Showing papers on "Demodulation published in 2019"

Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios

Abstract: Automatic modulation recognition (AMR) is an essential and challenging topic in the development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation and demodulation capabilities to sense and learn environments and make corresponding adjustments. AMR is essentially a classification problem, and deep learning achieves outstanding performances in various classification tasks. So, this paper proposes a deep learning-based method, combined with two convolutional neural networks (CNNs) trained on different datasets, to achieve higher accuracy AMR. A CNN is trained on samples composed of in-phase and quadrature component signals, otherwise known as in-phase and quadrature samples, to distinguish modulation modes, that are relatively easy to identify. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. A CNN based on constellation diagrams is also designed to recognize modulation modes that are difficult to distinguish in the former CNN, such as 16 quadratic-amplitude modulation (QAM) and 64 QAM, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio.

Distributed optical fiber sensing: Review and perspective

Abstract: Over the past few decades, optical fibers have been widely deployed to implement various applications in high-speed long-distance telecommunication, optical imaging, ultrafast lasers, and optical sensors. Distributed optical fiber sensors characterized by spatially resolved measurements along a single continuous strand of optical fiber have undergone significant improvements in underlying technologies and application scenarios, representing the highest state of the art in optical sensing. This work is focused on a review of three types of distributed optical fiber sensors which are based on Rayleigh, Brillouin, and Raman scattering, and use various demodulation schemes, including optical time-domain reflectometry, optical frequency-domain reflectometry, and related schemes. Recent developments of various distributed optical fiber sensors to provide simultaneous measurements of multiple parameters are analyzed based on their sensing performance, revealing an inherent trade-off between performance parameters such as sensing range, spatial resolution, and sensing resolution. This review highlights the latest progress in distributed optical fiber sensors with an emphasis on energy applications such as energy infrastructure monitoring, power generation system monitoring, oil and gas pipeline monitoring, and geothermal process monitoring. This review aims to clarify challenges and limitations of distributed optical fiber sensors with the goal of providing a pathway to push the limits in distributed optical fiber sensing for practical applications.

Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis

Abstract: High-resolution time-frequency representation (TFR) method is effective for signal analysis and feature detection. However, for variable speed bearing vibration signal, conventional TFR method is prone to blur and affect the accuracy of the instantaneous frequency estimation. Moreover, the traditional order tracking, relying on equi-angular resampling, usually suffers from interpolation error. To solve such problems, we propose a joint time-frequency (TF) squeezing method and generalized demodulation (GD) to realize variable speed bearing fault diagnosis. The method can represent the time-varying fault characteristic frequency precisely and be free from resampling. First, using fast spectral kurtosis to select the optimal-frequency band which is sensitive to rolling bearing fault, and extracting envelope by Hilbert transform within the selected optimal frequency band. Next, a high-quality TF clustering method based on short-time Fourier transform is applied to the TF analysis of the envelope to get a clear TFR, from which the frequency information for GD is obtained. Finally, processing the basic demodulator via the peak search through the TF analysis results in the TFR for GD to gain a resampling-free-order spectrum. Based on the more precise TF information from the clearer TFR, the bearing fault can be diagnosed via GD without tachometer or any resampling involved, avoiding the amplitude error and low computational efficiency of resampling. Simulation study and experimental signal analysis validate that the proposed method has better performance than those methods based on conventional TF analysis and resampling.

Low complexity LMMSE Receiver for OTFS

Abstract: Orthogonal time frequency space modulation is a two dimensional (2D) delay-Doppler domain waveform. It uses inverse symplectic Fourier transform (ISFFT) to spread the signal in time-frequency domain. To extract diversity gain from 2D spreaded signal, advanced receivers are required. In this work, we investigate a low complexity linear minimum mean square error receiver which exploits sparsity and quasi-banded structure of matrices involved in the demodulation process which results in a log-linear order of complexity without any performance degradation of BER.

