M-QAM Signals Research Articles

Multibeam-based mmWave segmented beamforming array system for high efficiency and secure communication

A new mmWave transmitter architecture with multibeam-based segmented beamforming arrays (SBAs) for high-efficiency and secure communication is proposed. Unlike convetional communication architecture, which transmits a high-order QAM signal through a single RF-chain, the proposed structure transmits independently controlled multiple QPSK signals to form M-QAM signals. The multiple independent SBA chains radiate independent QPSK signals at each chain’s maximum operation efficiency to reduce the peak-to-average-power ratio. Then, multiple beams with QPSK signals are steered by the SBAs to form a line of combination (LoC) where multiple QPSK signals are spatially vector-combined to generate M-QAM signals correctly. For any unidentified receiver outside the LoC, the desired high-order QAM signals cannot be retrieved owing to the lack of required sets of QPSK signals and SBA steering information. Thus, higher data security can be also guaranteed. To verify the proposed architecture, 28-GHz SBA-based transmitter and reference receiver were implemented. Three QPSK signal sets were transmitted by three SBAs supporting independent beamforming to flexibly create a narrow LoC where the receiver retrieved 64-QAM signals successfully. Furthermore, secured data transmission was verified with an extra eavesdropper in the vicinity of the target receiver. By controlling the LoC, only the target receiver successfully demodulated the 64-QAM signals.

Pump-Free and Reconfigurable All-Optical Modulation Format Conversion for MQAM Signals by Parallel Nonlinear Mach-Zehnder Interferometers

Pump-Free and Reconfigurable All-Optical Modulation Format Conversion for MQAM Signals by Parallel Nonlinear Mach-Zehnder Interferometers

A Deep MIMO Detector Based on MQAM Signal Decomposition and Channel Ordering

A Deep MIMO Detector Based on MQAM Signal Decomposition and Channel Ordering

Low-complexity algorithms for coherent optical systems with transceiver IQ imbalance.

The next-generation coherent optical communication relies on higher symbol transmission rates with higher modulation cardinality. The imbalance between the in-phase (I) and the quadrature (Q) arms in such systems leads to deteriorated performance if not corrected. In this work, we propose a reference constellation adaptation-based single-tap, novel carrier phase recovery algorithm, tolerant to the transmitter IQ imbalance and a pilot-free geometric parameter extraction-based receiver IQ imbalance correction method for MQAM signals. The proposed scheme is numerically investigated through extensive simulations for 32 GBaud polarization multiplexed (PM)-16QAM, and 64QAM modulation formats and experimentally verified for 32 GBaud PM-16QAM signal. The proposed pilot-free scheme is applicable to any modulation format and offers lower computational complexity than traditional algorithms.

Design Implementation and Performance Analysis of M-QAM and PAM-n Signalling Schemes

Abstract: Two circuits have been simulated in the Optisystem 20 software. One circuit has M-QAM and the other has PAM-n. Both the circuits have been compared on the basis of Bits per symbol, signal power,Noise power, etc. The total power and noise power havebeen calculated in the electrical power meter visualizer.In both the circuits, PRBS generator is used to generate the sequence of the bits. In M-QAM,return to zero pulse generator is used and in PAM-n notreturn to zero pulse generator is used. The full width pulse is also known as NRZ (not return to zero) and halfwidth pulse is also known as RZ (return to zero). The main objective of the simulation is to get perfect Eye diagram in the Eye Diagram Anaylzer. The parametriccomparison of different types of QAM have also be doneon the basis of number of samples, sequence length, samples per bit and output power in dBm andcomparison is done by state of art. In the final result, itwill be concluded that M-QAM performs much better than the PAM-n as it is capable of transferring higher data rates. The different types of M-QAM and PAM-n eye diagrams have been plotted like 16-QAM, 64-QAMand PAM-3.

Voting-Based Deep Convolutional Neural Networks (VB-DCNNs) for M-QAM and M-PSK Signals Classification

Automatic modulation classification (AMC) using convolutional neural networks (CNNs) is an active area of research that has the potential to improve the efficiency and reliability of wireless communication systems significantly. AMC is the approach used in a communication system to detect the type of modulation format at the receiver end. This paper proposes a voting-based deep convolutional neural network (VB-DCNN) for classifying M-QAM and M-PSK signals. M-QAM and M-PSK signal waveforms are generated and passed through the fading channel in the presence of additive white Gaussian noise (AWGN). The VB-DCNN extracts features from the input signal through convolutional layers, and classification is performed on these features. Multiple network instances are trained on different subsets of training data in the VB-DCNN. A network instance predicts the input signal during testing. Based on the votes, the final prediction is made. Different simulation experiments are carried out to analyze the performance of the trained network, and the DCNN is designed with the Deep Neural Network Toolbox in MATLAB. The generated frames are divided into training, validation, and test datasets. Lastly, the classification accuracy of the trained network is determined using test frames. The proposed model’s accuracy is near to 100% at lower SNRs. The simulation results show the superiority of the proposed VB-DCNN compared to existing state-of-the-art techniques.

