Showing papers on "Digital signal processing published in 2020"

Learning in the Frequency Domain

Abstract: Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed without accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well-known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.60% and 0.63% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1.42%. In addition, we observe a 0.8% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset.

800G DSP ASIC Design Using Probabilistic Shaping and Digital Sub-Carrier Multiplexing

Abstract: The design of application-specific integrated circuits (ASIC) is at the core of modern ultra-high-speed transponders employing advanced digital signal processing (DSP) algorithms. This manuscript discusses the motivations for jointly utilizing transmission techniques such as probabilistic shaping and digital sub-carrier multiplexing in digital coherent optical transmissions systems. First, we describe the key-building blocks of modern high-speed DSP-based transponders working at up to 800G per wave. Second, we show the benefits of these transmission methods in terms of system level performance. Finally, we report, to the best of our knowledge, the first long-haul experimental transmission – e.g., over 1000 km – with a real-time 7 nm DSP ASIC and digital coherent optics (DCO) capable of data rates up to 1.6 Tb/s using two waves (2 × 800G).

DDSP: Differentiable Digital Signal Processing

Abstract: Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods. In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods. Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks. Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources. In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning. The library is publicly available at this https URL and we welcome further contributions from the community and domain experts.

Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning.

Abstract: In long-haul optical communication systems, compensating nonlinear effects through digital signal processing (DSP) is difficult due to intractable interactions between Kerr nonlinearity, chromatic dispersion (CD) and amplified spontaneous emission (ASE) noise from inline amplifiers. Optimizing the standard digital back propagation (DBP) as a deep neural network (DNN) with interleaving linear and nonlinear operations for fiber nonlinearity compensation was shown to improve transmission performance in idealized simulation environments. Here, we extend such concepts to practical single-channel and polarization division multiplexed wavelength division multiplexed experiments. We show improved performance compared to state-of-the-art DSP algorithms and additionally, the optimized DNN-based DBP parameters exhibit a mathematical structure which guides us to further analyze the noise statistics of fiber nonlinearity compensation. This machine learning-inspired analysis reveals that ASE noise and incomplete CD compensation of the Kerr nonlinear term produce extra distortions that accumulates along the DBP stages. Therefore, the best DSP should balance between suppressing these distortions and inverting the fiber propagation effects, and such trade-off shifts across different DBP stages in a quantifiable manner. Instead of the common ‘black-box’ approach to intractable problems, our work shows how machine learning can be a complementary tool to human analytical thinking and help advance theoretical understandings in disciplines such as optics. Nonlinear effects provide inherent limitations in fiber optical communications. Here, the authors experimentally demonstrate improved digital back propagation with machine learning and use the results to reveal insights in the optimization of digital signal processing.

DDSP: Differentiable Digital Signal Processing

Abstract: Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods. In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods. Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks. Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources. In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning. The library will be made available upon paper acceptance and we encourage further contributions from the community and domain experts.

A Tutorial and Review Discussion of Modulation, Control and Tuning of High-Performance DC-DC Converters Based on Small-Signal and Large-Signal Approaches

Abstract: Many commercial controller implementations for dc-dc converters are based on pulse-width modulation (PWM) and small-signal analysis. Increasing switching frequencies, linked in part to wide bandgap devices, provide the opportunity to increase operating bandwidth and enhance performance. Fast processors and digital signal processing offer new computational techniques for power converter control. Conventional control techniques rarely make full use of operating capability. The objectives of this paper are to present an overview and link to literature on conventional modulation and control techniques for hard-switched dc-dc converters, identify performance limits associated with conventional small-signal-based design, discuss geometric control approaches, and compare strategies for control tuning. The discussion shows how current mode controls have alternative state feedback implementations, and describes unusual opportunities for large-signal control tuning. Considerations for minimum response time are described. Comparisons among tuning methods illustrate how geometric controls can achieve order of magnitude dynamic performance increases. The paper is intended as a baseline tutorial reference for future work on power converter control.

Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications

Abstract: Signal processing over graphs has recently attracted significant attention for dealing with the structured data. Normal graphs, however, only model pairwise relationships between nodes and are not effective in representing and capturing some high-order relationships of data samples, which are common in many applications, such as Internet of Things (IoT). In this article, we propose a new framework of hypergraph signal processing (HGSP) based on the tensor representation to generalize the traditional graph signal processing (GSP) to tackle high-order interactions. We introduce the core concepts of HGSP and define the hypergraph Fourier space. We then study the spectrum properties of hypergraph Fourier transform (HGFT) and explain its connection to mainstream digital signal processing. We derive the novel hypergraph sampling theory and present the fundamentals of hypergraph filter design based on the tensor framework. We present HGSP-based methods for several signal processing and data analysis applications. Our experimental results demonstrate significant performance improvement using our HGSP framework over some traditional signal processing solutions.

Time-Encoding Analog-to-Digital Converters: Bridging the Analog Gap to Advanced Digital CMOS-Part 1: Basic Principles

Abstract: The scaling of CMOS technology deep into the nanometer range has created challenges for the design of highperformance analog ICs The shrinking supply voltage and presence of mismatch and noise restrain the dynamic range, causing analog circuits to be large in area and have a high power consumption in spite of the process scaling Analog circuits based on time encoding [1], [2] and hybrid analog/digital signal processing [3] have been developed to overcome these issues Realizing analog circuit functionality with highly digital circuits results in more scalable design solutions that can achieve excellent performance This article reviews the basic principles of time encoding applied, in particular, to analog-to-digital converters (ADCs) based on voltage-controlled oscillators (VCOs), one of the most successful time-encoding techniques to date

Joint Radar-Communication With Cyclic Prefixed Single Carrier Waveforms

Abstract: In recent years, software defined radio and digital signal processing have been widely used in communication and radar. As a result, the hardware and RF front-end for radar and wireless communication tends to be similar. Thus, using the same RF and hardware platform for joint radar-communication becomes viable. Joint radar-communication would bring more efficient plan and usage for the radio spectral resource. Furthermore, it could enable new applications that require information exchange and precise localization at the same time. In this paper, cyclic prefixed single carrier (CP-SC) and its variations are chosen as the waveforms for joint radar-communication. CP-SC waveform and its variations are popular in wireless communication and have been chosen by a few standards like IEEE 802.11ad and LTE-advanced. Efficient algorithms are proposed to use such waveforms for range and speed detection/estimation of targets. The proposed algorithms are derived from the maximum likelihood (ML) principle and have low computational complexity. Simulations show that the estimation performance of the proposed method is almost the same as that of ML and is much better than that of the channel estimation based method.

Real-Time 100-GS/s Sigma-Delta Modulator for All-Digital Radio-Over-Fiber Transmission

Abstract: All-digital radio-over-fiber (RoF) transmission has attracted a significant amount of interest in digital-centric systems or centralized networks because it greatly simplifies the front-end hardware by using digital processing. The sigma-delta modulator (SDM)-based all-digital RoF approach pushes the digital signal processing as far as possible into the transmit chain. We present a real-time 100-GS/s fourth-order single-bit SDM for all-digital RoF transmission in the high-frequency band without the aid of analog/optical up-conversion. This is the fastest sigma-delta modulator reported and this is also the first real-time demonstration of sigma-delta-modulated RoF in the frequency band above 24 GHz. 4.68 Gb/s (2.34 Gb/s) 64-QAM is transported over 10-km standard single-mode fiber in the C-band with 6.46 $\text{}\%$ (4.73 $\%$ ) error vector magnitude and 3.13 Gb/s 256-QAM can be even received in an optical back-to-back configuration. The carrier frequency can be digitally tuned at run-time, covering a wide frequency range from 22.75 to 27.5 GHz. Besides, this high-speed sigma-delta modulator introduces less than 1 μs latency in the transmit chain. Its all-digital nature enables network virtualization, making the transmitter compatible with different existing standards. The prominent performance corroborates the strong competitiveness of this SDM-based RoF approach in high-frequency RoF 5G communication.

