Demodulation Performance Research Articles

Robust neural network receiver for multiple-eigenvalue modulated nonlinear frequency division multiplexing system

Nonlinear frequency division multiplexing (NFDM) has been shown to be promising in overcoming the fiber Kerr nonlinearity limit. In multiple-eigenvalue modulated NFDM systems, the transmission capacity increases with the number of modulated eigenvalues. However, as the number of modulated eigenvalues increases, the complexities of the signal waveform and the nonlinear Fourier transform (NFT) algorithm for demodulation increase dramatically as well, while the accuracy drops significantly. Meanwhile, impairments such as amplifier spontaneous emission noise and phase noise in practical channels would perturb the eigenvalues and the corresponding nonlinear spectra during transmission. Coupled with an increase in the modulation format order, it is difficult for NFT algorithm-based receivers to recover information. To enable the use of multiple-eigenvalue modulated NFDM systems, we propose an innovative receiver based on regression neural networks (NNs), which can demodulate information correctly for both single- and dual-polarization NFDM systems. The results show that it has strong robustness and has a certain tolerance to the impairments of communication systems. In the contrast that the poor demodulation performance of the NFT and the Euclidean minimum distance (MD) receivers for multi-eigenvalue modulated NFDM systems, our proposed NN receiver can achieve low bit error rate with 2 GBaud 16QAM modulation over 1,000 km transmission in four-eigenvalue modulated single-polarization NFDM systems. The performance of three receivers (NFT, MD and NN) in a two-eigenvalue modulated NFDM system are also compared, the NN receiver shows the best performance and appears more suitable for higher-order modulation formats.

Demodulating AM Square Signals via a Digital Timer for Sensor Applications

This article evaluates theoretically and experimentally the performance of a timer-based demodulator applied to low-frequency amplitude-modulated (AM) square signals coming from sensor circuits. The demodulator extracts the amplitude of the AM square signal by measuring the period of a reference triangular signal that is altered by the AM signal itself, as already suggested in a previous article but for AM sinusoidal signals. The demodulation and digitization are carried out simultaneously via a digital timer and without requiring a rectifier, a mixer, a low-pass filter, or an analog-to-digital converter, thus resulting in a simple and low-cost design solution. Unlike the sinusoidal case, this article proposes to infer the amplitude of the AM square signal by measuring the bias of the period measurement with respect to the ideal (known) value. Experimental results show a nonlinearity error lower than 0.03% full-scale span and a resolution of 13.3 bits.

Application of adaptive digital down-conversion to underwater acoustic communication with nonuniform Doppler shift

In underwater acoustic communication, nonuniform Doppler shift can severely degrade demodulation performance. Nonuniform Doppler shift causes nonuniform phase shifts and variation of received time. In recent studies, a new type of processing was proposed to suppress the effects of the nonuniform time variation. In this paper, we show mathematically how the nonuniform time variation affects communication in conventional demodulation, and describe how the proposed processing works against the time variation. Additionally, we use a simulation to investigate how the proposed processing improves demodulation performance. The simulation is carried out under numerous motional conditions at various symbol rates. The results clearly show the improvement by the proposed processing and clarify the conditions under which the new processing improves performance.

