Showing papers on "Demodulation published in 2021"
TL;DR: This is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information, and it is proved that data augmentation at the test stage can be interpreted as model ensemble.
Abstract: Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method.
100 citations
TL;DR: The object covered by the metasurface is hence perfectly camouflaged to a foe radar and can be detected by a friend radar possessing the spread-spectrum demodulation key corresponding to the metAsurface modulation, and this detection is robust to interfering signals.
Abstract: This article presents the concept of spread-spectrum selective camouflaging based on time-modulated metasurface. The spectrum spreading is realized by switching the metasurface between the reflective states of a perfect electric conductor (PEC) mirror and a perfect magnetic conductor (PMC) mirror, using an array of microstrip patches connected to the ground via diode switches, according to a periodic pseudorandom noise sequence. As the spectrum spreading induces a drastic reduction of the power spectral density of the signal, the level of the scattered wave falls below the noise floor of the interrogating radar, and the object covered by the metasurface is hence perfectly camouflaged to a foe radar. Moreover, the object can be detected by a friend radar possessing the spread-spectrum demodulation key corresponding to the metasurface modulation, and this detection is robust to interfering signals. The proposed system is analyzed theoretically and demonstrated by both simulation and experimental results.
64 citations
TL;DR: A novel multi-task learning (MTL)-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error, showing that the proposed architecture can achieve higher robustness and generalization than the conventional ones.
Abstract: Automatic modulation classification (AMC) is a critical algorithm for the identification of modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Deep learning (DL)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work under the corresponding condition. In this paper, a novel multi-task learning (MTL)-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from knowledge-sharing-based MTL in varying noise scenarios. In detail, multiple CNN models with the same structure are trained for multiple SNR conditions, but they share their knowledge (e.g. model weight) with each other. Thus, MTL can extract the general features from datasets in different noise scenarios. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.
62 citations
TL;DR: This study indicates that the widespread IDI incurs a computational burden for the element-wise detector like the message passing in the state-of-the-art works, and proposes a block-wise OTFS receiver by exploiting the structure and characteristics of the O TFS transmission matrix.
Abstract: Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme realized in the delay-Doppler domain, which targets the robust wireless transmissions in high-mobility environments. In such scenarios, OTFS signal suffers from multipath channel with continuous Doppler spread, which results in significant inter-symbol interference and inter-Doppler interference (IDI). In this article, we analyze the interference generation mechanism, and compare statistical distributions of the IDI in two typical cases, i.e., limited-Doppler-shift channel and continuous-Doppler-spread channel (CoDSC). Focusing on the OTFS signal transmission over the CoDSC, our study firstly indicates that the widespread IDI incurs a computational burden for the element-wise detector like the message passing in the state-of-the-art works. Addressing this challenge, we propose a block-wise OTFS receiver by exploiting the structure and characteristics of the OTFS transmission matrix. In the receiver, we deliberately design an iteration strategy among the least squares minimum residual based channel equalizer, reliability-based symbol detector and interference eliminator, which can realize fast convergence by leveraging the sparsity of channel matrix. The simulations demonstrate that, in the CoDSC, the proposed scheme achieves much less detection error, and meanwhile reduces the computational complexity by an order of magnitude, compared with the state-of-the-art OTFS receivers.
54 citations
TL;DR: This paper proposes a novel OTFS based multi-user precoder at the base station and a corresponding low complexity detector (LCD) at the user terminals (UTs), which allows for separate demodulation of each DD domain information symbol at the UT.
