High-order Modulation Research Articles

A Survey of Wi-Fi 6: Technologies, Advances, and Challenges

Wi-Fi is a popular wireless technology and is continuously extended to keep pace with requirements such as high throughput, real-time communication, dense networks, or resource and energy efficiency. The IEEE 802.11ax standard, also known as Wi-Fi 6, promises to provide data rates of up to almost 10 Gb/s, lower energy consumption, and higher reliability. Its capabilities go far beyond Wi-Fi 5 (802.11ac) and novel technical concepts have been introduced for this purpose. As such, the Wi-Fi 6 standard includes Multi-User Orthogonal Frequency Division Multiple Access (MU OFDMA), Multi-User Multiple-Input Multiple-Output (MU MIMO), new mechanisms for Spatial Reuse (SR), new mechanisms for power saving, higher-order modulation, and additional minor improvements. In this paper, we provide a survey of Wi-Fi 6. Initially, we provide a compact technological summary of Wi-Fi 5 and its predecessors. Then, we discuss the potential application domains of Wi-Fi 6, which are enabled through its novel features. Subsequently, we explain these features and review the related works in these areas. Finally, performance evaluation tools for Wi-Fi 6 and future roadmaps are discussed.

A Deep Learning-Based Robust Automatic Modulation Classification Scheme for Next-Generation Networks

Due to stochastic wireless environment, the process of modulation classification has become a challenging task. Because of its powerful feature extraction ability and promising performance over the conventional schemes, deep learning (DL) models are employed to automatic modulation classification (AMC) problems. Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. In this work, different scaling factors are selected for the generation of [Formula: see text]-QAM frames. The combination of scaling factors, which maximize the classification accuracy is chosen. A convolutional neural network (CNN) with six stages is employed for AMC. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes.

Photonic-layer secure 56 Gb/s PAM4 optical communication based on common noise driven synchronous private temporal phase en/decryption.

Achieving photonic layer security at the lowest network layer to supplement the upper layer digital cryptography in fiber-optic networks is a constant pursuit but a critical challenge. In this Letter, we propose and experimentally demonstrate a high-speed photonic-layer secure optical communication system based on a novel, to the best of our knowledge, common noise driven synchronous private temporal phase en/decryption scheme, which is capable of supporting high-order modulation formats and enhancing security. A record high bit rate of 56 Gb/s 4-level pulse amplitude modulation (PAM4) confidential signal is successfully encrypted and decrypted by remotely synchronized private noise-like en/decryption signals after secret transmission over 20 km of optical fiber with a bit-error-rate (BER) lower than the hard-decision forward error correction (HD-FEC) limit. The demonstrated scheme may provide a promising way for future ultrahigh-speed photonic-layer secure optical communication.

Transmit antenna selection for spatial modulation based on machine learning

The next-generation of networks should have better spectrum and energy efficiency. These competing design parameters can be dealt with spatial modulation (SM). Transmit antenna selection (TAS) strategies can enhance the average bit error rate (ABER) performance of SM. The purpose of this research is to examine the employment of machine learning (ML)-based TAS schemes for SM. The conventional Euclidean distance-based antenna selection (EDAS) scheme offers optimal performance at the expense of computational complexity. This paper discusses the application of four different ML algorithms to SM-TAS: Support vector machines (SVM), K-nearest neighbour (KNN), decision tree (DT), and naïve Bayes (NB). With reduced complexity, an SVM-based TAS scheme for SM can offer a minimum gain of ∼3.4 dB over SM with no transmit antenna selection (SM-NTAS). Furthermore, the ABER performance of all schemes is tested with a higher modulation order and a number of transmit antennas. In every scenario, ML-based TAS schemes outperform SM-NTAS in terms of ABER performance.

Advanced nonlinearity equalizer with TC-NL-MLSE for transmitting beyond 200-Gb/s PAM-8 in IM/DD systems.