RoBA Multiplier: A Rounding-Based Approximate Multiplier for High-Speed yet Energy-Efficient Digital Signal Processing

Abstract: In this paper, we propose an approximate multiplier that is high speed yet energy efficient. Now-a-days, Energy minimization is one of the main design requirements especially in the portable gadgets i.e., smart phones, tablets and so on. In these types of gadgets, DSP blocks are key components, where the computational core of these blocks is the arithmetic logic unit where multiplications have a greatest share. So, by the use of the multipliers the computational part of multiplications is omitted by improving the speed and power/efficiency characteristics of multipliers as it plays a key role. In this, the approach is to round the operands to nearest exponent of two. By this approximations are made for improving the speed and efficiency. Since the final outputs are used in two Image processing applications, i.e., image sharpening and smoothing. This can be performed at different design abstraction levels i.e., circuit, logic and architecture levels using different techniques, here we use function approximation method (e.g., modifying the Boolean function of a circuit), a number of approximating arithmetic building blocks, such as adders, multipliers have been suggested. Finally, It has added advantage that it can be used as common multiplier design for both signed and un-signed operations and reduces logic size and facilitates with less power and delay. Here we are using Verilog HDL and Xilinx ISE14.8 software tools for simulation and synthesis purpose.

The Method Of Calibration Compensation For Fiber Nonlinearity- A Review

Abstract: Digital signal processing for compensation for fibre nonlinearity Is a key enabler of ever-greater demand higher data rates for unified optical transmissions. A big challenge to the existing techniques that is the nonlinear coefficient of fiber Must be properly scale-up during compensation In order to attain the achieveable improvement in signal quality. In this paper, we have reviewed several papers in Fiber Nonlinear using several algorithms as well as processing the signal transmitting This problem is adaptively optimized using a low-complexity algorithm A soft-decision, bitwise demodulator based metric used for modern applications Decoders from the FEC. The study demonstrates an optimal scaling factor analysis with predictable accuracy.

Real-time gap-free dynamic waveform spectral analysis with nanosecond resolutions through analog signal processing

Abstract: Real-time tracking of a waveform frequency content is essential for detection and analysis of fast rare events in communications, radar, radio astronomy, spectroscopy, sensing etc. This requires a method that can provide real-time spectrum analysis (RT-SA) of high-speed waveforms in a continuous and gap-free fashion. Digital signal processing is inefficient to perform RT-SA over instantaneous frequency bandwidths above the sub-GHz range and/or to track spectral changes faster than a few microseconds. Analog dispersion-induced frequency-to-time mapping enables RT-SA of short isolated pulse-like signals but cannot be extended to continuous waveforms. Here, we propose a universal analog processing approach for time-mapping a gap-free spectrogram -the prime method for dynamic frequency analysis- of an incoming arbitrary waveform, based on a simple sampling and dispersive delay scheme. In experiments, the spectrograms of GHz-bandwidth microwave signals are captured at a speed of ~5×109 Fourier transforms per second, allowing to intercept nanosecond-duration frequency transients in real time. This method opens new opportunities for dynamic frequency analysis and processing of high-speed waveforms.

Real-Time 2.2-Gb/s Water-Air OFDM-OWC System With Low-Complexity Transmitter-Side DSP

Abstract: Underwater optical wireless communication (UOWC) is of great interest to the academic and the industry community. In this article, we propose a low-complexity and effective joint transmitter-side digital signal processing (DSP) including geometric shaping, time-domain tone reservation (TR), and clipping. The peak-to-average power ratio reduction performance and implementation complexity of the proposed time-domain TR are extensively analyzed. We then develop a real-time 2.2-Gb/s system, which is optimized for its shaping ratio and clipping ratio. With the help of the efficient DSPs, an 8-dB received optical power enhancement is realized under the bit-error-rate threshold of 3.8 × 10−3. We successfully demonstrate a time-multiplexed four 4K video transmission in real-time using the proposed scheme over a 3.6-m underwater and 8-m free-space channel. The implementation details, as well as the analyses of system stability, resource utilization, and latency, are presented. The results validate the feasibility and effectiveness of the proposed scheme and a 2.2-Gbit/s real-time OWC system is demonstrated for a water-air communication link.

