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In optical networks, failure localization is essential to stable operation and service restoration. Several approaches have been presented to achieve accurate failure localization of nodes and inter-nodes. However, due to increasing traffic and demand for flexibility, the reconfigurable optical add/drop multiplexer (ROADM) is evolving towards a multi-degree architecture. Therefore, each ROADM is composed of multiple devices, which makes intra-node failures become more complex. In this context, intra-node failure localization can effectively reduce the pressure on network operators to further find specific devices. In this work, we redefine the intra-/inter-node failure model for multi-degree ROADM-based optical networks and propose a suspect fault screen assisted graph aggregation network (SFS-GRN) for intra-/inter-node failure localization. The SFS is responsible for screening out suspect fault devices from all devices and reducing the number of candidate devices. The GRN is used to analyze these monitoring data from an optical performance monitoring (OPM) node and network wide and to determine the most likely failure device. The proposed scheme is evaluated in a nine-node simulated network and three-node testbed network. Extensive results show that the SFS-GRN achieves higher accuracy compared with existing methods under different percentages of OPM deployment, numbers of service requests, and failure types. The SFS can remove more than 98% of devices, which is beneficial to further detection and repair for network operators. Moreover, the proposed strategy takes about 10 ms to detect a potential failure, and it has the potential to be applied to a real scenario.

In this paper, an optical transmitter authentication method using hardware fingerprints based on the characteristic of electro-optic chaos is proposed. By means of phase space reconstruction of chaotic time series generated by an electro-optic feedback loop, the largest Lyapunov exponent spectrum (LLES) is defined and used as the hardware fingerprint for secure authentication. The time division multiplexing (TDM) module and the optical temporal encryption (OTE) module are introduced to combine chaotic signal and the message to ensure the security of the fingerprint. Support vector machine (SVM) models are trained to recognize legal and illegal optical transmitters at the receiver. Simulation results show that LLES of chaos has the fingerprint characteristic and is highly sensitive to the time delay of the electro-optic feedback loop. The trained SVM models can distinguish electro-optic chaos generated by different feedback loops with a time delay difference of only 0.03ns and have a good anti-noise ability. Experimental results show that the recognition accuracy of the authentication module based on LLES can reach 98.20% for both legal and illegal transmitters. Our strategy can improve the defense ability of optical networks against active injection attacks and has high flexibility.

A programmable logic controller deployed in the cloud or edge cloud (cloud PLC) contributes to flexible and scalable industrial manufacturing processes. For the industrial Internet, a time-division multiplexing passive optical network is a promising access technology for connecting cloud PLC and industrial devices. Time-sensitive (TS) industrial applications such as motion control and mobile robotics impose strict delay and jitter requirements, pursuing deterministic transmission capability, that is, guaranteeing that the upper bounds on delay and jitter of the TS industrial flow meet its requirements. However, traditional bandwidth allocation schemes aim to leverage statistical multiplexing to improve bandwidth resource utilization efficiency, so it guarantees only the “average delay/jitter” of the service and cannot meet the deterministic requirements of the TS industrial flow. This paper presents a time-aware deterministic bandwidth allocation (TA-DetBA) scheme to satisfy the deterministic transmission of TS industrial flows. To this purpose, first, we constrain the position of the transmission window (i.e., bandwidth) allocated by optical line termination to the optical network unit according to the arrival characteristics (i.e., flow arrival time and flow cycle) and requirements (i.e., delay and jitter) of the flow. Subsequently, we propose an integer linear programming scheduling model and a greedy-based time slot equalization heuristic scheduling algorithm to improve schedulability and reduce the average delay of best-effort flows. Simulation results show that our proposed TA-DetBA scheme guarantees the deterministic delay and jitter of TS industrial flow. Compared with the fixed bandwidth allocation (FBA) scheme, the average resource utilization efficiency of the TA-DetBA scheme is improved by up to 20.6% higher than FBA.

In this paper, a novel electro-optic chaotic system based on the logistic map feedback (EOLM) is proposed. The logistic map is used to introduce additional nonlinear effects into the electro-optic feedback loop. The simulation results show that, with the increase of logistic map iterations N, the bandwidth and permutation entropy of the chaotic output can be significantly increased, and the spectrum is flatter. The time-delay signature (TDS) of the system can be concealed within the appropriate range of values of parameters, which ensures the security of the key. Synchronization results show that the system is not only sensitive to time delay T but is also sensitive to the feedback intensity β, so β is also the key of the system. Utilizing the sensitivity to β, a dynamic EOLM communication system with changing key (DEOLM) is designed. Based on chaotic self-control, the chaotic optical signal is transformed nonlinearly to generate the control signal, which drives the gain coefficient of the amplifier to change dynamically, so as to realize the changing of β. Simulation of communication performance shows that the DEOLM system greatly raises the difficulty for the eavesdropper to crack the message and improves the confidentiality of communication.