Interleaved Chirp Spreading LoRa-Based Modulation

Abstract: LoRa has established itself as one of the leading technologies within evolving low power wide area networks. In the patented LoRa modulation, a linearly increasing basis chirp signal spans the LoRa bandwidth. Cyclic shifts of this basis chirp signal create a multidimensional space for the orthogonal signaling of nonbinary LoRa symbols. The number of bits per LoRa symbol as well as the symbol rate depend on an applied spreading factor (SF) that could vary between 7 and 12. In this paper, interleaved chirp spreading LoRa (ICS-LoRa) is proposed as a physical layer-inspired approach to enhance data rates of the capacity-limited LoRa networks. In ICS-LoRa, interleaved versions of the nominal LoRa chirp signals constitute a new multidimensional space with the purpose of adding one extra bit within each transmitted ICS-LoRa symbol. The proposed interleaving pattern of ICS-LoRa maintains the same communication robustness of nominal LoRa. The ICS interleaving pattern is also designed to simplify implementation where both ICS-LoRa modulation and demodulation can share the same ICS-interleaver block. An accurate approximation for BER performance of the proposed ICS-LoRa is derived in order to evaluate the underlying reception sensitivities. It is shown that the 14% capacity gain of ICS-LoRa with an SF of 7 is associated with a sensitivity loss at the scale of only 0.8 dB. On the other hand, ICS-LoRa achieves more than 8% in capacity gain with virtually no impact on sensitivity performance when using an SF of 12 that governs the maximum coverage range.

Low Complexity LMMSE Receiver for OTFS

Abstract: Orthogonal time frequency space modulation is a two dimensional (2D) delay-Doppler domain waveform. It uses inverse symplectic Fourier transform (ISFFT) to spread the signal in time-frequency domain. To extract diversity gain from 2D spreaded signal, advanced receivers are required. In this work, we investigate a low complexity linear minimum mean square error receiver which exploits sparsity and quasi-banded structure of matrices involved in the demodulation process which results in a log-linear order of complexity without any performance degradation of BER.

Lora Digital Receiver Analysis and Implementation

Abstract: Low power wide area network technologies (LPWANs) are attracting attention because they fulfill the need for long range low power communication for the Internet of Things. LoRa is one of the proprietary LPWAN physical layer (PHY) technologies, which provides variable data-rate and long range by using chirp spread spectrum modulation. This paper describes the basic LoRa PHY receiver algorithms and studies their performance. The LoRa PHY is first introduced and different demodulation schemes are proposed. The effect of carrier frequency offset and sampling frequency offset are then modeled and corresponding compensation methods are proposed. Finally, a software-defined radio implementation for the LoRa transceiver is briefly presented.

Spatial Modulation for Molecular Communication

Abstract: In this paper, we propose an energy-efficient spatial modulation-based molecular communication (SM-MC) scheme, in which a transmitted symbol is composed of two parts, i.e., a space derived symbol and a concentration derived symbol. The space symbol is transmitted by embedding the information into the index of a single activated transmitter nanomachine. The concentration symbol is drawn according to the conventional concentration shift keying (CSK) constellation. Benefiting from a single active transmitter during each symbol period, SM-MC can avoid the inter-link interference problem existing in the current multiple-input multiple-output (MIMO) based MC schemes, which hence enables low-complexity symbol detection and performance improvement. Correspondingly, we propose a low-complexity scheme, which first detects the space symbol by energy comparison, and then detects the concentration symbol by the maximum ratio combining assisted CSK demodulation. In this paper, we analyze the symbol error rate (SER) of the SM-MC and of its special case, namely the space shift keying-based MC (SSK-MC), where only space symbol is transmitted and no CSK modulation is invoked. Finally, the analytical results are validated by computer simulations. Our studies demonstrate that both the SSK-MC and SM-MC are capable of achieving better SER performance than the conventional MIMO-MC and single-input single-output-based MC, when given the same symbol rate.

Efficient Design of Chirp Spread Spectrum Modulation for Low-Power Wide-Area Networks

Abstract: LoRa is an abbreviation for low power and long range and it refers to a communication technology developed for low-power wide-area networks (LPWANs). Based on the principle of chirp spread spectrum (CSS), LoRa technology is very attractive to provide low bit-rate wireless connections over an extended communication range and under very low power consumption. While the medium access control (MAC) layer of LoRa specifications is open for developers, the physical layer is not. In particular, LoRa modulation and demodulation techniques are patented by Semtech and have not been mathematically described in detail. This paper presents novel approaches to modulate and demodulate LoRa signals with very high implementation efficiency, great flexibility, and excellent performance. In particular, compared to the commercially available receiver made by Semtech, the proposed design is shown to yield a saving of transmitted power from 0.9 to 2.5 dB over the spreading factor (SF) range of 6–12. Moreover, this paper suggests a method to exploit the phase information of CSS signals to encode extra information bits, leading to throughput improvement over the conventional CSS system, for example, by 33%, 25%, 20%, and 17% for SFs of 6, 8, 10, and 12, respectively.