Forward-Inverse 2D Hardware Implementation of Approximate Transform Core for the VVC Standard

Abstract: The future video coding standard named Versatile Video Coding (VVC) is expected by the end of 2020. VVC will enable better coding efficiency than the current High Efficiency Video Coding (HEVC) standard. This coding gain is brought by several coding tools. The Multiple Transform Selection (MTS) is one of the key coding tools that have been introduced in VVC. The MTS concept relies on three transform types including Discrete Cosine Transform (DCT)-II, Discrete Sine Transform (DST)-VII and DCT-VIII. Unlike the DCT-II that has fast computing algorithms, the DST-VII and DCT-VIII rely on more complex matrix multiplication. In this paper an approximation approach is proposed to reduce the computational cost of the DST-VII and DCT-VIII. The approximation consists in applying adjustment stages, based on sparse block-band matrices, to a variant of DCT-II family mainly DCT-II and its inverse. Genetic algorithm is used to derive the optimal coefficients of the adjustment matrices. Moreover, an efficient hardware implementation of the forward and inverse approximate transform module is proposed. The architecture design includes a pipelined and reconfigurable forward-inverse DCT-II core transform as it is the main core for DST-VII and DCT-VIII computations. The proposed 32-point 1D architecture including low cost adjustment stages allows the processing of a video in 2K and 4K resolutions at 1095 and 273 frames per second, respectively. A unified 2D implementation of forward-inverse DCT-II, approximate DST-VII and DCT-VIII is also presented. The synthesis results show that the design is able to sustain a video in 2K and 4K resolutions at 386 and 96 frames per second, respectively, while using only 12% of Alms, 22% of registers and 30% of DSP blocks of the Arria10 SoC platform.

Design of Digital FIR Filters for Low Power Applications

Abstract: In recent advances in multimedia and mobile computing applications demand low power and high performances VLSI digital signal processing systems. In DSP FIR filtering is most widely used method. Major part of the design for power consumption is clocking. To enhance the performance of the Flipflop (FF) is to combine the clock pulse given to the multiple Flipflops, so that the power dissipation will reduce. By using single clock pulse, multi-bit-FF is designed for maintaining the same functionality of dual single-bit-FF. In this paper, 5-Tap FIR filter is designed using single-bit DFF and multi-bit DFF and its performance was compared with the single-bit TFF and multi-bit TFF in terms of power dissipation. This design is implemented using Tanner EDA tool.

Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network

Abstract: Few studies in the literature have researched the use of surface electromyography (sEMG) for motor assessment post-stroke due to the complexity of this type of signal. However, recent advances in signal processing and machine learning have provided fresh opportunities for analyzing complex, non-linear, non-stationary signals, such as sEMG. This paper presents a method for identification of the upper limb movements from sEMG signals using a combination of digital signal processing, that is discrete wavelet transform, and the enhanced probabilistic neural network (EPNN). To explore the potential of sEMG signals for monitoring motor rehabilitation progress, this study used sEMG signals from a subset of movements of the Arm Motor Ability Test (AMAT) as inputs into a movement classification algorithm. The importance of a particular frequency domain feature, that is the ratio of the mean absolute values between sub-bands, was discovered in this work. An average classification accuracy of 75.5% was achieved using the proposed approach with a maximum accuracy of 100%. The performance of the proposed method was compared with results obtained using three other classification algorithms: support vector machine (SVM), k-Nearest Neighbors (k-NN), and probabilistic neural network (PNN) in terms of sEMG movement classification. The study demonstrated the capability of using upper limb sEMG signals to identify and distinguish between functional movements used in standard upper limb motor assessments for stroke patients. The classification algorithm used in the proposed method, EPNN, outperformed SVM, k-NN, and PNN.

Simultaneous Detection of Anomaly Points and Fiber Types in Multi-Span Transmission Links Only by Receiver-Side Digital Signal Processing

Abstract: We experimentally demonstrate simultaneous localization of optical excess loss points and spans with different dispersion in multi-span fiber links using a neural-network based digital backpropagation.

Reducing the Error of Digital Algorithms for Deductive Signal Processing Based on Their Multi-Stage Discrete Fourier Transform by the Difference Digital Filters

Abstract: The issues of using digital methods of discrete Fourier transform of polyharmonic signals for their deductive processing by elementary devices of nanoelectronics or programmable logic devices are considered. Deductive processing of digital signals is performed on the basis of their discrete Fourier transform by adding and shifting time samples of these signals. Their digital difference filtering with integer difference coefficients provides such a discrete Fourier transform without performing multiplication operations. Unerroric of deductive processing of polyharmonic signals based on their discrete Fourier transform, which does not require multiplication operations, will reduce the error of such processing on elementary nanoelectronics devices and programmable logic devices.