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

A Machine Learning Approach to Phase Reference Estimation With Noise

This paper concerns the problem of phase reference estimation with noise, introduced by the imperfect phase-locked loop (PLL) circuit, or the imperfect channel estimation, or both. Prior solutions for suppressing phase noise focus on improving the accuracy of phase reference estimation. The accuracy of phase reference estimation is not high enough due to the following two limitations. First, since the PLL circuit works in radio-frequency (RF), a PLL circuit with high accuracy leads to high cost and high complexity, which makes the deployment difficult. Second, as data rates increase and wireless channels become more complex, the receiver is more difficult to obtain an ideal channel estimation and the negative effect of phase noise becomes more apparent. In this paper, we propose a machine learning approach to mitigate the negative effect of phase noise by using clustering algorithms. The key intuition of our approach is that the clustering algorithm can adaptively trace the shifted constellation point due to the phase noise. Our approach is adaptive because it can adaptively find each received symbol belongs to its original constellation point if the phase noise is not too large, e.g. , no larger than $0.25 \pi $ . While the shifted distance is not too large, we can map the received symbols into the correct constellation point to mitigate the negative effect of phase noise. Instead of directly using conventional clustering algorithms into the proposed machine learning approach, we propose a new weighted ensemble clustering algorithm to further improve the performance of our approach. In comparison with prior approaches based on RF circuits, our approach has comparable reception performance but with low complexity and low cost. Our experimental results show that, for a QPSK system, our approach improves the demodulation performance and the decoding performance about 10 dB, 8 dB under BCH codes, and 3 dB under Turbo codes, respectively. Even the demodulation performance of our approach without channel coding is better than the decoding performance of the system with channel coding about 5 dB under BCH codes.

Wide Dynamic Range Multichannel Lock-In Amplifier for Contactless Optical Sensors With Sub-pS Resolution

A multichannel impedance-sensing CMOS chip for the readout of noninvasive light detectors in silicon photonics (SiP) is presented. The performance of the lock-in phase-sensitive demodulator is improved thanks to a capacitive feedback transimpedance amplifier (TIA) equipped with an active discharge network, able to keep a dc output offset lower than 3 mV for leakage currents up to 4 nA while injecting a noise lower than 40 fA/ $\sqrt {\mathrm{ Hz}}$ . The addition of a sensor capacitance compensation system at the input of the chip allows a 30-fold increase of the stimulation amplitude, enabling the tracking of sub-picoSiemens conductance variations on a baseline of few $\mu \text{S}$ , with a bandwidth of 70 Hz. The chip performance opens the way to multipoint monitoring of complex photonic systems, addressing the challenges posed by the growing complexity of these architectures.

ML-Based Forward Symbol Timing Offset Estimation for Burst Signals

In order to ensure the performance of burst signal demodulation, forward symbol timing offset estimation algorithm is usually used for symbol timing recovery. Aiming at the shortcomings of common forward symbol timing estimation algorithms, a new algorithm based on maximum likelihood estimation joint trigonometric polynomial interpolation is proposed in this article, which is suitable for the data of four samples per symbol and can resist large frequency offset. Hereafter, in order to make it more suitable for engineering practice, optimize it into an improved data-aided (DA) forward symbol timing offset estimation algorithm with multiple characteristics, e.g., moderate frequency offset capture range, insensitive to shaping coefficient, relatively low complexity, excellent estimation performance, flexible algorithm structure and less sample data required for calculation. The simulation results show that the performance of the improved algorithm in this article can approach the modified cramer-rao bound (MCRB) within the frequency offset capture range, under the conditions of low signal to noise ratio (SNR) and small shaping coefficient.

Push the Limit of Light-to-Camera Communication

Recent years have witnessed the conceptualization and development of light-to-camera communication (LCC). Various innovative modulation schemes are designed for throughput improvement. Nevertheless, the fundamental limits of LCC itself are still unknown from a theoretical perspective. In this paper, we present the LCC channel model to shed light on a question: how to improve the system performance under identical conditions of transmitting/receiving device and modulation scheme. We reveal the channel parameters of LCC that affect the formation of the signal layer and provide a design guideline for increasing the symbol rate. Moreover, we improve the demodulation performance by sampling judging points from the separated signal layer instead of detecting the widths of bands in capture images. To enhance the signal-to-noise ratio, we propose a short-time linear regression algorithm to filter out the noise from the signal layer. We build a hardware prototype with the simple ON-OFF keying to demonstrate the proposed channel model under various experiment settings. The results show that the system throughput reaches 8.2 kbps with a symbol rate of 25 kBd. We believe our techniques for symbol rate increasing and demodulation improvement can help other LCC systems improve their performance.