Automatic Modulation Classification for MASK, MPSK, and MQAM Signals Based on Hierarchical Self-Organizing Map.

Automatic modulation classification (AMC) plays a fundamental role in common communication systems. Existing clustering models typically handle fewer modulation types with lower classification accuracies and more computational resources. This paper proposes a hierarchical self-organizing map (SOM) based on a feature space composed of high-order cumulants (HOC) and amplitude moment features. This SOM with two stacked layers can identify intrinsic differences among samples in the feature space without the need to set thresholds. This model can roughly cluster the multiple amplitude-shift keying (MASK), multiple phase-shift keying (MPSK), and multiple quadrature amplitude keying (MQAM) samples in the root layer and then finely distinguish the samples with different orders in the leaf layers. We creatively implement a discrete transformation method based on modified activation functions. This method causes MQAM samples to cluster in the leaf layer with more distinct boundaries between clusters and higher classification accuracies. The simulation results demonstrate the superior performance of the proposed hierarchical SOM on AMC problems when compared with other clustering models. Our proposed method can manage more categories of modulation signals and obtain higher classification accuracies while using fewer computational resources.

Widely Linear Filtering for Multiimpairment Compensation in Dispersion Managed mQAM Modulated Optical Systems

We propose a blind joint equalization algorithm for M-QAM signals based on a widely linear filtering approach. The proposed scheme jointly compensates for receiver IQ imbalance, receiver IQ skew, polarization mixing, carrier recovery, followed by transmitter IQ imbalance and skew. We first investigate the proposed scheme&#x2019;s tolerance to each of these impairments through numerical simulations for 32 GBd PM-16QAM and PM-64QAM signals and compare its performance with the conventional digital processing algorithms with and without IQ imbalance compensation. The proposed joint transceiver equalizer outperforms the conventional algorithms with a <i>Q²</i> improvement of greater than 1 dB, and with improved tolerance to IQ imbalance. Further, with the proposed algorithm, we experimentally demonstrate the improvement in <i>Q²</i> value with respect to conventional DSP for both PM-16QAM and PM-64QAM signals. We also show that the MSE convergence of the proposed joint equalizer is much faster than conventional DSP algorithms.

Bit Error Floor Reduction for MQAM Signals in the Presence of Phase Error

Bit Error Floor Reduction for MQAM Signals in the Presence of Phase Error

Experimental validation of CNNs versus FFNNs for time- and energy-efficient EVM estimation in coherent optical systems

Error vector magnitude (EVM) has proven to be one of the optical performance monitoring metrics providing the quantitative estimation of error statistics. However, the EVM estimation efficiency has not been fully exploited in terms of complexity and energy consumption. Therefore, in this paper, we explore two deep-learning-based EVM estimation schemes. The first scheme exploits convolutional neural networks (CNNs) to extract EVM information from images of the constellation diagram in the in-phase/quadrature (IQ) complex plane or amplitude histograms (AHs). The second scheme relies on feedforward neural networks (FFNNs) extracting features from a vectorized representation of AHs. In both cases, we use short sequences of 32 Gbaud m-ary quadrature amplitude modulation (mQAM) signals captured before or after a carrier phase recovery. The impacts of the sequence length, neural network structure, and data set representation on the EVM estimation accuracy as well as the model training time are thoroughly studied. Furthermore, we validate the performance of the proposed schemes using the experimental implementation of 28 Gbaud 64QAM signals. We achieve a mean absolute estimation error below 0.15%, with short signals consisting of only 100 symbols per IQ cluster. Considering the estimation accuracy, the implementation complexity, and the potential energy savings, the proposed CNN- and FFNN-based schemes can be used to perform time-sensitive and accurate EVM estimation for mQAM signal quality monitoring purposes.

Помехоустойчивость приема сигналов с амплитудно-фазовой манипуляцией в присутствии фазоманипулированной помехи