DSP-Based Flexible-Waveform and Multi-Application 5G Fiber-Wireless System

Abstract: This article reports the implementation and experimental performance investigation of a DSP-based flexible-waveform fiber-wireless (FiWi) system for 5G enhanced mobile broadband (eMBB) and new vertical applications. Our radio over fiber-based fronthaul solution uses a wavelength-division-multiplexing passive optical network (WDM-PON) infrastructure, from a commercial Internet service provider, to enable 5G operation over multiple frequency bands, including: a DSP and flexible waveform-based signal at 788 MHz, which can be set as generalized frequency division multiplexing (GFDM) or filtered orthogonal frequency division multiplexing (F-OFDM); 5G new radio (NR) signals at 3.5 or 26 GHz in accordance to 3GPP Release 15; an additional vector 26 GHz signal with bandwidth of up to 800 MHz. The DSP-based functionality provides digital pre-distortion (DPD), besides the real-time waveform generation. Experimental results demonstrate 4.41 Gbit/s total throughput in the air in accordance to the 3GPP requirements, as well as an innovative low-latency M2M application based on PROFINET standard.

One photon-per-bit receiver using near-noiseless phase-sensitive amplification.

Abstract: Space communication for deep-space missions, inter-satellite data transfer and Earth monitoring requires high-speed data connectivity. The reach is fundamentally dictated by the available transmission power, the aperture size, and the receiver sensitivity. A transition from radio-frequency links to optical links is now seriously being considered, as this greatly reduces the channel loss caused by diffraction. A widely studied approach uses power-efficient formats along with nanowire-based photon-counting receivers cooled to a few Kelvins operating at speeds below 1 Gb/s. However, to achieve the multi-Gb/s data rates that will be required in the future, systems relying on pre-amplified receivers together with advanced signal generation and processing techniques from fibre communications are also considered. The sensitivity of such systems is largely determined by the noise figure (NF) of the pre-amplifier, which is theoretically 3 dB for almost all amplifiers. Phase-sensitive optical amplifiers (PSAs) with their uniquely low NF of 0 dB promise to provide the best possible sensitivity for Gb/s-rate long-haul free-space links. Here, we demonstrate a novel approach using a PSA-based receiver in a free-space transmission experiment with an unprecedented bit-error-free, black-box sensitivity of 1 photon-per-information-bit (PPB) at an information rate of 10.5 Gb/s. The system adopts a simple modulation format (quadrature-phase-shift keying, QPSK), standard digital signal processing for signal recovery and forward-error correction and is straightforwardly scalable to higher data rates. Communication links for deep-space exploration spacecraft and satellites could become more efficient using an optical system which can reduce signal losses during transmission and delivers one bit of data per each received photon at a rate of 10 gigabits per second. Peter Andrekson and colleagues at Chalmers University of Technology in Sweden developed the system and demonstrated its potential in laboratory scale experiments. It relies on a technology known as phase-sensitive optical amplification. The researchers transmitted signals across only a one meter, but they believe their work proves the validity of a process that could readily be scaled up for communication across space. Replacing current radio-frequency technology with more effective optical systems could meet the demands of future space communications systems, which will need to operate at higher data rates and across greater distances.

Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications

Abstract: We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN) for communication over dispersive nonlinear channels. In particular, our focus is on the recently proposed sliding window bidirectional RNN (SBRNN) optical fiber auto-encoder. We show that adjusting the processing window in the sequence estimation algorithm at the receiver improves the reach of simple systems trained on a channel model and applied "as is" to the transmission link. Moreover, the collected experimental data was used to optimize the receiver neural network parameters, allowing to transmit 42Gb/s with bit-error rate (BER) below the 6.7% hard-decision forward error correction threshold at distances up to 70 km as well as 84 Gb/s at 20 km. The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization. Our results show that, for fixed algorithm memory, the DSP based on deep learning achieves an improved BER performance, allowing to increase the reach of the system.