Induction Motor Stator Current AM-FM Model and Demodulation Analysis for Planetary Gearbox Fault Diagnosis

Abstract: Induction motor-planetary gearbox drivetrains are widely used for industrial productions, including machine tools in manufacturing systems. For fault diagnosis of planetary gearboxes in such electromechanical systems, motor current signal analysis provides an effective alternative approach, because motor current signals have easier accessibility and are free from time-varying transfer path effects. Planetary gearbox faults generate load torque oscillations, leading to both amplitude modulation and frequency modulation (AM-FM) effects on induction motor current signals. To thoroughly understand gear fault features in current signals, an AM-FM current signal model is derived through mechanical–magnetic–electric interaction analysis, explicit equation of Fourier spectrum is derived, and sidebands characteristics are summarized. To avoid an intricate sideband analysis, amplitude and frequency demodulation analyses are proposed, explicit equations of corresponding demodulated spectra are derived, and gear fault features are summarized. The theoretical derivations are validated through lab experiments. Localized fault on the sun, planet, and ring gears are all successfully diagnosed using the proposed method.

Analysis, Design, and Implementation of a Wireless Power and Data Transmission System Using Capacitive Coupling and Double-Sided LCC Compensation Topology

Abstract: Wireless power and data transmission (WPDT) is required in many scenarios. This paper proposes a novel WPDT scheme based on capacitive coupling. The power transfer is analyzed, and the power circuit is equivalent to a double-sided LCC compensated wireless power transfer system. The modulation, injection, extraction, and demodulation circuits for data transfer are introduced. The function of each element in the proposed system is explained in detail. In order to facilitate the design of a practical WPDT system, the specific steps are summarized. Theoretical analysis on the interferences between the power transfer and data transfer indicates that the interferences can be significantly diminished by proper design. A 100-W WPDT prototype is built. The measured power transfer efficiency, from dc input to dc output, is 90.5%. The transferred data are correctly recovered in the receiver side at a data rate of 119 kb/s. The data circuit works well even though the coupling coefficient is decreased by 60.2%. The practically measured interferences between the power transfer and data transfer are quite small. The power transfer and data transfer are not affected by these interferences.

Real-time self-calibration PGC-Arctan demodulation algorithm in fiber-optic interferometric sensors.

Abstract: Fiber-optic interferometric sensors (FOISs) are widely used in seismometers, hydrophones, and gyroscopes. The arctangent approach of phase-generated carrier (PGC-Arctan) demodulation algorithm is one of the key demodulation techniques in FOISs. The conventional PGC-Arctan demodulation algorithm requires the specific value of the phase modulation depth C to work properly. However, C will variate with laser wavelength, temperature, and humidity in the actual working environment, which leads to harmonic distortion and even demodulation failure. In this paper, a novel PGC demodulation algorithm called self-calibration PGC-Arctan (PGC-Arctan-SC) demodulation algorithm is presented. The proposed algorithm can jointly estimate the accurate C value by the elliptical parameters and C-related components while suppressing nonlinear distortion by ellipse fitting algorithm (EFA). Then C can be calibrated to the specific predefined optimal value by the closed-loop proportion integration differentiation (PID) module. The simulation results are consistent with theoretical analysis, and the all-digital PGC-Arctan-SC demodulation system is implemented on the embedded SoC. The experimental results show that C can be estimated and calibrated accurately in real time. The signal-to-noise and distortion ratio (SINAD) of the PGC-Arctan-SC demodulation output achieves 61.57 dB.

Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

Abstract: In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost)-based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost-based demodulator is developed, which employs the $k$ -nearest neighbor as a weak classifier to form a strong combined classifier. Finally, the experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost-based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood-based demodulator.