The severe band-limited effect resulted from the low-cost optical transceiver increases the channel memory length and the number of taps of the equalizers. Besides, the interaction of fiber dispersion and square-law detection introduce nonlinear distortions in intensity modulation and direct-detection (IM/DD) transmission systems. The serious band-limited effect and nonlinear distortions degrade the transmission performance and bring challenges to current equalizers for low-complexity implementation. In this paper, we propose a trellis-compression nonlinear maximum likelihood sequence estimation (TC-NL-MLSE) algorithm to compensate the linear and nonlinear distortions with lower complexity. In the TC-NL-MLSE, we introduce a polynomial nonlinear filter (PNLF) to partly compensate both the linear distortions and nonlinear distortions. Then, we establish a look-up-table (LUT) to calculate the nonlinear branch metric (BM). To simplify the calculation, two or three levels with the highest probabilities are selected according to decision thresholds for each symbol to compress the state-trellis graph (STG). This significantly reduces computational complexity on BM calculations especially for high-order modulations. We conduct experiments to transmit beyond the 200-Gb/s PAM-8 signal over 2-km standard single mode fiber (SSMF) at C-band. The TC-NL-MLSE outperforms the reduced-state MLSE with PNLF, and can reach the 7%-overhead hard-decision forward error correction threshold. Moreover, the TC-NL-MLSE reduces the complexity by 97% compared with standard LUT-MLSE, limiting the multipliers around 100 at the expense of only 0.2-dB receiver sensitivity penalty.

Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM

We experimentally demonstrate a novel size reduction approach for symbol-based look-up table (LUT) digital predistortion (DPD) of the transmitter impairments taking advantage of the periodicity in the pattern-dependent distortions. Compared to other reduced-size LUT schemes, the proposed method can significantly lessen the storage memory requirements with negligible performance penalty for high-order modulation formats. To further alleviate the storage memory restriction, a twice reduced-size LUT scheme is proposed to provide further size reduction. Importantly, given a targeted memory length, we verify the importance of averaging over sufficient occurrences of the patterns to obtain a well-performing LUT. Moreover, it is necessary to evaluate the performance of LUT-based DPD using random data. Finally, we demonstrate a neural network (NN) based nonlinear predistortion technique, which achieves nearly identical performance to the full-size LUT for all employed constellations and is robust against a change of modulation format. The proposed techniques are verified in a back-to-back transmission experiment of 20 Gbaud 64-QAM, 256-QAM, and 1024-QAM signals considering 3 and 5 symbol memory. The performance of the LUT-based DPD is further validated in a noise loading experiment.

Research on Performance of Cooperative FSO Communication System Based on Hierarchical Modulation and Physical Layer Network Code

To solve the problem that the channel conditions in asymmetric cooperative FSO communication systems are not fully utilized, and the data reliability deteriorates due to high-order modulation, we proposed a layered modulation, joint physical-layer network coding scheme. In this scheme, we first designate the data priority of the information to be transmitted at the source node. Then, the transmission power of different proportions is allocated to the data based on its priority. Then, the modulated data is sent to each node, and physical-layer network coding is performed on the received data at the relay node. Finally, the relay node sends the encoded information to the destination node, and the destination node recovers the original information using the physical-layer network coding scheme. The simulation results showed that when the average signal-to-noise ratio of the channel was 15 dB, the BER of the cooperative FSO communication system could be reduced to below . In the strong atmospheric turbulence channel, the cooperative FSO communication system can obtain a signal-to-noise ratio gain of about 1.5 dB. Under strong atmospheric turbulence, this scheme could also improve the average channel capacity performance of a cooperative FSO communication system.

Higher-order modulations in the skyrmion lattice phase of Cu2OSeO3

Using small angle neutron scattering, we have investigated higher-order peaks in the skyrmion-lattice phase of Cu$_2$OSeO$_3$, in which two different skyrmion lattices, SkX1 and SkX2, are known to form. For each skyrmion-lattice phase, we observed two sets of symmetrically inequivalent peaks at the higher-order-reflection positions with the indices $(110)$ and $(200)$. Under the condition where the SkX1 and SkX2 coexist, we confirmed the absence of the scattering at $\mathbf{Q}$ positions combining reflections from the two phases, indicating a significantly weak double-scattering component. Detailed analysis of the peak profile, as well as the temperature and magnetic-field dependence of the peak intensity, also supports the intrinsic higher-order modulation rather than the parasitic double scattering. The two higher-order modulations show contrasting magnetic-field dependence; the former $(110)$ increases as the field is increased, whereas the latter $(200)$ decreases. This indicates that, in Cu$_2$OSeO$_3$, skyrmions are weakly distorted, and the distortion is field-dependent in a way that the dominant higher-order modulation switches from $(110)$ to $(200)$ under field. Monte Carlo simulations under sweeping external magnetic field qualitatively reproduce the observed magnetic-field dependence, and suggests that the higher-order modulations correspond to the superlattices of weak swirlings appearing in the middle of the original triangular-latticed skyrmions.

