific power amplifier are extracted by exploiting the CP structure of the PRACH frame, where the CP part and the duplicate part are adaptively filtered and the final adapted weight of the least mean square filter is used as RFF. No prior PRACH parameter knowledge is required in RFF extraction and both the frequency-domain and the temporal RFF are combined as the input of the convolutional neural network to enhance the classification accuracy. With four-months sampling of PRACH signals emitted from universal software radio peripheral devices, the experimental results show that the authentication accuracy of the proposed combined RFF can reach as high as 99.9% in the line-of-sight (LOS) scenario and 99.6% in the non-LOS scenario.

SDR implementation and real-time performance evaluation of 5G channel coding techniques

This article presents a study on the real-time performance and practical implementation of several 5G channel coding candidates including low-density parity check (LDPC), polar, turbo, and convolutional channel codes. The experiment was conducted using a wireless communication transceiver system implemented on a software defined radio (SDR) platform called Universal Software Radio Peripheral (USRP). The system was implemented using Matlab software and included scrambling, channel coding, interleaving, and required synchronization and estimation blocks. Various sets of system and channel parameters were tested, including the impact of information block sizes, code lengths, and modulation orders on the performance of each channel coding technique. The study also evaluated the impact of hardware impairments and the required blocks to mitigate them. The results indicated that polar codes had the best performance, while other codes had trade-offs for various system parameters. The study concludes that polar codes with cyclic redundancy check-successive cancellation list (CRC-SCL) are the most reliable 5G channel coding candidates.

PHY-PSIONICS: Physical-Layer Phase Secret Key Encapsulation in Correlated Subchannels

In this letter, we propose a physical-layer (PHY) phase secret key encapsulation in correlated subchannels, PHY-PSIONICS. Based on the PHY phase challenge-response authentication scheme (PHY-PCRAS), conventional PHY authentication approaches generally exploit the channel phase’s reciprocity to encapsulate the secret key. However, for typical multi-carrier systems, correlations between neighboring subcarriers could cause these schemes to lose credibility. Therefore, we propose a novel key encapsulation scheme to allow PHY phase-based authentication in practical multi-carrier systems. The shared secret key is efficiently encapsulated by the binning-based multi-bit alphabet phase fingerprinting. We constructed a testbed with several universal software radio peripherals for verification to evaluate the authentication performance experimentally in an indoor environment containing correlated subchannels. The numerical findings demonstrate that the proposed scheme is strong against eavesdropping and replay attacks, even under subchannel correlations.

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

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

SDR DESIGN AND IMPLEMENTATION OF DIFFERENTIAL STBC TRANSMISSION

Multiple-input-multiple-output (MIMO) technology uses multiple antennas and advanced signal processing to increase the communication system capacity, reliability and data rate without sacrificing energy consumption or RF bandwidth. In some scenarios, the MIMO communication system need to be designed without knowledge of the channel state information. One possible solution is differential space–time block coding (DSTBC). Ettus USRPs (Universal Software Radio Peripherals) are used in this research work to prototype a DSTBC-based 2 × 1 MISO (multiple-input-single-output) communication system. The signal processing is performed by software with MATLAB. This software defined radio (SDR) approach allows a reconfigurable implementation of the Differential STBC algorithm with tuneable parameters.

Implementation of DNN-Based Physical-Layer Network Coding

Physical-layer network coding (PNC) has been proposed to double the throughput of a wireless two-way relay network (TWRN) and has been adapted so that it can be applied in vehicular ad-hoc networks (VANETs). Deep neural network (DNN)-based physical-layer network coding (PNC) for the TWRN system has been studied in recent years, under the assumption that the channel state information (CSI) is available to the end nodes and the relay node. However, the state-of-the-art DNN-based PNC system was only feasible for theoretical simulations, and no practical system design has been studied. When moving from theory to practical wireless systems, there are two critical issues to tackle: 1) unlike conventional regularly shaped quadrature amplitude modulations (QAMs), the constellation points of a DNN-based PNC system's end nodes are irregular and unpredictable to the relay node; and 2) other than in high-precision simulations, the practical DNN-based PNC system's constellation points are not fixed, that is, there is usually a cluster of points that must be grouped together so that the transceiver's digital-to-analog converter (DAC) and analog-to-digital converter (ADC) can work properly. To solve the above issues, we proposed the DNN-PNC implementation that can be realized using the Universal Software Radio Peripheral (USRP) platform. The computational complexity of our simulated DNN-PNC is one order of magnitude smaller than that given in the literature, while maintaining a good bit error rate (BER). In the implementation phase, we find that the naturally-happened power imbalance between the two end nodes' signals in our implementation can significantly boost the PNC constellation recovery, and hence reduce the system BER. Our experimental results showed that the BER of DNN-PNC can be lower than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-4}$</tex-math></inline-formula> when the signal-to-noise ratio (SNR) is 16 dB for the 2-QAM case and 20 dB for the 4-QAM case, respectively. These results indicate that DNN-based PNC is feasible for VANET applications, where the CSI changes and is difficult to obtain.