Abstract: We consider the problem of degradation in performance of multi-carrier multi-user massive MIMO systems when channel induced Doppler spread is high. Recently, Orthogonal Time Frequency Space (OTFS) modulation has been shown to be robust to channel induced Doppler spread. In OTFS based systems, information symbols are embedded in the delay-Doppler (DD) domain where they are jointly modulated to generate the time-domain transmit signal. Due to the multi-path delay and Doppler shifts, the DD domain information symbols need to be jointly demodulated at the receiver. For multi-carrier based communication (e.g., Orthogonal Frequency Division Multiplexing (OFDM)), massive MIMO systems have been shown to achieve high spectral and energy efficiency with low complexity multi-user precoding in the downlink. Extending the same to OTFS based downlink multi-user massive MIMO systems is challenging due to the requirement for joint demodulation of all information symbols at the user terminal (UT). In this paper, we solve this problem by proposing a novel OTFS based multi-user precoder at the base station (BS) and a corresponding low complexity detector (LCD) at the user terminals (UTs), which allows for separate demodulation of each DD domain information symbol at the UT. The complexity of the proposed precoder increases only linearly with increasing number of BS antennas $Q$ and the number of UTs. We show, through analysis, that as $Q$ increases (with total transmitted power decreased linearly with $Q$ ), the proposed low complexity detector achieves a sum spectral efficiency close to that achieved with optimal joint demodulation at each UT. Numerical simulations confirm our analysis and also show that the spectral efficiency and error rate performance of the proposed OTFS based massive MIMO precoder (with the proposed LCD detector at each UT) is significantly more robust to channel induced Doppler spread when compared to OFDM based multi-user massive MIMO systems.
53 citations
15 Nov 2021
TL;DR: NELoRa as mentioned in this paper is a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication.
Abstract: Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. However, the standard demodulation method does not fully exploit the properties of chirp signals, thus yields a sub-optimal SNR threshold under which the decoding fails. Consequently, the communication range and energy consumption have to be compromised for robust transmission. This paper presents NELoRa, a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication. Taking the spectrogram of both amplitude and phase as input, we first design a mask-enabled Deep Neural Network (DNN) filter that extracts multi-dimension features to capture clean chirp symbols. Second, we develop a spectrogram-based DNN decoder to decode these chirp symbols accurately. Finally, we propose a generic packet demodulation system by incorporating a method that generates high-quality chirp symbols from received signals. We implement and evaluate NELoRa on both indoor and campus-scale outdoor testbeds. The results show that NELoRa achieves 1.84-2.35 dB SNR gains and extends the battery life up to 272% (~0.38-1.51 years) in average for various LoRa configurations.
48 citations
21 May 2021
TL;DR: Remote non-contact monitoring of human vital signs has recently received lots of attention due to the advancement and availability of millimeter wave (mmWave) radars, and MIMO configuration can be used to improve the SNR level by taking advantage of its channel diversity.
Abstract: Remote non-contact monitoring of human vital signs has recently received lots of attention due to the advancement and availability of millimeter wave (mmWave) radars. These sensors are significantly reduced in size, but still face serious electromagnetic (EM) propagation loss and signal obstructions resulting in lower signal-to-noise ratios (SNR). As mmWave received signals also have higher sensitivity to body motions, these effects typically degrade the accuracy of heart rate (HR) detection. To overcome this challenge, MIMO configuration can be used to improve the SNR level by taking advantage of its channel diversity. We use here a Frequency Modulated Continuous Wave (FMCW) radar from Texas Instruments (TI) at 77 GHz to collect data from 192 channels. Additionally, vital sign information is extracted using Arctangent Demodulation (AD) and Maximal Ratio Combining (MRC) combined with an adapted-wavelet Continuous Wavelet Transform (CWT) are utilized to demonstrate improvement of HR estimation accuracy.
43 citations
TL;DR: In this paper, the authors focus on modulation design for molecular communication via diffusion systems, where chemical signals are transported using diffusion, possibly assisted by flow, from the transmitter to the receiver, and the primary challenges in designing these systems are how to encode and modulate information onto chemical signals and how to design a receiver that can detect and decode the information from the corrupted chemical signal observed at the destination.
Abstract: This survey paper focuses on modulation aspects of molecular communication, an emerging field focused on building biologically-inspired systems that embed data within chemical signals. The primary challenges in designing these systems are how to encode and modulate information onto chemical signals, and how to design a receiver that can detect and decode the information from the corrupted chemical signal observed at the destination. In this article, we focus on modulation design for molecular communication via diffusion systems. In these systems, chemical signals are transported using diffusion, possibly assisted by flow, from the transmitter to the receiver. This tutorial presents recent advancements in modulation and demodulation schemes for molecular communication via diffusion. We compare five different modulation types: concentration-based, type-based, timing-based, spatial, and higher-order modulation techniques. The end-to-end system designs for each modulation scheme are presented. In addition, the key metrics used in the literature to evaluate the performance of these techniques are also presented. Finally, we provide a numerical bit error rate comparison of prominent modulation techniques using analytical models. We close the tutorial with a discussion of key open issues and future research directions for design of molecular communication via diffusion systems.