A Novel Blind High-Order Modulation Classifier Using Accumulated Constellation Temporal Convolution for OSTBC-OFDM Systems

Deep learning (DL)-based blind modulation classification (BMC) has flourished in the single carrier system for low-order modulation signals. However, the research of DL-based BMC has not been well explored in the OSTBC-OFDM system (Orthogonal Space-Time Block Coded-Orthogonal Frequency Division Multiplexing), especially high-order modulation types. This brief proposes a novel BMC algorithm for high-order modulation signals (e.g., 4096QAM), which is based on reconstruction processing and accumulation constellation temporal convolution BMC network (ACTC-BMCNet). First, we employ the blind zero-forcing (ZF) equalization algorithm to reconstruct the damaged signal and enhance the signal representation power. Then, the constellation accumulation strategy is executed on the raw constellation diagram (CD) to generate the accumulated CD (ACD) image for reducing constellation dispersion. After that, instead of traditional 2D convolution, the constellation temporal convolution enabled in ACTC-BMCNet is leveraged to reduce computational overhead and extract the more discriminative feature. Monte-Carlo experiments are performed on fourteen modulation signals, and results indicate that the proposed ACTC-BMCNet possesses a superior classification accuracy and a faster recognition speed than the DL-based benchmark classifiers.

RC-Struct: A Structure-Based Neural Network Approach for MIMO-OFDM Detection

In this paper, we introduce a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier leverages the repetitive constellation structure in the system to perform multi-class detection. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. Experiments show that the introduced RC-Struct outperforms both the conventional model-based symbol detection approaches and the state-of-the-art learning-based strategies in terms of bit error rate (BER). The advantages of RC-Struct over existing methods become more significant when rank and link adaptation are adopted. The introduced RC-Struct sheds light on combining communication domain knowledge and learning-based receive processing for 5G/5G-Advanced and Beyond.

Parallel Distribution Matcher Base on CCDM for Probabilistic Amplitude Shaping in Coherent Optical Fiber Communication

As a typical high-order modulation format optimization technology, constellation probability shaping enhances generalized mutual information (GMI) by optimizing the probability distribution of each constellation point of the signal. It can improve the transmission capacity of the same order M Quadrature Amplitude Modulation (QAM) signal under the condition of limited average transmission power, and further narrow the gap with the Shannon limit capacity. The distribution matcher is a key part of constellation probability shaping since it not only ensures the one-to-one mapping of input and output sequences but also realizes the function of probability shaping. The constant composition distribution matcher (CCDM) structure is a widely utilized distribution matcher in the current probability shaping technology. Based on CCDM, a parallel distribution matcher scheme is proposed in this paper. It has a lower rate loss than CCDM for short output lengths (n is less than 100). Block lengths can be reduced by up to 30% with the same rate loss. When the GMI is the same as for the probability shaping (PS) 64QAM signal using CCDM, the OSNR required by the PS-64QAM signal using this scheme can be enhanced by 0.12dB, the block length can be reduced by 40%, and the transmission distance in a standard single-mode fiber can be slightly extended.

Parity-time-symmetric optoelectronic oscillator based on higher-order optical modulation.

An optoelectronic oscillator (OEO) for single-frequency microwave generation, enabled by broken parity time (PT) symmetry based on higher-order modulation using a Mach-Zehnder modulator, is proposed and demonstrated. Instead of using two physically separated mutually coupled loops with balanced gain and loss, the PT symmetry is realized using a single physical loop to implement two equivalent loops with the gain loop formed by the beating between the optical carrier and the ±1st-order sidebands and the loss loop formed by the beating between the ±1st-order sidebands and the ±2nd-order sidebands at a photodetector. The gain and loss coefficients are made identical in magnitude by controlling the incident light power to the modulator and the modulator bias voltage. Once the gain/loss coefficient is greater than the coupling coefficient, the PT symmetry is broken, and a single-frequency oscillation without using an ultra-narrow passband filter is achieved. The approach is evaluated experimentally. For an OEO with a loop length of 10.1 km, a single-frequency microwave signal at 9.997 GHz with a 55-dB sidemode suppression ratio and -142-dBc/Hz phase noise at a 10-kHz offset frequency is generated. No mode hopping is observed during a 5-hour measurement period.