In this paper, a scheme to realize encryption and digital identity authentication at the same time is proposed for enhancing the physical-layer security of point-to-point optical links (PPOL). Exploiting identity code encrypted by the key as authentication information effectively resists passive eavesdropping attacks in fingerprint authentication. The proposed scheme theoretically realizes secure key generation and distribution (SKGD) by phase noise estimation of the optical channel and the generation of identity codes with good randomness and unpredictability by the four-dimensional (4D) hyper-chaotic system. The local laser, erbium doped fiber amplifier (EDFA), and public channel provide the entropy source of uniqueness and randomness to extract symmetric key sequences for legitimate partners. The simulation conducted in a quadrature phase shift keying (QPSK) PPOL system over 100km standard single mode fiber verify successfully that 0.95Gbit/s error-free SKGD. The unpredictability and high sensitivity to the initial value and control parameters of the 4D hyper-chaotic system provide a huge space of ~10125 for identity codes, which is sufficient to resist exhaustive attack. With the proposed scheme, the security level of key and identity can be increased markedly.

Fault location is an essential part of optical network operation and maintenance, and network operators have expectations to achieve highly accurate and precise fault location for reducing the cost of fault recovery. However, due to the scale of such networks, the volume of monitoring data (MD) is quite large, which poses a great challenge for fault location. An attention mechanism is an effective way to focus on the important information from massive input for the current task, which originates from the study of human vision. Targeting component fault location in optical networks, we propose an attention mechanism-based strategy, which consists of a sequence attention mechanism (SAT), a channel attention mechanism (CAT), a graph attention mechanism (GAT), and a fully connected neural network (FCNN). SAT, CAT, and GAT are applied for link, node, and network representation, respectively, taking corresponding MD as input. The FCNN is responsible for analyzing the correlation between MD and completing the fault location decision. All three attention mechanisms can filter out the more critical MD, assisting the FCNN to make more accurate decisions. We compare the performance of the proposed strategy and artificial neural networks (ANNs) in partial telemetry scenarios. Simulation results indicate that our strategy outperforms ANNs with respect to the accuracy of fault location by focusing on more critical MD and achieves a maximum improvement by 5.6%. Moreover, its feasibility with real data is verified on an experimental testbed consisting of hybrid optical-electrical switching nodes. Extensive results show that our strategy has the potential to achieve highly accurate fault location in real networks.

We first demonstrate a collaborative data-driven (using deep reinforcement learning) and model-driven (using experience knowledge) routing engine for cross-layer optimization in a real X-Haul testbed with a real dataset, which achieves 23% wavelength saving.

We propose core and wavelength allocation schemes of SNS-QKD for future metropolitan transmission over multicore fiber. Experiments verify that the proposed schemes can suppress noise photons up to 57.54% compared to conventional channel allocation.

An ANN-based QoT estimator with asymmetric loss function is proposed. It enables efficient assignment of modulation format, which increases network capacity compared to the model trained based on the symmetric loss function in test networks.

We propose a time-aware robust routing in OSU-based OTN through the newly designed graph sequence attention network-enabled reinforcement learning. Simulation results show > 28% OSU frame loss reduction compared to the baselines.

Understanding the effect of ocean turbulence on optical beam propagation is critical to the design and performance evaluation of underwater wireless optical communication systems. In this Letter, we propose a unified Weibull–generalized gamma distribution to characterize the laser beam irradiance fluctuations of turbulent underwater thermocline wireless optical channels. The proposed model shows an excellent agreement with the measured data under various experimental emulated channel conditions that cover turbulences induced by temperature, salinity, and air bubbles. To the best of our knowledge, this is the first model that comprehensively describes the statistics of the laser beam irradiance fluctuations in underwater wireless optical channels due to both thermohaline gradient and air bubbles.