A 115–135-GHz 8PSK Receiver Using Multi-Phase RF-Correlation-Based Direct-Demodulation Method

Abstract: This paper presents the theory, design, and implementation of an 8PSK direct-demodulation receiver based on a novel multi-phase RF-correlation concept. The output of this RF-to-bits receiver architecture is demodulated bits, obviating the need for power-hungry high-speed-resolution data converters. A single-channel 115–135-GHz receiver prototype was fabricated in a 55-nm SiGe BiCMOS process. A max conversion gain of 32 dB and a min noise figure (NF) of 10.3 dB were measured. A data rate of 36 Gb/s was wirelessly measured at 30-cm distance with the received 8PSK signal being directly demodulated on-chip at a bit-error rate (BER) of 1e-6. The measured receiver sensitivity at this BER is −41.28 dBm. The prototype occupies 2.5 $\times $ 3.5 mm2 of die area, including PADs and test circuits (2.5-mm2 active area), and consumes a total dc power of 200.25 mW.

Signal Demodulation With Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset, and Algorithms

Abstract: In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k -nearest neighbor algorithm. These three demodulators are trained and tested by our online open dataset. The experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance.

Hierarchical Bandwidth Modulation for Ultra-Broadband Terahertz Communications

Abstract: Terahertz (THz)-band (0.1–10 THz) communication has been envisioned as a key technology to enable wireless Terabit-per-second (Tbps) links. At THz frequencies, the path-loss is governed by the spreading loss and the molecular absorption loss. The latter also determines the available transmission bandwidth, which drastically shrinks with distance. New physical layer solutions that capture this behavior are needed to maximally utilize the THz band. In this paper, the concept of hierarchical bandwidth modulation is introduced for single-transmitter multiple-receiver communication in the THz band. In the proposed modulation scheme, multiple flows of information aimed at users at different distances are transmitted at the same time and over the same frequency, by simultaneously adapting both the modulation order and, more importantly, the symbol time. Details for the modulator and the demodulator implementations are provided. The performance of the proposed modulation is analytically derived, both in terms of the achievable data rate as well as symbol error rate. Extensive numerical results based on analytical channel models that capture the peculiarities of the THz band are provided to illustrate the performance of the proposed scheme. The results show that the proposed modulation scheme can maximize the utilization of the distance-dependent bandwidth of the THz channel, turning molecular absorption into an advantage.

Extraction of Carrier Phase Delay for Nonlinear Errors Compensation of PGC Demodulation in an SPM Interferometer

Abstract: Traditional phase generated carrier (PGC) schemes require that the carrier phase delay be kept zero and the phase modulation depth be maintained at a certain value; otherwise, serious nonlinear errors will be introduced into the phase demodulation of a sinusoidal phase modulation interferometer. Proposed herein is a robust carrier phase delay extraction method for nonlinear errors compensation of PGC demodulation that mitigates the effect of the interference phase shift. The carrier phase delay is directly extracted by adopting the first-, second-, and fourth-order in-phase and quadrature harmonic components generated by quadrature multi-harmonics frequency down conversion. Further, with the extracted phase delay, real-time compensations for the phase delay and phase demodulation depth are realized simultaneously in PGC demodulation. Theoretical analysis and derivation of the proposed method are given in detail. Simulation and displacement measurement experiments with different phase delays and phase modulation depths are performed to verify the effectiveness of the proposed method. The experimental results demonstrate that the proposed method is able to precisely extract the phase delay and efficiently eliminate the effects of the phase delay and modulation depth instability.

Noncoherent Detection for Ambient Backscatter Communications Over OFDM Signals

Abstract: Backscattering communications have been recently proposed as an effective enabling technology for massive Internet of Things (IoT) development. A novel application of backscattering, called ambient backscattering (AmBC), has been gaining much attention, wherein backscattering communications exploit existing RF signals without the need for a dedicated transmitter. In such a system, data demodulation process is strongly complicated by the random nature of the illuminating signal, as well as by the presence of the direct-link interference (DLI) from the legacy system. To overcome these shortcomings, one can resort to noncoherent detection strategies, aimed at reducing or even nullifying the amount of a priori information needed to reliably perform signal demodulation. In this paper, we investigate noncoherent detection strategies for backscatter communications over ambient OFDM signals and solve the noncoherent maximum-likelihood (ML) detection problem for a general Q-ary signal constellation. Additionally, we derive a suboptimal detector, which takes the form of the classical energy-detector (ED), whose performance is evaluated in closed-form. Finally, the performance of the proposed detectors is corroborated through Monte Carlo simulations.