Performance-Enhanced Optical Non-Orthogonal Multiple Access Enabled by Orthogonal Chirp Division Multiplexing

In this paper, we propose a novel optical non-orthogonal multiple access (NOMA) scheme based on orthogonal chirp division multiplexing (OCDM) in a low-cost intensity modulation and direct detection system for passive optical networks (PON). The proposed OCDM-NOMA scheme can effectively improve spectral efficiency and serve more users by multiplexing spectrally overlaid OCDM signals in power domain. Under the advantages of the chirp spread spectrum, the proposed OCDM-NOMA scheme can also enhance the bit error rate (BER) performance compared to NOMA scheme based on orthogonal frequency division multiplexing (OFDM). A 17.8 Gb/s OCDM-NOMA PON transmission over 25 km standard single-mode fiber is experimentally demonstrated. The results show that, when compared to OFDM-NOMA and precoding OFDM-NOMA, OCDM-NOMA can achieve receiver sensitivity gains of 1.3 dB and 0.5 dB for the high-power branch signal, and 1.4 dB and 0.6 dB for the low-power branch signal at a BER of 3.8×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . Furthermore, we investigate the feasibility of using higher-order modulation in NOMA technology for the first time to further improve data throughput and spectral efficiency. In a 22.2 Gb/s transmission, the optimal power division ratio is determined, and the transmission performance is investigated. According to the experimental results, OCDM-NOMA with higher-order modulation can be a promising candidate for next-generation PONs.

Exploiting Multiple RISs and Direct Link for Performance Enhancement of Wireless Systems With Hardware Impairments

In this paper, the performance of a wireless system, which is beneficial from utilization of multiple reconfigurable intelligent surfaces (RISs) and the direct link between the source node and the mobile user, is analyzed. Especially, the effect of transceiver hardware impairments (HIs) are taken into consideration. We successfully derive the outage probability (OP), throughput, achievable data rate (ADR), and symbol error probability (SEP) expressions of this multiple-RIS assisted wireless systems with and without HIs (referred as RIS-HI and RIS-ID system, respectively) over Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channels. The above performance factors are also benchmarked with the case that only a point-to-point (P2P) link without RIS is considered (denotes as as P2P-HI and P2P-ID systems for HI and ideal hardware conditions, respectively). All obtained formulas are verified by Monte-Carlo simulations. It is observed that the RIS-HI system significantly outperforms the P2P-HI system at low transmit power regime, while the performance of the RIS-ID system is always better than that of P2P-ID system. On the other hand, HIs show more severe effect for higher data transmission rates or high modulation orders.

IRS-Assisted Physical Layer Network Coding Over Two-Way Relay Fading Channels

This article investigates the performance of intelligent reflective surfaces (IRS)-aided physical layer network coding (PNC) in two-way relaying channels (TWRC). Specifically, IRS is used to eliminate carrier phase offset (CPO) at the relay node. To this end, the IRS reflectors’ phase shifts are optimized to align the received signals from two source nodes at the relay. This facilitates using a simple mapping function at the relay to map the superimposed signal to a network-coded signal. Two scenarios are considered, the first of which assumes that each source node is served by a separate IRS panel, while the second scenario considers the more challenging case where only one IRS panel is available for the two source nodes. In the latter case, the IRS panel is seen by both source nodes and its phase shifts are optimized to mitigate the CPO problem while maximizing the received signal amplitude at the relay. This optimization problem is formulated and solved over the complex circle manifold. Finally, we extend the IRS-assisted PNC system to include channel coding and higher modulation orders, for which a repeat accumulate (RA) channel-coded IRS-aided PNC scheme is proposed for general quadrature amplitude modulation (QAM) signals. A belief propagation (BP) based algorithm is designed to decode the network-coded sequences over a q-Ring using modular arithmetic. Our simulation results validate the theoretical error expressions derived for the two-IRS scenario as well as the efficacy of the proposed manifold optimization approach for the one-IRS scenario. The results also confirm the efficacy of the designed channel-coded IRS-aided PNC using high QAM modulation orders.

Joint Precoder and Beamformer Design for Secure Relay Networks With Finite-Alphabet Inputs and Statistical CSI of Eve

Because of discrepant deteriorations of the intended receiver and the unintended receiver, artificial noise (AN) can be invoked in conjunction with the precoder to wireless transmissions for enhancing the secrecy rate (SR) performance as long as we elaborately frame them. This paper studies a secure transmission strategy by jointly designing the precoder and AN beamformer at the relay network, where a passive eavesdropper and finite-alphabet inputs are taken into account. We propose a pair of solutions for low-order modulation and high-order modulation, respectively. To solve the first optimization problem, we propose a low-complexity algorithm with the aid of the invoked cut-off rate. Interestingly, we find that the phase of an optimum precoder for maximizing the SR has a correlation to both the channels spanning from the transmitter to the relays and spanning from the relays to the legitimate receiver. Furthermore, we reveal that the AN beamformer vector has at most one non-zero component, which only locates at the position corresponding to the minimum element of the channel between the relays and the intended receiver. According to these findings, the SR maximization problem over the two vectors is simplified as one only related to a pair of scalars. As for the high-order modulation, a new solution is further proposed for circumventing the predicament that the computational complexity exponentially increases as the size of the adopted modulation increases, where we not only eliminate two-layer summation over the legitimate symbols but also conceive a concave maximization SR problem. Finally, simulation results demonstrate the efficiency of the proposed algorithms in terms of the SR performance.