X-Haul aims to provide a unified transport network for integrating mobile fronthaul/midhaul/backhaul. Its architecture is inherited from the IP-over-WDM paradigm, in which the upper layer is an electrical packet-switched network and the bottom layer is an optical circuit-switched network. Traffic grooming in X-Haul poses a big challenge, as the mobile traffic load has shown. As a multi-layer routing problem, traffic grooming has been well studied through some classic mathematical models, such as the auxiliary graph (AG). Recently, a new approach based on machine learning has been attracting much attention for its network routing decision making. According to previous studies, either of the two approaches has its limitations, but the combination of the two may achieve profit maximization. To this end, we propose and demonstrate, for the first time, to our knowledge, a collaborative data-driven (using machine learning) and model-driven (using experiential knowledge) intelligent routing engine (ADMIRE) for traffic grooming in an X-Haul testbed with a real mobile dataset. The principle of ADMIRE is to exploit the capability of machine learning for an accurate AG model in a dynamic network environment. We compare ADMIRE with a traditional model-driven approach (i.e., the AG), and the evaluation results show that ADMIRE can achieve good performance and a strong generalization ability. In addition, we also verify the influence of data correlation on ADMIRE.

Using network flying platforms (NFPs) as aerial relays has been emerging as a promising solution for mobile backhaul frameworks in fifth generation and beyond wireless networks. Moreover, free space optics (FSO) technology has become the alternative to radio frequency technology for backhaul links due to its advantages of a high data rate, long transmission range, and negligible interference. In an FSO-based NFP relay backhaul network, due to the fragility of FSO links, how to deploy NFPs to satisfy network traffic demands is a challenging problem. Moreover, the establishment of non-essential FSO links leads to the extra deployment of NFPs due to the limited number of transceivers carried by each NFP, which makes the NFP deployment problem more complex. To solve these issues, we jointly consider the deployment of NFPs and the formation of network topology, the targets of which are to minimize the number of deployed NFPs and network congestion. The problem is formulated as mixed-integer nonlinear programming, which is hard to solve directly, and we linearize it based on transmission range simplification. A heuristic algorithm is proposed to solve the problem for large-scale network scenarios. Simulation results show that the joint optimization scheme of NFP deployment and network topology formation surpasses the single optimization scheme, and the proposed heuristic algorithm can solve the joint optimization problem efficiently in differently scaled networks.

The emerging mobile services have imposed several new challenges on the radio access network (RAN), which stimulates its evolution from cloud-RAN to next-generation RAN (NG-RAN). In NG-RAN, baseband functions are redefined as the radio unit (RU), distributed unit (DU), and central unit (CU), which are, respectively, connected by optical front/mid/backhaul (X-Haul) networks. The DU-CU placement and lightpath provision need an elaborate strategy for allocating resources tailored to user requests. However, most existing methods fail to fully perceive network conditions and then make inappropriate solutions, which may result in over-consumption of processing and transmission resources. Therefore, we propose a reinforcement-learning-based DU-CU placement and lightpath provision strategy using an edge-enhanced graph neural network, i.e, EGNN-RL. The EGNN is leveraged to adequately exploit graph-structured features in X-Haul networks, while proximal-policy-optimization-based RL is introduced to maintain policy stability. In addition, this problem is formulated as an integer linear programming model to find the optimal solution. We validate the proposed strategy under different service types (i.e., enhanced mobile broadband, ultra-reliable low latency communication, and massive machine connections) and different numbers of services, respectively. The results show that our scheme can achieve higher resource efficiency compared with existing methods. Moreover, it also adapts to new networks with the same nodes through fine-tuning the model. This can decrease nearly 15% of the retaining time and obtain similar performance with retaining.

An optical format interconversion scheme between on-off keying (OOK) and quadrature phase shift keying (QPSK) is proposed and verified in this paper. The conversion system mainly consists of a coherent vector combiner and a reconfigurable two-dimensional (2D) vector mover. As a key element of the proposed conversion system, the 2D vector mover is implemented by a non-degenerate phase-sensitive amplifier (PSA). The operating principle and theoretical derivations of the PSA-based 2D vector mover are fully introduced. The reconfigurable transfer characteristics of the vector mover are analyzed under different parameter settings to exhibit the flexible 2D moving function. The signal constellations, eye diagrams, spectrum, error vector magnitudes, and bit error ratios are estimated and depicted to validate the proposed idea. With the input signal-to-noise ratios of 20 dB and 25 dB, error-free conversions are achieved between 50G Baud OOK and QPSK. The scheme proposed in this paper fills the lack of the one-to-one interconversion between OOK and QPSK, and has potential applications in optical interconnect nodes, across-dimensional optical transmissions, and flexible optical transceivers.