40 citations
TL;DR: A robust three-phase grid synchronization technique has been proposed for rapid detection of fundamental frequency, phase, and amplitude and a novel two consecutive samples based frequency estimator is developed for fast detection of the fundamental frequency.
Abstract: In this article, a robust three-phase grid synchronization technique has been proposed for rapid detection of fundamental frequency, phase, and amplitude. The widely accepted phase locked-loop (PLL) algorithms possess complex architectures and require tedious tuning process for attaining a good stability margin. In order to surpass the shortfalls of PLL algorithms, a computationally efficient, stable, and open-loop scheme has been reported in this article. A novel two consecutive samples based frequency estimator is developed for fast detection of the fundamental frequency. Moreover, an efficient hybrid prefiltering approach is implemented based on the demodulation of the grid voltage signal. Additionally, the combination of a delayed signal cancellation operator and a band-pass filter allowed rapid rejection of dc-offset and harmonics, respectively. In the event of a grid voltage imbalance, the instantaneous symmetrical component method is a rescuer for the rejection of the fundamental negative sequence component without any delay. Subsequently, overall transient response time of the scheme is observed to be improved. On the other hand, the fundamental positive sequence component facilitates the estimation of amplitude and phase angle information. Importantly, the dynamic performance of the proposed scheme has been experimentally validated in presence of various grid disturbances.
35 citations
TL;DR: The proposed improved frequency band selection method called maximum envelope based-Autogram (MEAutogram) can reduce the calculation amount through overcoming the influence of irrelevant components on the segmentation position, which can be adaptively determined according to the characteristics of signal.
32 citations
TL;DR: In this article, a single valued neutrosophic entropy based adaptive sensitive frequency band selection for the purpose of identifying defective components in an axial pump was proposed. But the proposed methodology is applied in the following steps: first, VMD is applied for decomposing vibration signals into various frequency bands, called as modes.
TL;DR: In this paper, the authors proposed orthogonal time sequency multiplexing (OTSM), a single carrier modulation scheme that places information symbols in the delay-sequency domain.
Abstract: This paper proposes orthogonal time sequency multiplexing (OTSM), a novel single carrier modulation scheme that places information symbols in the delay-sequency domain followed by a cascade of time-division multiplexing (TDM) and Walsh-Hadamard sequence multiplexing. Thanks to the Walsh Hadamard transform (WHT), the modulation and demodulation do not require complex domain multiplications. For the proposed OTSM, we first derive the input-output relation in the delay-sequency domain and present a low complexity detection method taking advantage of zero-padding. We demonstrate via simulations that OTSM offers high performance gains over orthogonal frequency division multiplexing (OFDM) and similar performance to orthogonal time frequency space (OTFS), but at lower complexity owing to WHT. Then we propose a low complexity time-domain channel estimation method. Finally, we show how to include an outer error control code and a turbo decoder to improve error performance of the coded system.
TL;DR: In this article, an atom-based receiver for AM and FM microwave communication with a 3 dB bandwidth in the baseband of ~100 kHz was demonstrated, which provides optical circuit-free field pickup, multiband carrier capability, and inherently high field sensitivity.
Abstract: Radio reception relies on antennas for the collection of electromagnetic fields carrying information, and receiver elements for demodulation and retrieval of the transmitted information. Here, we demonstrate an atom-based receiver for AM and FM microwave communication with a 3 dB bandwidth in the baseband of ~100 kHz that provides optical circuit-free field pickup, multiband carrier capability, and inherently high field sensitivity. The atom-based receiver exploits field-sensitive cesium Rydberg vapors in a centimeter-sized glass cell, and electromagnetically induced transparency, a quantum-optical effect, as a readout of baseband signals modulated onto carriers with frequencies ranging over four octaves, from $C$ -band to $Q$ -band. Receiver bandwidth, dynamic range and sideband suppression are characterized, and acquisition of audio waveforms of human vocals demonstrated. The atomic receiver is a valuable receiver technology because it does not require antenna structures and is resilient against electromagnetic interference, while affording multiband operation in a single compact receiving element.