NOMA-Aided Precoded Spatial Modulation for Downlink MIMO Transmissions

Abstract: In this paper, a novel multi-user multiple-input multiple-output (MIMO) transmission scheme, called non-orthogonal multiple access (NOMA) aided precoded spatial modulation (PSM) (NOMA-PSM) is proposed for overloaded downlink transmissions. NOMA-PSM beneficially amalgamates the concept of index modulation (IM) and NOMA techniques, and therefore it inherits both the merits of IM with low-complexity transceiver and the advantages of NOMA with high bandwidth efficiency. For the proposed scheme, we develop a pair of low-complexity yet effective detection algorithms by combining the spatial index demodulation and successive interference cancelation. The spectral efficiency (SE), implementation cost, and multi-user interference of NOMA-PSM are evaluated and compared with conventional designs. Furthermore, we derive the mutual information (MI) of the proposed NOMA-PSM to characterize its achievable SE and also obtain a lower bound for simplifying the measurement of MI. Our simulation results show that the proposed NOMA-PSM scheme is capable of achieving considerable performance gains over conventional orthogonal multiple access aid PSM and antenna-grouping-based PSM in wireless MIMO fading channels.

Full-Duplex High-Speed Simultaneous Communication Technology for Wireless EV Charging

Abstract: This letter presents a novel simultaneous wireless power and data transmission (SWPDT) system for wireless electric vehicle (EV) charging. The data carrier is injected and extracted by a plug-and-play toroidal-core inductor. Two data carriers of 5 and 6.25 MHz are adopted to achieve bidirectional and full-duplex communication using frequency-division multiplexing. Differential quadrature phase-shift keying modulation is achieved by a Class-E amplifier in the data transmitter. An analog switch circuit is designed to demodulate the data carrier in the data receiver. The proposed method is verified by a prototype that achieves up to 64 kbps full-duplex data transmission under 3.3 kW power transfer. The bit rate and interference immunity of data transmission are greatly improved compared to the reported SWPDT systems, which makes it possible to realize high-speed simultaneous communication in the kilowatt-level wireless EV charging.

"Machine LLRning": Learning to Softly Demodulate

Abstract: Soft demodulation, or demapping, of received symbols back into their conveyed soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any modern receiver. In this paper, a trainable universal neural network-based demodulator architecture, dubbed "LLRnet", is introduced. LLRnet facilitates an improved performance with significantly reduced overall computational complexity. For instance for the commonly used quadrature amplitude modulation (QAM), LLRnet demonstrates LLR estimates approaching the optimal log maximum a-posteriori inference with an order of magnitude less operations than that of the straightforward exact implementation. Link-level simulation examples for the application of LLRnet to 5G-NR and DVB-S.2 are provided. LLRnet is a (yet another) powerful example for the usefulness of applying machine learning to physical layer design.

An IR-UWB CMOS Transceiver for High-Data-Rate, Low-Power, and Short-Range Communication

Abstract: An impulse radio ultra-wideband (IR-UWB) transceiver with digitalized multipulse position modulation (D-MPPM) is proposed for high-data-rate, low-power, and short-range communication. The D-MPPM overcomes the data-rate dependence on the system clock frequency by modulating data with the time difference between the two pulses and also allows fully digital circuit-based modulation and demodulation. With this technique, the data rate is improved by a factor of 5 compared to conventional IR-UWB modulation schemes at the same clock frequency, which results in 4.28-dB link margin enhancement compared to bi-pulse position modulation (BPPM). Also, the technique makes it possible to design a clockless receiver, which allows more compact and power efficient design. Thus, it can achieve 500 Mb/s with 27.7-mW power consumption for Rx and 7 mW for Tx and the radio range of 1–5 m.