Optical transmission security has attracted much attention. In recent years, many secure optical transmission systems based on channel characteristics are proposed. However, there are many drawbacks with these systems, such as separated plaintext and key transmission, low key generation rate (KGR), insecurity when the eavesdropper has acquired the lengths of the local fibers utilized by legal parties. To solve the above problems, we propose a novel secure optical transmission system based on neural networks (NNs), which are employed to estimate channel characteristics. By training NNs locally and transmitting pseudo-keys, the proposed system can transmit the plaintext together with key, transforming the key dynamically. Moreover, since the channel characteristics for legal parties and eavesdropper are not completely identical, the NNs trained by legal parties and eavesdropper are inconsistent. Even though the eavesdropper has attained the lengths of local fibers wielded by legal parties, the NN model trained by the legal parties is still unavailable to illegal eavesdropper. The final key is generated by the trained NN and pseudo-key, so the keys generated by legal parties and eavesdropper are dissimilar. The simulation results prove the feasibility of the proposed system with the transmission distance of 100 km and the bit rate of 100 Gbps. Meanwhile, if plaintext and key have equivalent code length, the KGR of 50 Gbps for legal parties and the key disagreement rate (KDR) of 50% for illegal eavesdropper will be realized.

We propose a neural architecture search-enabled deep reinforcement learning (NAS- DRL) for RAN slice deployment in a converged optical-wireless access network. Simulation results validate its superiority in resources saving and DRL retaining times.

A secure key generation and distribution shared between the legal partners is proposed in this paper, where an error-free key generation rate of 0.95Gbit/s is successfully demonstrated by simulation over 100km Optical Fiber Link.

We propose a sample distribution matching based transfer learning approach to perform accurate QoT estimation with fewer samples. Simulation results demonstrate that the proposed method can save training samples effectively.

This paper demonstrates a proactively reconfigured edge network that can adaptively configure IP topology for dynamic service demand. Simulation result show the average utilization has improved from 32.50% to 68.98% compared traditional static network.

We propose a secure and efficient resource allocation scheme for anti-eavesdropping in MCF-based SDM-EONs. Simulation results show that our scheme significantly reduces the intercept possibility of eavesdroppers while maintaining the quality of transmission.

We designed and experimentally demonstrate a RAN slice management system which supports fault monitoring and handling. The system can not only quickly build RAN slices that meet user needs, but also can automatically and timely complete fault handling and service recovery when RAN slice resources fail.

We propose a topology-level protection scheme (TLPS) tailored to differential reliability demands for 5G/B5G network slicing. The cases of reliable slicing are numerically evaluated that TLPS shows its superiority in redundancy resources saving.

We propose an active learning strategy for regression-model-based QoT estimation to achieve higher accuracy with fewer samples. Results show that our strategy decreases 35.8% MSE on average than traditional method with the restricted sample size.

A novel security-enhanced physical layer encryption scheme based on dispersion equalization and DNA encoding is proposed. The security of encryption transmission is verified by simulations for 10GBaud 16QAM signals over 300 km fiber.

In this paper, a novel electro-optic chaotic system with enhanced nonlinearity by deep learning (ENDL) is proposed to achieve time-delay signature (TDS) elimination. A long-short term memory network (LSTM) is trained by a specially designed loss function to enhance the nonlinear effect that can hide the TDS of the system. For the first time, the trained deep learning module is put into a single feedback loop to participate in chaos generation. Simulation results show that the ENDL system can eliminate TDS and increase the bandwidth to more than 31GHz when the feedback intensity is very low (α = 4V). Moreover, the complexity of the chaotic output can be improved with permutation entropy (PE) reaching 0.9941. The synchronization result shows that the ENDL system has high sensitivity to TDS but has low sensitivity to the feedback intensity, thus the system has both high security and high robustness. This system has an uncomplicated synchronization structure and high flexibility, and it opens up a new direction for high-quality chaos generation.

A multi-stage aggregation and lightpath provisioning algorithm is proposed for geo-distributed data in EON assisted by MEC. Simulation results show the algorithm can reduce the job completion time and bandwidth consumption.

A high-availability slicing scheme is proposed to establish NG-RAN slicing with ILP model and heuristics. Simulation results show that heuristics can effectively improve the slice availability and produce solutions that approximate well the optimal ILP. © 2020 The Author(s)

We propose a distributed caching and lightpath provisioning algorithm to reduce bandwidth consumption and improve storage space utilization in MEC based EONs. Simulation results show that the algorithm can reduce blocking probability and bandwidth consumption. © 2020 The Author(s)

We propose a reinforcement learning based DU-CU placement scheme to accommodate diversified services in 5G/B5G networks. It outperforms ILP model and widely used heuristics in terms of the service-scale and resource-saving respectively.

The proposed hybrid recovery utilizes flexible optical network with reinforcement learning to recover IP fault for edge network. The testbed experiments indicate, the recovery time is 20% of rerouting-based strategy for a heavy-loaded network.

We experiment on a containerized vDU/vCU migration for load balancing among processing pools over WDM metro networks. Two stateful migration strategies to reduce migration time are verified on a converged edge access network platform.