TL;DR: In this paper, the authors proposed an algorithm to compensate the phase noise in OFDM receivers in mm-wave ARoF systems for 5G and showed the effectiveness of the proposed algorithm under those conditions.
Abstract: Fifth-generation mobile networks (5G) are the solution for the demanding mobile traffic requirements, providing technologies that fulfill the requisites of different type of services. The utilization of the millimeter-wave (mm-wave) band is the straightforward technique to achieve high bit rates. Moreover, analog radio-over-fiber (ARoF) brings outstanding benefits such as low cost, low power consumption, and high spectral efficiency, among others. Thereby, mm-wave ARoF is a strong candidate to pave the way for common public radio interface (CPRI) in the fronthaul for the future 5G architecture. As orthogonal frequency-division multiplexing (OFDM) is the adopted waveform in the 5G standard, it should be also utilized in mm-wave ARoF systems for 5G. However, phase noise is one of the most degrading factors in mm-wave OFDM ARoF systems. Therefore, in this work, an analysis of the phase noise is carried out through an experimental setup up. The configuration of this setup enables to gradually modify the final phase noise level of the system. Furthermore, an original and novel algorithm to compensate the phase noise in OFDM receivers is proposed. The performance of this algorithm is experimentally evaluated through the setup for different phase noise levels and different subcarrier spacings. The obtained results show the effectiveness of the proposed algorithm under those conditions, highlighting the viability of mm-wave OFDM ARoF for 5G and beyond.
TL;DR: A corner-inserted pilot pattern is proposed, which targets the low pilot overhead and satisfactory CE performance and an OTFS signal detector, leveraging the time-domain channel equalization, linear-complexity interference cancellation and delay-Doppler domain maximal ratio combining detection, is developed to retrieve the transmitted data symbols.
Abstract: Orthogonal time frequency space (OTFS) has shown to be a promising modulation technology that achieves the robust wireless transmission in high-mobility environments. The high mobility incurred Doppler effect in OTFS system, is represented as a continuous and relatively large band in the Doppler frequency. It yields the equivalent channel responses (ECRs) in the system change significantly within one symbol block, posing a challenge to channel estimation (CE) or tracking. In order to tackle this issue, in this paper, a set of transform-domain basis functions is designed to span a low-dimensional subspace for modeling the OTFS channel. Then, the CE can be performed by estimating a few projection coefficients of ECRs in the developed subspace, with training pilots. According to the individual transmission characteristic of OTFS signal, we propose a corner-inserted pilot pattern, which targets the low pilot overhead and satisfactory CE performance. Moreover, an OTFS signal detector, leveraging the time-domain channel equalization, linear-complexity interference cancellation and delay-Doppler domain maximal ratio combining detection, is developed to retrieve the transmitted data symbols. The simulations show the precisely estimated ECRs enable the detector to ideally demodulate 256-ary quadrature amplitude modulation signaling, under a velocity of 550 km/h at 5.9 GHz carrier frequency.
TL;DR: The effectiveness and efficiency of the proposed SEC method are verified on an IoT platform compared with a conventional method, and its advantages include high output SNR, low power consumption, and compatibility with edge computing.
Abstract: Continuous condition monitoring and fault diagnosis of motor bearings are vital to guarantee motor safety operation and reduce breakdown losses. With numerous Internet of things (IoT) sensors being installed on motors for condition monitoring, data transmission and storage problems have become new challenges. This study designed a signal enhancement and compression (SEC) method and implemented on an IoT platform for motor bearing fault diagnosis. First, vibration signal is acquired from an accelerometer installed on the motor. The bearing signal is demodulated using an online demodulation algorithm. Second, an envelope signal is downsampled and enhanced using a stochastic resonance-based nonlinear filter. The enhanced signal is compressed using an Opus encoder and transmitted to a receiver. Lastly, the received signal is decompressed using the Opus decoder, and the bearing fault type can be recognized. The effectiveness and efficiency of the proposed SEC method are verified on an IoT platform compared with a conventional method. The proposed method improves 3.83 dB of the average signal-to-noise ratio (SNR), and reduces 94.7% of the total time and 94.6% of the dissipative power. The advantages of the proposed SEC method include high output SNR, low power consumption, and compatibility with edge computing. These advantages show potential applications in remote motor fault diagnosis using battery power supply.