Software-Defined Radio-Based Contactless Localization for Diverse Human Activity Recognition

This paper presents a study on contactless localization for activity recognition based on radio-frequency sensing. The focus of this study is on the quantitative analysis of the collected data, which is in the form of channel state information (CSI). The proposed method utilizes a software-defined radio (SDR) system in combination with an ensemble learning technique to localize and identify daily living activities such as leaning, sitting, standing and walking. Specifically, SDR device, Universal Software Radio Peripheral (USRP) models X300/X310 are utilized to collect data on the aforementioned activities. The data is collected from an empty space and a participant performing daily living activities in different territories. The acquired data is labelled based on the region, zone and performed activity. The CSI data is subsequently preprocessed and fed into an ensemble-based machine learning algorithm for classification. Furthermore, the stability analysis of the proposed method is performed to evaluate its reliability and robustness. The performance of the algorithm is evaluated using various metrics, including a confusion matrix, accuracy, cross-validation score and training time. The results demonstrate that the proposed ensemble-based approach achieves a high accuracy of up to 90% in activity recognition, highlighting the effectiveness of the proposed method in contactless localization for activity recognition.

Validation hybrid filter detection for Multi-User multiple input multiple output F-OFDM by Universal software radio Peripheral

The present communication system is not enough efficient for the data rate requirement for the real-time applications like video conferencing and file sharing. To meet the requirement of the data rate, the concept of F-OFDM (Filter- Orthogonal Frequency Division Multiplexing) has been proposed. To further improve the efficiency of the system, Cognitive Radio is used. Cognitive Radio is capable of recognizing the spectrum utilization of the licensed users and accordingly switches the frequency bands. The aim of the paper is to detect the primary users in an efficient and novel way. Therefore, a hybrid filter detection technique is proposed for the Multi-User Multiple Input Multiple Output (MU-MIMO) F-OFDM system where multiple users are served with the same time and frequency resource and improve signal strength. The proposed technique uses the combination of cosine filtering, Bartlett segmenting, and Hamming windowing. The cosine filtering differentiated between the 5G traffic and the noise. Then, the filtered signals are segmented with the help of the Bartlett Segmenting for decreasing the noise variance then every segment is windowed using the Hamming windowing for maintaining the signal resolution. This paper proposes a Validation Hybrid Filter Detection for Multi-User Multiple Input Multiple Output (MIMO) Frequency-Division Multiplexing Orthogonal Frequency Division Multiplexing (F-OFDM) by Universal Software Radio Peripheral (USRP) based on Cognitive Radio (CR). Detection performance dropped to its least value for M−ary = 256 and 10 user SUs. Further, false alarm probability at −24 dB SNR was less than 0.01, which is excellent considering system characteristics of less than 0 dB SNR, 0.092% and > 98% global system error and detection likelihoods, respectively. The study also evaluated spectral efficiency to offer optimum detection at acceptable complexity levels for low SNR signals. The performance of the proposed technique is compared with the existing techniques and it is found that the proposed technique better and can effectively improve the detection accuracy of the signal with low SNR.

Design and FPGA-Based Hardware Implementation of NB-IoT Physical Uplink Shared Channel Transmitter and Physical Downlink Shared Channel Receiver

With the anticipated growth of the internet of things (IoT) market, many low-power wide-area (LPWA) technologies have been introduced to connect a wide range of IoT devices with varying performance requirements. The narrowband internet of things (NB-IoT) is a 3rd generation partnership project (3GPP) standardized LPWA technology that meets most IoT service requirements. In this paper, the design and implementation of the physical uplink transmitting chain as well as the physical downlink receiving chain of the NB-IoT user equipment (UE) are presented. Both chains’ main blocks are designed to follow the 3GPP NB-IoT LTE standard Release 14 (Rel-14). The whole design is experimentally implemented on the Virtex 7 (VC 709) Connectivity kit, and all performance metrics are reported. Moreover, an NB-IoT base station is implemented and integrated with the two prototyped UEs to set-up an NB-IoT system, which is employed to send data from one UE to another UE through the NB-IoT base station using two FPGAs (one to implement the sending UE and the other one to implement the receiving UE) and three universal software radio peripherals (USRPs) B200 (one to implement the RF front-end of the transmitting UE FPGA, one to implement the base station RF front-end, and one to implement the RF front-end of the receiving UE FPGA). Experimental results show that the implemented NB-IoT system is working successfully, as the two NB-IoT UEs communicate together successfully through the NB-IoT base station and exchange the data properly.