An analog photonic down-conversion link with simultaneous IMD3 distortion suppression and dispersion-induced power fading compensation

A novel analog photonic down-conversion link based on a polarization modulator and a phase modulator with simultaneous linear optimization and chromatic dispersion-induced power fading compensation is proposed and experimentally demonstrated. By introducing an appropriate phase shift to the optical sidebands, chromatic dispersion-induced power fading is compensated, and third-order intermodulation distortion terms are eliminated in the same time. The scheme exhibits wide operating frequency range due to its no use of filters and frequency-dependent electrical or optical devices. The scheme is robust, as the operation of the polarization modulator and the phase modulator are independent of signal amplitude and bias voltage. Experimental results show that, for a 25-km transmission link, an improvement of more than 15.5 dB in spurious-free dynamic range is achieved and the dispersion-induced power fading is compensated at the same time. In addition, benefiting from the power compensation and significant suppression of IMD3, a 16QAM vector signal is demonstrated to have a good demodulation performance.

Complex-Valued Neural Networks for Noncoherent Demodulation

Noncoherent demodulation is an attractive choice for many wireless communication systems. It requires minimal protocol overhead for carrier synchronization, and it is robust to radio impairments commonly found in low-cost transceivers. Machine learning techniques, such as neural networks and deep learning, offer additional benefits for these systems. Practical communication systems often include nonlinearities, non-stationarity, and non-Gaussian noise, which complicate mathematical derivation of optimum demodulators. Learning approaches can optimize demodulator performance directly from simulated or measured radio data, which is often plentiful in the design and verification of today’s integrated transceivers. This paper examines several candidate neural network topologies for use in noncoherent demodulation and provides a mathematical framework for their comparison. Each is based on a complex-valued feature detection layer, which may be characterized as coherent or noncoherent, followed by one or more real-valued classification layers. Backpropagation equations for the noncoherent feature layer include a synchronization term that facilitates training with noncoherent input data. The coherent layer does not synchronize training data, however a noncoherent demodulator can still be constructed by increasing the coherent layer capacity and adding a max pooling layer to marginalize the unknown signal phase. A frequency classification example highlights the differences between the topologies and confirms that optimum noncoherent demodulation can be learned in the presence of AWGN and random phase offsets. The topologies considered here are suitable for noncoherent demodulation of power-efficient modulations such as FSK and ASK, which are typical in today’s short-range wireless communication systems. It is hoped that such topologies will lead to a future common architecture that can support the wide range of modulation formats in this space.

Towards the implementation of advanced random access schemes for satellite IoT

SummaryMachine‐to‐machine (M2M) communications being pivotal for internet of things (IoT) networks are characterized by low‐cost, low complexity, and often energy constrained terminals with low traffic duty cycle. Satellite networks provide an attractive low‐cost solution for such application, in particular when the terminals (both fixed and mobile) are distributed over a wide geographical area not well served by terrestrial infrastructure. An ALOHA random access (RA) scheme is a natural candidate for M2M communications since it is well matched to sporadic traffic, and it requires little terminals coordination. However, the classical ALOHA scheme suffers from a low throughput when operated in a load region requiring low probability of packet loss. To overcome this intrinsic ALOHA limitation, in the last decade, a lot of effort has been devoted to the investigation of evolutions of the ALOHA scheme reducing the probability of destructive packet collisions thus making it more attractive for satellite IoT. In such context, the Contention Resolution Diversity ALOHA (CRDSA) scheme and the Asynchronous Contention Resolution Diversity ALOHA (ACRDA) have emerged as promising solutions thanks to their high spectral efficiency achievable with low packet loss probability. The results reported in literature are however assuming ideal demodulator performance. This paper investigates the design and optimization of RA burst demodulator algorithms for single‐frequency and multifrequency CRDSA and ACRDA. It evaluates the performance of such algorithms in a number of system scenarios of practical interest and studies the impact of relevant system parameters on several performance metrics.

