Real-time Hardware Research Articles

A facility for the research, development, and translation of advanced technologies for ion-beam therapies

This article describes the updated GSI radiotherapy research facility (Cave M) located at the GSI Helmholtz Center for Heavy Ion Research in Darmstadt, Germany. This facility was upgraded by modernizing the beamline that supported a pilot project in carbon ion cancer therapy in Europe from 1997 to 2008. Descriptions are provided of the modernized beamline, related hardware components and treatment delivery system. The performance specifications and general characteristics for each major component are described, along with example pre-clinical test results of selected components. These upgrades to Cave M allow for investigating novel therapy methods. The radiotherapy research facility is located on a beamline of the heavy ion synchrotron (Schwer-Ionen-Synchrotron, or SIS-18) accelerator complex, capable of delivering 0.1 to 2 GeV/u charged particle beams, ranging from protons to uranium. This beamline contains components for fast beam gating, aborting, focusing, scanning, monitoring, and shifting the range of the beam. The beam scanning magnets, position detectors, and beam monitors are described, along with tests of functionality and performance. A dose delivery system (DDS) was adapted from a clinical unit at the National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy, and consists of modular real-time hardware and software. The DDS was modified to enable research on adaptively-managed patient motion through the use of libraries of 4D-optimized radiation treatment plans, an unsolved problem of importance for treating moving tumors. The system is modular and is designed to support future research studies, such as high dose rate (Flash) radiotherapy and radioactive ion beams. A series of validation tests confirmed the functionality and performance of various key components and systems. For example, an end-to-end test revealed that dosimetric spatial homogeneity of over 95% was achieved for square treatment fields. More generally, all performance characteristics that were tested satisfied anticipated clinical requirements.

Novel Roadside Unit Positioning Framework in the Context of the Vehicle-to-Infrastructure Communication System Based on AHP — Entropy for Weighting and Borda — VIKOR for Uniform Ranking

The positioning of roadside units (RSUs) in a vehicle-to-infrastructure (V2I) communication system may have an impact on network performance. Optimal RSU positioning is required to reduce cost and maintain the quality of service. However, RSU positioning is considered a difficult task because numerous criteria, such as the cost of RSUs, the intersection area and communication strength, affect the positioning process and must be considered. Furthermore, the conflict and trade-off amongst these criteria and the significance of each criterion are reflected on the RSU positioning process. Thus, this work proposes a new RSU positioning framework based on multicriteria decision-making (MCDM) in the context of the V2I communication system. Three stages are completed for this purpose. First, a real-time V2I hardware is developed to collect data. The developed hardware consists of multiple mobile nodes (i.e., cars with sending–receiving hardware devices) and physical RSUs. The RSUs and the devices in the cars are connected via the nRF24L01[Formula: see text]PA/LNA transceiver module with Arduino Uno. Second, seven testing scenarios are identified toward acquiring the required data upon the connection of the V2I devices. Moreover, three evaluation attributes (i.e., number of packet losses [PKL], cost and ratio of intersection area [RIA]) are used to evaluate each scenario. A decision matrix is constructed on the basis of the crossover between ‘RSU positioning scenarios’ and ‘multi-evaluation attributes (i.e., PKL, cost and RIA)’. Third, the RSU positioning scenarios are ranked using MCDM techniques, such as the integrated analytic hierarchy process (AHP), entropy and group Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Furthermore, the Borda voting approach is used to aggregate multiple individual rankings into a uniform and final rank. Results indicate the following: (1) integrating AHP, entropy and VIKOR is effective for solving RSU positioning problems; (2) the VIKOR ranking results for individuals vary; (3) the rank of scenarios obtained from the group-VIKOR-based Borda voting context shows that the second scenario, which consists of four RSUs distributed along the street with a maximum distance of 200[Formula: see text]m between them and 2-m high antennas, is the best in terms of optimally placing the RSUs; and (4) significant differences are observed amongst the scores of the groups, indicating that the ranking results are valid.