TL;DR: The signal modulation is realized by tuning the turn-off angle of the SRM to produce voltage ripples on the power transmission line, whereas autoregressive power spectrum density method is innovatively utilized to demodulate useful data from the powerline.
Abstract: Powerline communication is one of the key technologies contributing to grid intelligentization and needs to be specially designed for individual converter-based generators and loads. In this article, a novel power and signal composite modulation (PSCM) strategy is proposed for powerline data transmission in switched reluctance motor (SRM)-based distributed power grids. In detail, the signal modulation is realized by tuning the turn- off angle of the SRM to produce voltage ripples on the power transmission line, whereas autoregressive power spectrum density method is innovatively utilized to demodulate useful data from the powerline. Hardware experimentation is conducted in this article, which verifies the effectiveness of the proposed PSCM method.
TL;DR: The analysis results prove that the Enkurgram has satisfactory demodulation capability in dealing with signals under low Signal-to-Noise Ratio levels or with non-Gaussian noise, which is the Achilles’ heel of SK-based methods.
TL;DR: Simulations reveal that even in very high mobility scenarios, the SE and symbol error rate performance of the alternate conversion is invariant of Doppler shift and is significantly better than the performance achieved with two-step conversion.
Abstract: In Orthogonal Time Frequency Space (OTFS) modulation, information symbols are embedded in the delay-Doppler (DD) domain instead of the time-frequency (TF) domain. In order to ensure compatibility with existing OFDM systems, most prior work on OTFS receivers consider a two-step conversion, where the received time-domain (TD) signal is firstly converted to a time-frequency (TF) signal (using an OFDM demodulator) followed by post-processing of this TF signal into a DD domain signal. In this paper, we show that the spectral efficiency (SE) performance of a two-step conversion based receiver degrades in very high mobility scenarios where the Doppler shift is a significant fraction of the communication bandwidth (e.g., control and non-payload communication (CNPC) in Unmanned Aircraft Systems (UAS)). We therefore consider an alternate conversion, where the received TD signal is directly converted to the DD domain. The resulting received DD domain signal is shown to be not the same as that obtained in the two-step conversion considered in prior works. The alternate conversion does not require an OFDM demodulator and is shown to have lower complexity than the two-step conversion. Simulations reveal that even in very high mobility scenarios, the SE and symbol error rate performance of the alternate conversion is invariant of Doppler shift and is significantly better than the performance achieved with two-step conversion (which degrades with increasing Doppler shift).
TL;DR: In this article, the authors proposed LoRaSyNc (LoRa receiver with SyNchronization and Cancellation), a second generation LoRa receiver that implements simultaneous interference cancellation and time synchronization to improve the performance of LoRa gateways.
Abstract: In this paper we propose LoRaSyNc (LoRa receiver with SyNchronization and Cancellation), a second generation LoRa receiver that implements Successive Interference Cancellation (SIC) and time synchronization to improve the performance of LoRa gateways. Indeed, the chirp spread spectrum modulation employed in LoRa experiences very high capture probability, and cancelling the strongest signal in case of collisions can significantly improve the cell capacity. An important feature of LoRaSyNc is the ability to track the frequency and clock drifts between the transmitter and receiver, during the whole demodulation of the interfered frame. Due to the use of low-cost oscillators on end-devices, a signal cancellation scheme cannot result accurate without such a tracking, especially at the lower data rates. We validate the performance of LoRaSyNc in presence of collisions by implementing a receiver prototype on software-defined-radios, and perform several experiments in different realistic scenarios, by also comparing our receiver with commercial gateways. Finally, we simulate a cell deployment with one or more gateways, showing that the proposed scheme improves performance by almost 50% compared to a traditional receiver.
TL;DR: This paper proposes to diagnose planetary gearbox faults from three complementary analysis results of the PMSG stator current, i.e., Fourier spectrum, amplitude demodulated spectrum and frequency demodulation spectrum, derived through mechanical-magnetic-electric interaction analysis.
TL;DR: A new transmission scheme, called OTFS with dual-mode index modulation (OTFS-DM-IM), is proposed to balance the transmission reliability and spectral efficiency, and a modified log-likelihood ratio (LLR) detector based on the minimum Hamming distance is designed for demodulation.