Blind nonlinearity equalization by machine-learning-based clustering for QAM-based quantum noise stream cipher transmission

In quantum noise stream cipher (QNSC) systems, it is difficult to compensate fiber nonlinearity by digital signal processing (DSP) due to interactions between chromatic dispersion (CD), amplified spontaneous emission (ASE) noise from erbium-doped fiber amplifier (EDFA) and Kerr nonlinearity. Nonlinearity equalizer (NLE) based on machine learning (ML) algorithms have been extensively studied. However, most NLE based on supervised ML algorithms have high training overhead and computation complexity. In addition, the performance of these algorithms have a lot of randomness. This paper proposes two clustering algorithms based on Fuzzy-logic C-Means Clustering (FLC) to compensate the fiber nonlinearity in quadrature amplitude modulation (QAM)-based QNSC system, including FLC based on subtractive clustering (SC) and annealing evolution (AE) algorithm. The performance of FLC-SC and FLC-AE are evaluated through simulation and experiment. The proposed algorithms can promptly obtain suitable initial centroids and choose optimal initial centroids of the clusters to achieve the global optimal initial centroids especially for high order modulation scheme. In the simulation, different parameter configurations are considered, including fiber length, optical signal-to-noise ratio (OSNR), clipping ratio and resolution of digital to analog converter (DAC). Furthermore, we measure the Q-factor of transmission signal with different launched powers, DAC resolution and laser linewidth in the optical back-to-back (BTB) experiment with 80-km single mode fiber. Both simulation and experimental results show that the proposed techniques can greatly mitigate the signal impairments.

Transmission of OCDM Based on Asymmetric Pair Mapping Over a Seven Core Fiber

In this paper, transmission of orthogonal chirp division multiplexing (OCDM) based on asymmetric pair mapping over a seven core fiber is proposed. High-order 32 quadrature amplitude modulation (QAM) is reduced to quadrature phase-shift keying (QPSK) and 8 QAM signal employing asymmetric pair mapping, which will not affect the rate of the system. Combined with geometric shaping (GS), the average transmission power is successfully lowered, realizing signal damage suppression. In order to verify the performance of the proposed asymmetric pair mapping scheme, a 140 Gb/s asymmetric pair mapping OCDM signal transmission over 2 km seven core fiber is experimentally demonstrated. The experimental results show that the proposed transmission scheme has the best performance when the power distribution ratio (PDR) is 4 dB. Meanwhile, compared with GS 32 QAM with QPSK+rectangle 8 QAM, GS 32 QAM with QPSK+star 8 QAM and GS 32 QAM, the proposed asymmetric pair mapping OCDM signal has improved by 1.55 dB, 1.45 dB, and 0.7 dB, respectively. It has a wide range of applications in the future optical transmission system due to its outstanding performance in the suppression of high-order modulation signal degradation.

Low complexity blind detection in OFDM systems with phase noise

Phase noise is one of the major impairments that degrade the performance of orthogonal frequency division multiplexing (OFDM) systems. When an OFDM system works at high-order modulation or high carrier frequency, phase noise's impairments cannot be ignored. Existing phase noise estimation algorithms either require a large number of pilots or have high computation complexity. In this paper, we propose a low complexity blind data detector based on the maximum a posteriori (MAP) criterion. With prior knowledge of slow time-varying property of phase noise, a simplified iterative phase noise estimation and data detection algorithm is developed. Compared to existing algorithms, the proposed algorithm does not require pilot data transmission, and achieves satisfactory performance with low computation complexity. Simulation results validate the effectiveness of proposed algorithm.

Low-complexity and blind receiver in-phase/quadrature imbalances compensation and estimation in the frequency domain for high-order modulation formats.

A frequency domain (FD) 4×2 multi-input and multi-output (MIMO) equalizer based on radially directed equalizer is proposed to compensate receiver in-phase/quadrature (IQ) imbalances of M-ary quadrature amplitude modulation signals. This algorithm has a significantly lower complexity compared with a conventional time-domain 4×2 MIMO equalizer. Furthermore, each of imperfection estimations is derived from the converged discrete frequency response of the FD 4×2 MIMO equalizer. The simulation and experimental results indicated that the receiver (Rx) IQ imbalances were fully compensated by the proposed equalizer and precisely estimated by estimators, even for long-haul transmission with Rx IQ imbalances varying over a wide range.