An MPPT Strategy Based on a Surface-Based Polynomial Fitting for Solar Photovoltaic Systems Using Real-Time Hardware

This paper presents an optimal design of a surface-based polynomial fitting for tracking the maximum power point (MPPT) of a photovoltaic (PV) system, here named surface-based polynomial fitting (MPPT-SPF). The procedure of the proposed MPPT-SPF strategy is based on a polynomial model to characterize data from the PV module with a global fit. The advantage of using polynomials is that they provide a good fit within a predefined data range even though they can diverge greatly from that range. The MPPT-SPF strategy is integrated with a DC-DC boost converter to verify its performance and its interaction with different control loops. Therefore, the MPPT strategy is applied to the reference outer PI control loop, which in turn provides the current reference to the inner current loop based on a discrete-time sliding current control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardware-in-the-loop system to obtain the results. The proposed strategy does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared with a conventional perturb and observe method to prove its effectiveness under demanding tests.

Cyber Attack Detection and Correction Mechanism in Distributed DC Microgrid

DC microgrids (DCMGs) are evolving into cyber-physical systems with advanced communication networks and computational methodologies, making them sensitive to cyber-attacks. These attacks can threaten the safe operation of a system. In this article, a virtual sensor-based framework is proposed for continuously monitoring and detecting malicious activities at the hardware level of a DCMG system. An adaptive state observer with a low computational burden is designed to estimate the voltage and current of a dc–dc converter. The proposed method considers the effects of cyber-attacks by injecting false data (FD) at targeted nodes in an attempt to affect the voltage regulation and current sharing by modifying the onboard voltage and current sensors. In the presented strategy, first, a DCMG is operated and controlled without any false data injecting attacks with different load conditions. Next, the DCMG is exposed to various cyber-attacks, and the output voltages and currents of dc–dc converters are estimated. Based on the error between the estimated and actual sensed signals, the cyber-attack is detected and corrected with the proposed framework. The proposed scheme is simple in design, efficient in operation, and easily implemented in a distributed control system. The effectiveness and robustness of the proposed method are illustrated by implementing it in real-time hardware using the LabView test bench by considering four dc systems (including a solar photovoltaic system) connected in a ringlike manner communicated via an ethernet-based local area network.

Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell

State of health (SOH) and remaining useful life (RUL) are two major key parameters which plays a major role in battery management system. In recent years, various machine learning approaches have been proposed to estimate SOH and RUL effectively for establishing the battery conditions. In the proposed work establishes an effective method to predict the battery aging process with accurate battery health estimation with real time simulations and hardware approach. This paper effectively exhibits a process to estimate SOH and RUL of a Li-Ion 18650 cell which are based on various factors like state of charge, discharge voltage transfers characteristics, internal resistance and capacity. To identify an optimal SOH and RUL machine learning based estimation approach, various battery’s statistical models are developed and implemented on a standalone hardware platform. The experimental results in this real time application shows that SOH is predicted by deep neural network approach which are found to be within the accepted error rate of ±5% and long short time memory neural network model estimates a battery’s RUL effectively with an accuracy of ±10 cycles. This approach exhibits various machine learning models in an realistic hardware platform which establishes optimal battery life.

Adaptive Nonlinear Control of Unified Model of Fuel Cell, Battery, Ultracapacitor and Induction Motor Based Hybrid Electric Vehicles

The public awareness about global warming, emission of green-house gases and depletion of natural resources like oil and natural gas, are the main factors due to which fuel cell hybrid electric vehicles (FHEVs) have attained importance in automotive industry. Hard driving conditions like steep areas, slippery roads and rough terrains boost up the nonlinearities present in vehicle’s model. The considered unified mathematical model of FHEV is based on fuel cell as a primary source, ultracapacitor and battery as storage units as well as the induction motor dynamics. The variations in parameters like resistance, capacitance, inductance and the nonlinearities of the dynamical system have also been considered. Three adaptation based nonlinear controllers namely adaptive terminal sliding mode, adaptive terminal synergetic and adaptive synergetic controllers have been proposed for the regulation of DC bus voltage along with speed tracking when subjected to European extra urban driving cycle. Lyapunov stability theory has been used to ensure global asymptotic stability of the system. Proposed controllers have been simulated on MATLAB/Simulink, where their comparison has been presented with each other and with recently proposed nonlinear controllers in the literature. Furthermore, ATSMC has further been implemented on real-time microcontroller hardware in the loop setup. The experimental results show that it provides better performance.

Evolution of the energy efficiency of LHCb’s real-time processing

The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4 TB/s in real time. Because LHCb’s physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the realtime processing software and hardware which will be used for the upgrade of the LHCb detector. We also review the impact of the decision to adopt a hybrid computing architecture consisting of GPUs and CPUs for the real-time part of LHCb’s future data processing. We discuss the implications of these results on how LHCb’s real-time power requirements may evolve in the future, particularly in the context of a planned second upgrade of the detector.