108-km Distributed Acoustic Sensor With 220-p $\varepsilon /\surd$ Hz Strain Resolution and 5-m Spatial Resolution

Abstract: The measurement distance is one of the most important parameters for distributed acoustic sensor (DAS). In this paper, we report a long-distance and high-sensitivity DAS system based on time-gated digital optical frequency domain reflectometry. The bi-directional distributed Raman amplification is adopted to realize long measurement distance. The shape of optical pulse is pre-distorted to be a standard Hanning window, which provides a theoretical peak-side lobe ratio of 46 dB to suppress the crosstalk. The interference fading and polarization fading are well suppressed, and hence phase-demodulation method is adopted, instead of intensity demodulation method. As a result, the sensitivity is enhanced and the full information (amplitude, phase, and frequency) of the vibration can be obtained. In experiments, the fiber length is about 108 km, whereas the spatial resolution is 5 m. A weak vibration with peak–peak amplitude of 14.7 n $\varepsilon$ is correctly located at the distance of 98 km with a high SNR of 30 dB. It is the first time that 220-p $\varepsilon /\surd$ Hz strain sensitivity is realized over 100-km-level fiber and the vibration waveform is retrieved linearly without harmonics.

A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis.

Abstract: To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Abstract: The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

Error Performance of Orthogonal Time Frequency Space (OTFS) Modulation

Abstract: Orthogonal time frequency space (OTFS) transmits information symbols in the delay-Doppler domain rather than in the time-frequency domain (as with OFDM). In its original version, OTFS uses transmit and receive shaping waveforms which are biorthogonal with respect to translations by integer multiples of basic time and frequency intervals. In this paper we examine the performance of OTFS using general waveforms. Based on a discrete model of the modulation/demodulation procedure, we compute error probability, and we discuss the amount of diversity that can be achieved on fading channels.

Resonant Microelectromechanical Receiver

Abstract: This paper reports a practical demonstration of a proposed resonant microelectromechanical receiver for low power wake-up receiver (WuRx) applications. The proposed system is made of three main components: a piezoelectric based acoustic resonator, an electrostatically-driven MEMS resonant demodulator, and a CMOS baseband trigger circuit. The filtering, amplification, and demodulation of the incoming signal are performed through the piezoelectric resonator and the resonant demodulator, ensuring zero power consumption for both the radio frequency (RF) and mixing stages. The only power consumption occurs at baseband by operating the CMOS circuit in deep subthreshold. This system utilizes a lithium niobate piezoelectric resonator possessing a figure of merit of around 650 and achieving voltage gain of 28 dB. A resonant demodulator fabricated in the Epi-Seal MEMS process is utilized to attain a conversion efficiency of 13.8 nA/V2. In the trigger circuit, an amplifier with transimpedance $>200~\text{M} {\Omega }$ is utilized to sense the output current from the demodulator. An intermediate buffer and a multiple stage passive latch rectifier are utilized to generate the wake-up signal through a Schmitt trigger. The demonstrated system achieves −40.2 dBm sensitivity for a 44 kHz amplitude-modulated 50 MHz RF signal while consuming 38.75 nW. This architecture is a promising step toward a WuRx capable of achieving higher interference rejection, high sensitivity, and nW range power consumption at a high data rate. [2018-0261]

A 60-GHz 3.0-Gb/s Spectrum Efficient BPOOK Transceiver for Low-Power Short-Range Wireless in 65-nm CMOS

Abstract: This paper presents a 60-GHz transceiver for low-power high-speed short-range wireless using the proposed binary-phase on-off keying (BPOOK) modulation scheme. The proposed BPOOK wireless transceiver transmits radio frequency (RF) signal with amplitude modulated on and off by input baseband data, and meanwhile, the phase is changing between 0° and 180°. The BPOOK transceiver achieves doubled spectral efficiency compared with OOK modulation and binary-phase-shift keying (BPSK) modulation. It also cancels the intrinsic local-oscillator feed through (LOFT) issue in the OOK modulation. The BPOOK RF signal can be demodulated by employing low-power square-law envelope detector incoherently. The transceiver is fabricated in a standard 65-nm CMOS technology. A data rate of 3.0 Gb/s is achieved while consuming a power of 100 mW from 1-V supply. The incoherent receiver has a sensitivity of −46 dBm. The core area of the transceiver is 1.56 mm2.