Considerations on the Use of Digital Signal Processing in Future Optical Access Networks

Abstract: In this invited article, we address the impacts of the rise of signal processing for increased capacity per wavelength and better receiver sensitivities in next generation optical access networks. We start by recalling the main channel limitations of currently deployed intensity modulated, directly detected, passive optical networks. Then, with the intention of providing a benchmarking of signal processing approaches used in communication systems other than optical access, we provide a historic perspective on how digital signal processing emerged in copper access systems, and we evaluate powerful techniques envisaged for future mobile generation in both air interface and radio access networks. We also assess signal processing in light of multi-vendor interoperability by providing insights on burst mode operation and the needed protocol and monitoring procedures. Interoperability with regard to optical transceivers is also considered. Last but not least, we evaluate power consumption of digital signal processing and forward error correction solutions used in disruptive, coherent transmission approaches.

Parallel Implementation of Model Predictive Control for Multilevel Cascaded H-Bridge STATCOM With Linear Complexity

Abstract: The algorithm complexity of model predictive control (MPC) for cascaded H-bridge (CHB) static synchronous compensators (STATCOMs) was optimized to a polynomial level in previous studies. However, implementing MPC with conventional approaches still suffers from long execution time, where a low cost digital signal processor (DSP) is used to execute optimization algorithms and a small-sized field-programmable gate array (FPGA) is used to extend gate signals and sampling functions. This paper presents a parallel implementation approach to reduce the execution time of MPC, where the FPGA is configured to be used for accelerating the high-complexity sorting tasks. By running the DSP and FPGA in parallel, the algorithm execution time is only determined by the DSP, and the resource consumption is only determined by the FPGA. To minimize the resource consumption and requirement in FPGA, a parallel comparison-based sorter with multiple comparison and accumulators is proposed. The proposed implementation method can reduce the time and space complexity of the MPC from the polynomial level to a linear level without additional hardware cost, which means the MPC for medium-voltage CHB STATCOMs (e.g., 20 stages) can be implemented online with existing control platforms. Experimental results for various CHB STATCOMs are presented to validate the proposed configuration and algorithm.

Design and Implementation of Butterworth, Chebyshev-I and Elliptic Filter for Speech Signal Analysis

Abstract: In the field of digital signal processing, the function of a filter is to remove unwanted parts of the signal such as random noise that is also undesirable. To remove noise from the speech signal transmission or to extract useful parts of the signal such as the components lying within a certain frequency range, filters are necessary. Filters are broadly used in signal processing and communication systems in applications such as channel equalization, noise reduction, radar, audio processing, speech signal processing, video processing, biomedical signal processing that is noisy ECG, EEG, EMG signal filtering, electrical circuit analysis and analysis of economic and financial data. In this paper, three types of infinite impulse response filter i.e. Butterworth, Chebyshev type I and Elliptical filter have been discussed theoretically and experimentally. Butterworth, Chebyshev type I and elliptic low pass, high pass, band pass and band stop filter have been designed in this paper using MATLAB Software. The impulse responses, magnitude responses, phase responses of Butterworth, Chebyshev type I and Elliptical filter for filtering the speech signal have been observed in this paper. Analyzing the Speech signal, its sampling rate and spectrum response have also been found.

Aperture-Level Simultaneous Transmit and Receive With Digital Phased Arrays

Abstract: While purely digital phased arrays were once discarded as simultaneous transmit and receive (STAR) capable platforms, this notion has recently been reconsidered. Previous work demonstrated that adaptive digital beamforming and digital self-interference cancellation (SIC) can enable transmitting and receiving subapertures in an array to operate simultaneously in the same frequency band. This approach, referred to as Aperture-Level Simultaneous Transmit and Receive (ALSTAR), uses only adaptive digital beamforming and digital SIC techniques. The ALSTAR architecture does not require custom radiators or analog canceling circuits that can increase front end losses and add significant size, weight, and cost to the array. This paper extends the previously proposed effective isotropic isolation (EII) metric to account for fixed dynamic range transmit and receive channels. An alternating optimization procedure that exploits the interdependence of the transmit and receive beamformers is proposed based on the symmetry of the EII metric, achieving higher EII than in previous work. This optimization procedure balances the goal of null-placement for interference and noise rejection with the goal of maintaining high transmit and receive gain. Simulated results are presented for a $\mathbf {50}$ -element array that achieves $\mathbf {187.1}$ dB of EII in narrowband operation with $\mathbf {2500}$ W of transmit power. We explore the effectiveness of the architecture and proposed optimization methods by demonstrating the high EII achieved across the full scan space of the array at several transmit power levels. Results are also presented for a regularized version of the beamformer optimization problem that allows the designer to trade EII for array gain.