Research on the control strategy of phase balance in H-bridge cascade SVG

Based on the H-bridge cascaded SVG as one of the best solutions to effectively compensate reactive currents, this article focuses on the cascaded high-voltage H-bridge SVG phase balance problem. While analyzing the influence factors of the SVG DC side voltage, it summarizes and introduces 5 SVG phase balance control strategy ， Analyzed its real-time performance, stability, applicable occasions, algorithm and hardware circuit complexity, etc.Some control strategies have been simulated and verified, and the simulation results have reached the expected value, which provides a reference for the selection of balance control strategies for high-voltage SVG equipment.

A Lightweight and Real-Time Hardware Architecture for Interference Detection and Mitigation of Time Synchronization Attacks Based on MLP Neural Networks

Stationary GPS receivers provide time information for critical infrastructures, such as phasor measurement units (PMUs), communication networks, and financial systems. Therefore, they are prone to a specific type of spoofing attack called time synchronization attack (TSA), which affects time information such as clock offset and clock drift. The receiver’s position remains constant during the attack; hence, attack detection and mitigation are challenging. Various countermeasures have been suggested to mitigate TSA effects. However, they are mainly software-based and are exploited to protect software implemented software-defined radios (SDRs). In this research, two hardware protection approaches are contributed for hardware-based SDRs based on multi-layer perceptron neural network (MLP NN) with sigmoid activation function. The most challenging part of MPL NN implementation is the activation function approximation. Therefore, two lightweight architectures are proposed for sigmoid function implementation. Linear approximation and look-up table (LA-LUT) and piece-wise linear approximation (PLA) are exploited for this task. The synthesis results demonstrate that the PLA approach has a slightly higher resource utilization in comparison to LA-LUT, while this method is more accurate. The mean squared error (MSE) of the PLA approach is equal to 0.019, which is 57% better than the LA-LUT approach with an MSE of 0.033. Furthermore, the designs are evaluated by two conventional types of TSA. According to the results, both methods are lightweight, and they only consume less than 0.3% of slice registers, 5% of slice LUTs, and 8% of DSP48E1Ss. Furthermore, they are real-time, and can mitigate the attack consequences; however, the PLA architecture has a better performance compared to LA-LUT.

Processing chain for 3D histogram of gradients based real-time object recognition

3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

Grid-Interconnected Solar Photovoltaic System for Power Quality Improvement Using Extended Reference Signal Generation Strategy

Abstract This research article exhibits a utility grid-interfaced solar photovoltaic (SPV) system for power quality enhancement. An adaptive proportional integral derivate (APID) controller with a unit vector template (UVT) is employed to generate a reference current signal of a shunt active power filter (APF). The UVT-based control scheme is used to separate the fundamental components from the load currents and to estimate the reference phase currents. These currents are used to generate pulse width modulation signal for the shunt APF. To maintain a constant dc link voltage of shunt APF, an APID controller is employed in the dc link voltage regulator. This extended reference signal generation scheme of shunt APF remarkably enhances the system performance and also diminishes the current and voltage harmonics in the power distribution system. The presented reference current generation scheme has been mathematically examined, and digital simulation results under different steady and dynamic states are presented. The SPV system is accomplished by utilizing the incremental conductance maximum power point technique and is interconnected with the utility grid. Finally, the entire system is validated through the real-time hardware prototype developed by using dsPIC30F4011.

Systematic Approach for State-of-the-Art Architectures and System-on-Chip Selection for Heterogeneous IoT Applications