Abstract: Orthogonal time frequency space (OTFS) modulation has been proved to have better bit error rate (BER) performance over orthogonal frequency division multiplexing (OFDM) under high-mobility conditions. In this letter, a new transmission scheme, called OTFS with dual-mode index modulation (OTFS-DM-IM), is proposed to balance the transmission reliability and spectral efficiency. Moreover, a modified log-likelihood ratio (LLR) detector based on the minimum Hamming distance is designed for demodulation. Then, the theoretical BER analysis of the proposed OTFS-DM-IM is presented. Simulation results highlight the performance advantage of OTFS-DM-IM over classical OTFS and the existing OTFS systems based on index modulation (OTFS-IM), and also confirm the superiority of the modified LLR detector compared to maximum likelihood detector and conventional LLR detector.
TL;DR: In this paper, the intersection of the upper and lower envelopes of gold-coated TFBG spectra was used to detect HER2 (Human Epidermal Growth Factor Receptor-2) proteins.
Abstract: Gold-coated tilted fiber Bragg gratings (TFBGs) have been highly studied over the last years, mainly for biosensing purposes. They present a comb-like spectrum of narrow-band cladding mode resonances that is often demodulated by tracking the change of a selected peak. In this paper, we report a twentyfold more sensitive demodulation method based on the intersection of the upper and lower envelopes of gold-coated TFBG spectra. This method has been successfully applied in biosensing experiments towards the detection of HER2 (Human Epidermal Growth Factor Receptor-2) proteins, a relevant biomarker for breast cancer. Practical improvements have also been implemented. First, a uniform FBG has been superimposed on the TFBG to reduce the read-out wavelength span to 10 nm instead of 70 nm while keeping the temperature-compensated measurements. Second, a micro-fluidic system has been implemented to smoothly deliver the samples to the sensor. These 3 originalities make this sensing platform even more attractive for use in practical applications.
TL;DR: This paper proposes a deep learning (DL)-aided multicarrier system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively.
Abstract: This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity.
TL;DR: In this article, a multi-peak detection deep learning model based on a dilated convolutional neural network (CNN) was proposed for signal demodulation in quasi-distributed fiber Bragg grating (FBG) sensor networks.
Abstract: In quasi-distributed fiber Bragg grating (FBG) sensor networks, challenges are known to arise when signals are highly overlapped and thus hard to separate, giving rise to substantial error in signal demodulation. We propose a multi-peak detection deep learning model based on a dilated convolutional neural network (CNN) that overcomes this problem, achieving extremely low error in signal demodulation even for highly overlapped signals. We show that our FBG demodulation scheme enhances the network multiplexing capability, detection accuracy and detection time of the FBG sensor network, achieving a root-mean-square (RMS) error in peak wavelength determination of < 0.05 pm, with a demodulation time of 15 ms for two signals. Our demodulation scheme is also robust against noise, achieving an RMS error of < 0.47 pm even with a signal-to-noise ratio as low as 15 dB. A comparison on our high-performance computer with existing signal demodulation methods shows the superiority in RMS error of our dilated CNN implementation. Our findings pave the way to faster and more accurate signal demodulation methods, and testify to the substantial promise of neural network algorithms in signal demodulation problems.
TL;DR: In this article, the authors proposed a phase generated carrier (PGC) demodulation method by simultaneously calculating the carrier phase delay and the phase modulation depth (m) to compensate the nonlinear error introduced by θ and m.
Abstract: We propose a novel phase generated carrier (PGC) demodulation method by simultaneously calculating the carrier phase delay ( θ ) and the phase modulation depth ( m ) to compensate the nonlinear error introduced by θ and m . Firstly, θ is calculated by adopting the fundamental in-phase and quadrature harmonic components and their differential components. Secondly, this calculated θ is used to set the phases of the harmonic components of reference carrier signal to obtain three new harmonic components independent of θ . Later, m is calculated and compensated by adopting the three new harmonic components and their differential components to obtain the demodulated result. Theoretical analysis and realization of the method are described in detail. Simulation and displacement measurement experiments with different θ and m are carried out to validate the proposed method. The experimental results demonstrate that the proposed method can accurately calculate and compensate θ and m , and effectively eliminate the nonlinear error of the demodulated signal.