Cascaded IF-Over-Fiber Links With Hybrid Signal Processing for Analog Mobile Fronthaul

Abstract: We present an intermediate frequency-over-fiber (IFoF) transmission system that uses cascaded connections between a broadband link and multiple narrowband links for future mobile fronthaul (MFH). The downlink MFH system employs analog and digital signal processing after broadband and narrowband IFoF transmissions, respectively, for extractions and frequency conversions of IF signals. The bandwidth of the optical components for the downlink MFH system is investigated and compared with that for the subcarrier multiplexed passive optical network (SCM-PON). The results support the satisfactory performance of the MFH system for bandwidth reduction. Two types of our developed digital signal processors for the MFH system are also described in detail: One is for an output of a single radio frequency (RF) stream, whereas the other is for simultaneous outputs of multiple RF streams. In addition, using either of the digital signal processors, we experimentally demonstrate the downlink MFH system's transmission of 64-QAM filtered OFDM (f-OFDM) signals with 360-MHz signal bandwidth and 3.6-MHz guard bands between adjacent IF signals and 64-QAM OFDM signals with 380.16-MHz signal bandwidth and 19.84-MHz guard bands between IF signals. In both experiments, eighteen IF signals are successfully transmitted over 20-km and 1-km fibers, which includes analog and real-time digital signal processing for IF channel extractions and frequency conversions. The results obtained in this study demonstrate the potential of cascaded IFoF links using hybrid signal processing for 5G and future MFH systems.

ASIC implementation of distributed arithmetic based FIR filter using RNS for high speed DSP systems

Abstract: Digital signal processing (DSP) systems are becoming popular with the emergence of artificial intelligence and machine learning based applications. Residue number system is one of most sought representation for implementing the high speed DSP systems. This paper presents an efficient implementation of memory less distributed arithmetic (MLDA) architecture in finite impulse response filter with residual number system. The input data and filter coefficients of MLDA are in residue number form and the output data from MLDA is converted into binary form using Chinese remainder theorem. In addition, compressor adders are used to reduce the area. For real time validation, the proposed design has been simulated and synthesized in application specific integrated circuit platform using synopsis design compiler with CMOS 90 nm technology. The results show that the proposed design has very high computation speed with total delay of only 20 ns and occupies 20% less area in comparison with the existing designs.

Optics-Simplified DSP for 50 Gb/s PON Downstream Transmission using 10 Gb/s Optical Devices

Abstract: Directly-modulated laser (DML) is widely employed in intensity modulation and direct detection (IMDD) system due to its low cost and high output power. However, the corresponding frequency chirp is regarded as one of the main disadvantages for its application in passive optical networks (PONs). In this paper, we theoretically analyze the frequency response evolution of DML based system under different chirp and dispersion conditions, proving that the system bandwidth can be improved by interactions between negative dispersion and DML chirp. Based on this concept, we experimentally demonstrated downstream 50 Gb/s PAM4 signal transmission over 20 km single-mode fiber (SMF) access based on the 10 Gb/s DML operating at 1310 nm and avalanche photodiode (APD). A dispersion-shifted fiber (DSF) providing −150 ps/nm dispersion at 1310 nm in the optical line terminal (OLT) is used to pre-equalize the frequency response of bandwidth-limited directly modulated signals in the optical domain. Thanks to our proposed dispersion-supported equalization (DSE) technique, the system bandwidth can be improved by 5 GHz. Feed-forward equalization (FFE), decision feedback equalization (DFE) and Volterra filter are employed to evaluate the signal performance improvement, respectively. By evaluating the receiver sensitivity, the DSE combined with FFE scheme shows 2 dB improvement than the complex Volterra algorithm, indicating its potential to reduce the complexity of digital signal processing (DSP) and therefore a lower cost and power consumption in PON.