The Internet of Things (IoT) refers to a network of physical devices, which collects data and processes into a system without human intervention. In the commercialized market, IoT architectures are upgrading day by day to reduce data transmission costs, latency, and bandwidth usage for various application requirements. The extensively available IoT architectures and their specification resist the researchers to select a system-on-chip (SoC) for heterogeneous IoT applications. This paper seeks to comprehend the various IoT device specifications and their characteristics to support multiple applications. Moreover, microprocessor architectures and their components are detailed to facilitate developer knowledge in advanced methodology and technology. The various instructions set architectures (ISA) are implemented in a Zynq-7000 (xc7Zz20clg484-1) FPGA device to examine the feasibility of design space requirements for real-time hardware execution. To select specific system-on-chip (SoC) architecture for heterogeneous IoT applications, a genetic algorithm (GA) based optimization method is implemented in MATLAB. The proposed algorithm identifies the optimized SoC architecture concerning device parameters such as a clock, cache, RAM space, external storage, network support, etc. Further, the confusion matrix method evaluates the proposed algorithm's accuracy, which yields 84.62% accuracy. The outcome of SoCs attained through the GA are tested by analyzing their execution time and performance using various evaluation benchmarks. This article helps the researchers and field engineers to comprehend the microarchitecture device configurations and to identify the superior SoC for next-generation IoT practices.

Comparative study of three different bridge-less converters for reduction of harmonic distortion in brushless DC motor

<p>This paper represents a comparative study of three different types of DC-DC<br />converter that can be used for reduction of Total Harmonic Distortion (THD)<br />in stator current, back electromotive force (EMF) and torque of brush less<br />DC motor. In addition, the topologies of these converters are analysed, and<br />the THD of the output characteristics have also been studied. In this work,<br />SEPIC, Zeta and Flyback converters are considered and their outputs are fed<br />to the BLDCM with the help of universal bridge or six step inverter.<br />Moreover the THD of the output voltages are not only measured for three<br />converters but also reduced by tuning the parameters. At first these three<br />converters are modeled in MATLAB/ Simulink based simulation platform<br />and studied the performance individually and further executed with hardware<br />circuitry. Finally the output parameters from both software simulation and<br />real time hardware are compared for these three converters separately and got<br />satisfactory similar results. Again we studied the performance with these<br />converters in terms of efficiency while fed in the commutation drive circuitry<br />of BLDCM by considering minimum THD. From this comparative<br />simulation results, it has been observed that Zeta converter showed maximum<br />efficiency. Therefore, in real time hardware implementation, the<br />commutation drive circuitry of BLDCM is studied with Zeta converter. With<br />this configuration, a comparatively low THD of stator current, back EMF and<br />electromagnetic torque have been achieved in BLDCM with PID controller.</p>

Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive

<span>Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the speed performance in terms of the wide range of operations and disturbance showed remarkable performance.</span>

1D convolutional neural networks and applications: A survey

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publicly shared in a dedicated website. While there has not been a paper on the review of 1D CNNs and its applications in the literature, this paper fulfills this gap.

Always-On Quantum Error Tracking with Continuous Parity Measurements

We investigate quantum error correction using continuous parity measurements to correct bit-flip errors with the three-qubit code. Continuous monitoring of errors brings the benefit of a continuous stream of information, which facilitates passive error tracking in real time. It reduces overhead from the standard gate-based approach that periodically entangles and measures additional ancilla qubits. However, the noisy analog signals from continuous parity measurements mandate more complicated signal processing to interpret syndromes accurately. We analyze the performance of several practical filtering methods for continuous error correction and demonstrate that they are viable alternatives to the standard ancilla-based approach. As an optimal filter, we discuss an unnormalized (linear) Bayesian filter, with improved computational efficiency compared to the related Wonham filter introduced by Mabuchi [New J. Phys. 11, 105044 (2009)]. We compare this optimal continuous filter to two practical variations of the simplest periodic boxcar-averaging-and-thresholding filter, targeting real-time hardware implementations with low-latency circuitry. As variations, we introduce a non-Markovian ``half-boxcar'' filter and a Markovian filter with a second adjustable threshold; these filters eliminate the dominant source of error in the boxcar filter, and compare favorably to the optimal filter. For each filter, we derive analytic results for the decay in average fidelity and verify them with numerical simulations.

Adaptive Control of Fuel Cell and Supercapacitor Based Hybrid Electric Vehicles

In this paper, an adaptive nonlinear control strategy for the energy management of a polymer electrolyte membrane fuel cell and supercapacitor-based hybrid electric vehicle is proposed. The purpose of this work was to satisfy: (i) tight DC bus voltage regulation, (ii) good fuel cell reference current tracking, (iii) better supercapacitor reference current tracking (iv) global asymptotic stability of the closed-loop control system, and (v) better vehicle performance by catering to slowly-varying parameters. We have selected the power stage schematic of a hybrid electric vehicle and utilized adaptive backstepping and adaptive Lyapunov redesign-based nonlinear control methods to formally derive adaptive parametric update laws for all slowly-varying parameters. The performance of the proposed system has been tested under varying load conditions using experimental data from the “Extra Urban Driving Cycle.” Mathematical analysis and Matlab/Simulink results show that proposed controllers are globally asymptotically stable and satisfy all the design requirements. The physical effectiveness of proposed system has been verified by comparing simulation results with the real-time controller hardware in the loop experimental results. Results show that proposed system shows satisfactory performance and caters for the time-varying parametric variations and the load requirements.