The development of television technologies and the need to transmit high-quality video information requires that modern digital television and broadcasting systems can transmit large amounts of information. The use of signals with multi-position amplitude and phase-shift keying 16-APSK and 32-APSK in DVB-S2 generation satellite television systems made it possible to transmit 30% more data in the same frequency bands compared to the previous DVB-S standard. The latest DVB-S2X digital satellite TV standard is an extension of the DVB-S2 standard and supports modulations of 64,128,256-APSK. It is known that increasing the positionality of signals reduces the noise immunity of their reception and the system as a whole. In addition, the presence of non-fluctuation interference in the radio channel that falls into the frequency band of the useful signal has a strong influence on noise immunity. The article uses statistical radio engineering methods to calculate the probability of a bit error when receiving M-APSK signals against the background of phase-shift keying interference at different parameters of the latter. It is shown that of phase-shift keying interference of even low intensity (µ &lt; 0.3) falling into the main lobe of the signal spectrum significantly reduces the noise immunity of receiving 16-APSK and 32-APSK signals. High-intensity interference (µ ≥ 0.5) practically destroys reception. The influence of phase-shift keying interference decreases with an increase in its detuning relative to the carrier frequency of the useful signal and an increase in the relative transmission rate of the interference. A comparison of the reception quality when using M-PSK, M-QAM and M-APSK signals is carried out and it is shown that the noise immunity of receiving M-QAM and M-APSK against the background of phase-shift keying interference is approximately the same. M-PSK signals are significantly inferior to them in terms of reception noise immunity.

The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

With the commercialization of 5G, in order to recognize QAM signals, one of the main modulation modes in 5G communication systems, this paper put forward the BP-BSA network model based on bird swarm algorithm (BSA) and BP neural network. Firstly, the instantaneous features and high-order cumulants were selected as the appropriate feature statistics by analyzing the characteristics of MQAM signals. After that, the structure of the BP neural network model was determined, and the initial parameters of the BP neural network were optimized using BSA to accelerate the convergence speed of the network. Finally, the features processed with signal to noise ratio (SNR) disorder were used to train and test the BP neural network model. The BP-BSA network model proposed in this paper applies the bird swarm algorithm to the field of modulation recognition for the first time. And the simulation results show that the recognition accuracies of 16, 32, 64, 128, 256QAM signals in the SNR range of -5 dB to 20 dB all reach above 98%. Compared with the same type algorithms, the algorithm proposed in this paper has good recognition performance.

Performance analysis of WDM-SDM system with employing Phase-Conjugated twin waves technique

In this paper, the phase conjugated twin waves (PCTWs) technique is proposed to reduce the nonlinear phase noise in wavelength division multiplexing (WDM) - spatially division multiplexing (SDM) system. In the proposed system, the m-ary quadrature amplitude modulation (mQAM) signals are generated and multiplexed to produce mQAM WDM signal. Similarly, the second WDM signal is created by multiplexing the phase conjugated copies of the mQAM signals and then they are spatially multiplexed by two fiber links. At receiver, they are firstly demultiplexed and then superimposed coherently to revoke the nonlinear phase noise and improve the signal to noise ratio (SNR). Moreover, the analytical model that best describes the phase noise alleviation is developed. Analytical results show that the proposed scheme significantly reduces the variance of the phase noise. Furthermore, to show the effectiveness of proposed scheme, 16-channel 4QAM WDM-SDM system with and without PCTWs method is numerically investigated. Numerical results reveal that the PCTWs scheme enhances the SNR of the received signal by more than 2.7 dB and extends the achievable transmission distance by 39.4% at 10 −5 bit error rate (BER).

Non-Standalone 5G NR Fiber-Wireless System Using FSO and Fiber-Optics Fronthauls

The fifth-generation of mobile networks (5G) claims for a radio access network (RAN) update in order to support the enormous incoming wireless data traffic. In this context, we experimentally evaluate two distinct hybrid architectures applied to 5G New Radio (NR) FiWi systems based on different optical fronthaul approaches. The first architecture operates in non-standalone (NSA) mode, defined by the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> generation partnership project (3GPP), for simultaneously transmitting 4G and 5G technologies through an unique FiWi system. The three considered waveforms are as follows: a filtered orthogonal frequency division multiplexing (F-OFDM) signal at 778 MHz with 10 MHz bandwidth from our 5G flexible-waveform transceiver; a long-term evolution-advanced (LTE-A) signal with five 20 MHz sub-bands centralized at 2.24 GHz; a 5G NR signal at 2.35 GHz with 100 MHz bandwidth. On the other side, the second architecture employs radio over fiber (RoF), free space optics (FSO), and wireless technologies converged into a heterogeneous network (HetNet). The additional multi-standard and multiband optical-wireless network is based on a 10-MHz bandwidth F-OFDM signal at 788 MHz, a 100-MHz bandwidth 5G NR signal at 3.5 GHz, and a 400-MHz bandwidth M-QAM signal at 26 GHz. Throughput up to 3 and 1.4 Gbps are demonstrated for RoF/FSO and RoF/FSO/Wireless transmission, respectively.