Phase noise impact on the performance of contention resolution slotted random access schemes

SummaryCost‐effective satellite terminals (STs) that are designed for a large scale deployment in the Internet of things (IoT) applications are constrained by consumer‐grade local oscillators (LOs) to lower the costs, on the one hand, and by limited equivalent isotropic radiated power (EIRP) due to limited peak transmit power and/or low antenna gain, on the other hand. To close the link budget with the low EIRP constraint, STs can resort to adopting robust forward error correcting (FEC) schemes and/or limiting the transmission baud rate, hence increasing the link margin with respect to the required receiver threshold. In general, the LOs at the terminal side are responsible for injecting multiplicative phase distortions to the transmitted signal, known as phase noise (PN). The PN caused by low‐cost LOs could be significant, especially for low baud rates, thus affecting the demodulator performance. Random access (RA) techniques are particularly encouraged on the return link in networks with low‐cost terminals and bursty/low‐duty cycle types of traffic. The state‐of‐the‐art time‐slotted RA techniques are often evaluated considering high EIRP at the terminal side and high baud rates. This paper investigates the impact of the PN in time‐slotted RA techniques and optimizes different carrier phase estimation (CPE) algorithms. Furthermore, it evaluates the impact of CPE errors on RA performance for a wide range of baud rates and typical STs phase noise masks.

Dependence of demodulation performance on symbol rate for underwater acoustic communication with nonuniform Doppler shift

In underwater acoustic communication between surface and underwater vehicles, the nonuniform Doppler shift can degrade demodulation performance severely depending on the symbol rate. In this paper, the dependence of demodulation performance on the symbol rate is investigated using simulations with and without a multipath signal. The results show that symbol rate plays an important role in determining demodulation performance under an environment with a nonuniform Doppler shift. Furthermore, the simulation results without a multipath signal indicate that the effects of a nonuniform Doppler shift of a direct signal can be suppressed at a relatively low symbol rate. On the other hand, the simulation results with a multipath signal show that a high symbol rate is required to suppress the effect of the multipath signal under an environment with a nonuniform Doppler shift.

Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System.

Low-Power Wide-Area Network (LPWAN) is the technology that the Internet-of-Things (IoT) uses in long-distance, wide-coverage scenarios. As one of the ultra-narrowband (UNB) modulation techniques, M-ary position phase shift keying (MPPSK) modulation can provide high coverage and high reliability for LPWAN. This paper proposes a multipath separation method based on MPPSK modulation, which aims to eliminate the influence of multipath on the main path without increasing the spectrum overhead and system complexity. Specifically, the modulation parameter of the system is adjusted according to the delay value, so that the phase jump of the multipath signal falls outside the phase jump of the main path symbol to achieve separation of the multipath from the main path. Moreover, a normalized symbol joint decision method is proposed in order to reduce the introduced noise while using multipath information for decisions. The simulation results indicate that the multipath separation conditions given in this paper can meet the requirements of multipath separation of MPPSK signals. Compared with the existing mainstream decision scheme, the normalized symbol joint decision improves the demodulation performance of the system.

Quadrature Block for UHF Reflection Coefficient Measurements Using a Directional Coupler and Injection Locking

The measurement of the radio frequency reflection coefficient is widely used in characterizing various materials nondestructively. Generally, the measurement is done using a one port vector network analyzer together with some sort of probe. Here, the design theory of a new, low-cost replacement for such a network analyzer is explained. The proposed Cartesian reflection coefficient measurement block is outlined as well as the results of computer simulations and measurements on a printed circuit board (PCB) setup. Unlike previous approaches, it explores the use of injection locking to enhance the performance of quadrature amplitude demodulation of both the forward and the reflected waves as sampled from the output path of an ultrahigh-frequency power amplifier by a bidirectional coupler. Computer simulations at 400 MHz resulted in an average error of 3.4% full scale on the reflection coefficient, including extreme impedance values as well as various parasitic effects of the circuit measurements on a simplified PCB setup, also at 400 MHz.

Underwater Acoustic OFDM Channel Estimation with Unknown Sparsity

Underwater acoustic channels estimation accuracy seriously affects the demodulation performance of the receiver in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. Many channel estimation algorithms based on compressed sensing (CS) have been proposed. However, these algorithms usually needs the information of sparsity, while the information of sparsity is unavailable in many UWA OFDM communications. In this paper, a novel channel estimation algorithm based on fractional Fourier transform (FRFT) and orthogonal matching pursuit (OMP) is proposed, FRFT is introduced to process the synchronization signal, so sparsity is estimated. OMP is adopted to reconstruct channel impulse response (CIR). The most innovative feature of the proposed algorithm is the estimation of sparsity, which makes OMP algorithm more practical. Simulation results show that the proposed algorithm outperforms the least square (LS) and in UWA OFDM communication.