Ultra-Low-Latency, High-Fidelity Analog-to-Digital-Compression Radio-Over-Fiber (ADX-RoF) for MIMO Fronthaul in 5G and Beyond

Wired-wireless convergence technology such as radio-over-fiber (RoF) is regarded as a promising candidate to deliver broadband wireless data from/to edge cloud to/from antenna sites, a notable example being the fronthaul (FH) in centralized/cloud radio access network (C-RAN). In 5G and beyond, the latency, bandwidth and fidelity requirements on FH pose great challenges to the RoF schemes. In this article, we focus on analog-to-digital-compression RoF (ADX-RoF) scheme based on low-latency MIMO data compression, which has the potential to reduce FH data rate by ~90% compared with traditional digital RoF (CPRI) while still maintaining signal fidelity for high-order radio modulation. To verify the practicality of ADX-RoF in the latency-sensitive FH scenario, real-time hardware demonstration with latency evaluation is indispensable. We propose and design a real-time ADX, which achieves low-latency and high-throughput together with high-fidelity. Enabled by the ADX prototyped on a single-chip field-programmable radio platform (i.e., Xilinx RFSoC), we experimentally demonstrate 16-channel MIMO radio reception, real-time ADX processing and RoF transport. With <; 500 ns one-way ADX latency, less than 1.5% average EVM is achieved for 1024QAM, 61.44 MHz NR-class signals at a compression ratio of 13.3%. Negligible fiber-induced performance penalty is observed after 40 km transport. The results highlight the attractiveness of ADX-RoF for future FH.

Flying capacitor voltage balance and neutral point voltage regulation of PV array fed active neutral point clamped converter in real‐time HIL

This study deals with a new control strategy for flying capacitor (FC) based multilevel converter, in order to attain the desired reference value for five-level output voltage in active neutral point clamped (ANPC) converter. The balancing of capacitor voltages is one of the crucial aspects of grid-tied photovoltaic (PV) fed ANPC converter. The accurate reference voltage for FC is required to experience minimum power pulsation and capacitor voltage imbalance. An offset injection-based FC voltage regulator is implemented to track the desired voltage reference for FC. A simple proportional–integral controller generates the offset signal, which is added to the sinusoidal modulating wave to eliminate the FC imbalance. Moreover, neutral point voltage (NPV) balancing is achieved with the help of DC component injection control. The DC–AC power transfer, FC balance, NPV balance and unity power factor operation are achieved simultaneously. The performance of the closed-loop controller for grid-tied PV-fed ANPC converter is analysed at constant and variable irradiances. The system is modelled in MATLAB/Simulink for offline simulation. Furthermore, offline simulation results are validated using real-time controller hardware in loop (HIL) test bench. Moreover, active power is injected into the AC grid seamlessly and results are found compliant with the IEEE 519 standard.

Hardware Implementation of Overlap-Save-Based Fading Channel Emulator

An efficient hardware implementation of correlated Rayleigh fading channel simulator is presented in this brief. It emulates Doppler effects based on the use of Overlap-Save (OLS) method. OLS is used for both, the fading variates generator and the time domain interpolator, leading to a scalable complete solution. Moreover, additional simplifications were introduced to reduce even further algorithmic complexity when compared with the original OLS-based proposal. An efficient hardware implementation is achieved through maximizing the utilization rate of allocated hardware resources. When added to its scalability, this makes the proposal appealing to emulate channels with multipath effects for MIMO systems. Indeed, the proposed parallel architecture enables a throughput of 34 Mega Samples per Second per path at a clock frequency of 275 MHz on Xilinx Virtex-7 FPGA. The achieved throughput allows the support of the most demanding LTE configuration with 20 MHz channel bandwidth. To the best of our knowledge, this is the first ever real-time hardware implementation of OLS-based channel emulator.