Modulation format identification of square and non-square M-QAM signals based on amplitude variance and OSNR

Blind modulation format identification (MFI) is essential in an elastic optical network (EON) due to unknown modulation format of the received signals. We propose an MFI method for square and non-square quadrature amplitude modulation (QAM) signals, quadrature phase-shift keying (QPSK), 8-QAM, 16-QAM, 32-QAM, 64-QAM, 128-QAM, 256-QAM, 512-QAM, 1024-QAM, and 2048-QAM. We identified these modulation formats by using two identification factors such as the amplitude variance and the optical signal-to-noise ratio (OSNR) of the received signal. The simulation results based on a look-up-table (LUT) and a neural network, respectively, show that the modulation format of the received signals can be identified correctly in the OSNR range of a bit error rate (BER) for communication, better than 10−2. We also investigate effects of both ONSR measurement errors and residual chromatic dispersion (CD) on the MFI performance.

Accuracy Enhancement of Moments-Based OSNR Monitoring in QAM Coherent Optical Communication

Optical signal-to-noise ratio (OSNR) is an important parameter in coherent optical communication systems to monitor the quality of the received signal and the transmission-link fault. The OSNR monitoring technique based on the moments of the received signals is simply and blind, where no extra training sequence is required. The moments-based method works well with QPSK signals with constant modulus constellations but has degraded performance for 16-QAM and 64-QAM signals with multilevel constellations. Instead of calculating higher-order moments, we proposed an accurate second- and fourth-order moments-based OSNR monitoring scheme, where the clock recovery of the M-QAM signal is first implemented and then the inner amplitude level is extracted to estimate the OSNR values. The proposed method has been verified and evaluated in QPSK, 16-QAM and 64-QAM simulations and experiments.

Impact of Frequency Shift on Nonlinear Compensation Using Optical Phase Conjugation for M-QAM Signals

Nonlinear compensation using optical phase conjugation (OPC) have been considered a promising technique to increase the reach of high-speed fiber-optic transmission systems. OPC-based nonlinear compensation employs an optical phase conjugation located at a middle of the fiber link to generate a complexed conjugated signal with respect the signal in the first half of the link for propagation in the second half. OPC technique assumes a symmetry for signal propagating in the first and second half to obtain a perfect nonlinear and chromatic dispersion. However, as most of practical OPC schemes are realized by nonlinear effects such as four-wave mixing or a combination of second-harmonic generation and difference frequency generation, the frequency shift induced by OPC affects the signal symmetrical requirement for nonlinear compensation because the chromatic dispersion is different for the first and second half transmissions. In this paper, we investigate the impact of frequency shift on the nonlinear compensation using OPC for high symbol rate, high level modulation format signals. This will be important to understand the tolerance of the OPC techniques against such a practical condition for actual system implementations.

Automatic Modulation Classification Based on Novel Feature Extraction Algorithms

In order to solve the problems that the accuracy of modulation recognition algorithms of MSK and MQAM signals is not ideal under the condition of low signal-to-noise ratio (SNR) in Additional White Gaussian Noise (AWGN) environment, two novel features, the differential nonlinear phase Peak Factor (PF) and the reciprocal of amplitude envelope variance of cyclic spectrum at zero frequency cross section after difference and forth power processing, are constructed, which can complete the recognition of MSK signal and the classification of MPSK and MQAM signals respectively. This paper proposes a mixed recognition algorithm based on the two new features and other classical features, and design a tree shaped multi-layer smooth support vector machine classifier based on feature selection algorithm (FS_DT-SSVM) to recognize eleven kinds of digital modulation signals. The simulation results illustrate that the algorithm can achieve the classification of the modulation signals {2FSK, MSK, 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM} with small SNR. When the SNR is not less than -1dB, the recognition rate of the classifier for all signals exceed 97%, which validates the effectiveness of the proposed modulation recognition algorithm.

Performance evaluation of OFDM systems over α – κ – μ fading

In this Letter, the authors derive the moment generating function-based BER expressions for binary phase shift keying and M-QAM signal for OFDM systems over –– fading. This fading model is known as the generalised fading model because it can accurately characterise all the well known small-scale fading models given in the literature. In order to validate the derived BER expressions, analytical and simulated BER curves for a variety of modulation formats at different values of fading parameters are also shown in the letter.

A Novel Automatic Modulation Classifier Using Graph-Based Constellation Analysis for &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;${M}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-ary QAM

An innovative automatic modulation classification via graph-based constellation analysis for M-ary QAM signals is presented. In our framework, a unified mesh model for the constellation diagrams of the M-QAM signals within the modulation candidate set is first constructed and exploited to transform the received M-QAM signal into graph domain. The concise graph representation of the received M-QAM signal is established from its constellation according to the positions of the recovered symbols in the mesh model. Then, the modulation feature vector is built from the eigenvector(s) corresponding to the maximum eigenvalue of its adjacency matrix. The modulation type can be identified by measuring the angle between the feature vectors resulting from the training data and the test data. Monte Carlo simulation results and theoretical analysis demonstrate that the proposed method with lower computational complexity can provide superior performance to the existing subtractive clustering technique, and is robust to the residual phase and timing offsets.