PAPR Reduction in OFDM-IM Using Multilevel Dither Signals

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a novel multicarrier scheme, which uses the indices of the active subcarriers to transmit data. The OFDM-IM also inherits the high peak-to-average power ratio (PAPR) problem, which induces in-band distortion and out-of-band radiation when OFDM-IM signal passes through high-power amplifier (HPA). In this letter, dither signals are added in the idle subcarriers to reduce the PAPR. Unlike the previous work using single level dither signals, multilevel dither signals are used by exploiting that the amplitudes of the symbols in the active subcarriers are variously distributed for different subblocks. As a result, the proposed scheme gives the dither signals more freedom (a larger radius of dithering) in average while keeping the good demodulation performance. Simulation results show that the proposed scheme can achieve better bit error rate performance than the previous works with consideration of HPA.

Design of Efficient LDPC Coded Non-Recursive CPE-Based GMSK System for Space Communications

We consider a low-density parity-check (LDPC) coded non-recursive Gaussian minimum shift keying (GMSK) scheme for space communications subject to low signal-to-noise ratio (SNR), limited power, and spectrum resources. First, we design a non-recursive continuous-phase encoder (NRCPE)-based GMSK modulator to alleviate the impact of the error propagation existed in a recursive CPE (RCPE)-based one. Then, a corresponding pilot-aided quasi-coherent demodulation algorithm (PA-QCDA) is developed for improving the performance of the non-coherent demodulation and the impact of the Doppler shift in the coherent demodulation, whose basic principle is that a modified Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm-based detection performs on the received signals with initial and ending trellis-states being determined using the very small pilot overhead. Finally, we choose proper modulation parameters for the NRCPE-based GMSK signaling according to the tradeoffs between power and spectral efficiency. The simulation results show that the proposed system using the PA-QCDA can achieve excellent performance and can also work well in the presence of the large Doppler shifts and some burst errors.

Evaluation of the Modulation Performance of Injection-Locked Continuous-Wave Magnetrons

This paper proves that 2.45- and 5.8-GHz band continuous-wave magnetrons can be used to perform amplitude, phase, and frequency modulations by applying an injection-locking method. The magnetron behaved like an amplifier, and its output could follow the injection signal. In addition, we have achieved the transmission of amplitude-shift keying data at 200 kb/s as well as phase-shift keying and frequency-shift keying at 10 Mb/s. Moreover, we quantitatively discussed several demodulation performances of the injection-locked magnetrons. Finally, the transmission of audio and video information was demodulated using the injection-locked magnetrons.

Generalized Lyapunov Demodulator for Amplitude and Phase Estimation by the Internal Model Principle

Effective demodulation of amplitude and phase is a requirement in a wide array of applications. Recent efforts have increased the demodulation performance, in particular, the Lyapunov demodulator allows bandwidths up to the carrier frequency of the signal. However, being inherently restricted to a single order filtering of the input signal, signal components outside its passband are not sufficiently attenuated for all applications, such as in multifrequency atomic force microscopy. In this article, the structure of the Lyapunov demodulator is transformed to an equivalent form, taking advantage of the internal model representation of the sinusoid to be tracked. A generalization of this formulation allows the application of standard filtering techniques in order to shape the characteristics of the demodulator, while retaining the perfect tracking condition provided by the internal model. Guidelines for the filter design are provided in order to achieve the desired characteristics, such as filtering order, tracking bandwidth, and transient performance. The resulting generalized Lyapunov demodulator structure is highly flexible, allows for direct employment of any standard filter type, is computationally simple, and easy to implement requiring only a bandpass filter, a single integrator, and two nonlinear transformations. Numerical results demonstrate the effectiveness of the approach, and provide a comparison of the various filters considered.