TL;DR: The proposed convolutional neural network (CNN) based demodulator for NOMA-VLC can effectively compensate for both linear and nonlinear distortions, thus achieving improved bit error ratio performance compared with the successive interference cancellation and joint detection based receivers.
Abstract: Non-orthogonal multiple access (NOMA) is a promising scheme to improve the spectral efficiency, user fairness, and overall throughput in visible light communication (VLC) systems However, the error propagation problem together with linear and nonlinear distortions induced by multipath, limited modulation bandwidth, and nonlinearity of light emitting diode significantly limit the transmission performance of NOMA-VLC systems In addition, having an accurate channel state information, which is important in the recovery of NOMA signal, in mobile wireless VLC is challenging In this work, we propose a convolutional neural network (CNN) based demodulator for NOMA-VLC, in which signal compensation and recovery are jointly realized Both simulation and experiment results show that, the proposed CNN based demodulator can effectively compensate for both linear and nonlinear distortions, thus achieving improved bit error ratio performance compared with the successive interference cancellation and joint detection based receivers Compared to SIC, the performance gains are 19, 27, and 27 dB for User1 for the power allocation ratios (PARs) of 016, 025, and 036, respectively, which are 4, 4 and 26 dB for User2 for PARs of 016, 025, and 036, respectively
TL;DR: This article overcomes the problem of high-order low-pass filters with high cutoff frequency by using double demodulation without recreating the double frequency component for rejection purposes, which reduces the computational complexity significantly.
Abstract: This article presents an enhanced frequency adaptive demodulation technique for grid-synchronization of grid-connected converters in variable frequency condition. Demodulation works by generating demodulated voltages which contain undesired double frequency components. As a result, high-order low-pass filters with high cutoff frequency are required to eliminate the undesired components. This reduces the dynamic performance. Frequency adaptive demodulation technique enhances the dynamic performance by rejecting the double frequency components as opposed to filtering, however, at the cost of additional computational complexity. This article overcomes this problem by using double demodulation without recreating the double frequency component for rejection purposes. This reduces the computational complexity significantly. Suitability of the proposed method is verified through numerical simulation and experimental study. Comparative study with existing frequency adaptive demodulation and second-order generalized integrator phase-locked loop techniques demonstrate the validity and performance improvement by the proposed technique.
TL;DR: In this paper, a weighted envelope spectrum (WES) is introduced by integrating the spectral correlation over the full spectral frequency band and assigning the new weighting vector on each spectral frequency.
Abstract: The key idea behind demodulation analysis for bearing diagnosis is to determine the fault-induced frequency band and directly detect the potential bearing fault characteristic frequency (FCF) in the demodulated spectrum. Till now, most demodulation methods are based on the optimal selection of only one informative frequency band. However, the unwanted in-band noise will be retained or some fault information may be ignored in the case of the discrete resonant frequency band or multiple informative frequency bands. To address the issue, a FCF-oriented criterion is proposed to determine all the informative frequency bands rather than only one specified frequency band. A new weighting vector is obtained to control the contribution of each spectral frequency in the demodulated spectrum. Subsequently, a weighted envelope spectrum (WES) is introduced by integrating the spectral correlation over the full spectral frequency band and assigning the new weighting vector on each spectral frequency. In this way, all frequency components with fault information are enhanced while other components are inhibited. Furthermore, expanded to the diagnosis of compound-fault, the FCF-oriented criterion can provide the different weighting vectors relevant to the different potential faults, and the separated fault features can be identified directly in the generated WESs. Finally, the advantages of WES over the traditional methods are testified by the simulated signal and experimental data.
TL;DR: A phase-shifted demodulation technique with a 3×3 coupler and ellipse fitting algorithm (EFA) for the interrogation of interferometric sensors is proposed and demonstrates high accuracy and stability for measuring small phase signals.
Abstract: A phase-shifted demodulation technique with a 3×3 coupler and ellipse fitting algorithm (EFA) for the interrogation of interferometric sensors is proposed. To reduce the error of the EFA as to measure small phase signals, additional phase modulation is introduced. The additional modulation provides a walk of the operating point along the Lissajous ellipse large enough to permit calculation of the ellipse parameters at every moment. Experimental result shows that this technique demonstrates high accuracy and stability for measuring small phase signals. The setting of this technology expands the application of the EFA in fiber-optic phase demodulation technology.