It demonstrates a flexible optical network enabled “Network Restructuring as Traffic Changes” for 5G/B5G backhaul network, which realizes proactive cross-layer network generation and mitigation-based network recovery, powered by cognitive enhancement and decision deduction.

We experimentally demonstrate a geo-distributed data processing and aggregation (GDPA) scheme in the MEC-empowered metro optical networks. The demonstration results show that the proposed scheme can improve resource utilization and reduce average job completion time.

We demonstrate a carrying capability evaluation system, which can evaluate and predict the access network capacity and efficiency by extracting detail network status and trends from hybrid data sources based on machine learning.

We design an isolation mechanism for 5G-RAN slices over OTN/WDM metro-aggregation networks, and an isolation-aware heuristic algorithm is proposed to implement slice embedding. Simulation shows that our approach can map RAN slices efficiently. © 2020 The Author(s)

We integrate QKD system and 5G optical fronthaul based on MCF, and experimentally evaluate the performance of QKD system at different bandwidths and modulation formats of classical signals.

An optical de-aggregation scheme based on quadrature de-multiplexing and vector moving is proposed. A 10GBaud 9QAM is de-aggregated into two 10GBaud PAM3s and a 10-km short-reach transmission is performed before signal detections. © 2020 The Author(s)

In this paper, a phase-sensitive amplifier (PSA)-based two dimensional (2D)-to-one dimensional (1D) constellation reforming system is proposed and analyzed in detail. The proposed system theoretically realizes seven kinds of 10 GBaud quadrature amplitude modulation (QAM)-to-pulse amplitude modulation (PAM) conversions, including quadrature phase shift keying-to-PAM4 and 8QAM-to-PAM8 conversions. The constellation reforming system consists of a constellation squeezing PSA and a multi-level vector moving PSA. The operating principle and formula derivations of constellation squeezing and vector moving processes are fully explained, including the PSA transfer characteristics and PSA gain axis angle analytical solutions. When implementing QAM-to-PAM conversions, the constellations, spectra, eye diagrams, error vector magnitudes and bit error ratio (BER) performances of the QAM and PAM signals are measured. For 8QAM-to-PAM8 conversion, with the input OSNR of 25 dB and 30 dB, at the BER of 10−3, the converted PAM8 shows the receiver OSNR of 38.9 dB and 35.2 dB, respectively. The proposed and verified 2D-to-1D constellation reforming system builds an optical bridge connecting long-haul and short-reach networks, which can be employed in the format conversion, high-order format signal generation and shaping, and flexible information aggregation/de-aggregation.

5G radio access network (RAN) slicing has recently been proposed to adapt service provision over substrate network resources to the diversified requirements of 5G services in terms of bandwidth, computation, latency, and reliability. 5G RAN slicing allows baseband processing functions, including centralized units and distributed units, to be deployed in the substrate network according to the different service requirements. Even though existing studies have shown how to perform 5G RAN slicing to flexibly provision service according to their initial requirements, a proper dynamic RAN slice adjustment and migration strategy is also required to accommodate the highly dynamic nature of mobile traffic while maintaining lower service blocking. In this paper, we provide a traffic-prediction-based strategy for dynamic 5G RAN slice adjustment and migration in wavelength division multiplexing (WDM) metro-aggregation networks, which targets the minimization of (1) the slice degradation penalty, which accounts for both the degradation degree and slice priority, and (2) the migrated traffic during slice migration. Our numerical results show that the proposed strategy can effectively reduce the penalty and slice migration.

The ability to sense dynamic biochemical reactions and material processes is particularly crucial for a wide range of applications, such as early-stage disease diagnosis and biomedicine development. Optical microcavities-based label-free biosensors are renowned for ultrahigh sensitivities, and the detection limit has reached a single nanoparticle/molecule level. In particular, a microbubble resonator combined with an ultrahigh quality factor ( Q ) and inherent microfluidic channel is an intriguing platform for optical biosensing in an aqueous environment. In this work, an ultrahigh Q microbubble resonator-based sensor is used to characterize dynamic phase transition of a thermosensitive hydrogel. Experimentally, by monitoring resonance wavelength shift and linewidth broadening, we (for the first time to our knowledge) reveal that the refractive index is increased and light scattering is enhanced simultaneously during the hydrogel hydrophobic transition process. The platform demonstrated here paves the way to microfluidical biochemical dynamic detection and can be further adapted to investigating single-molecule kinetics.

We propose a quantum-classical access network architecture based on multicore fiber to scale up the number of quantum key distribution (QKD) users. Quantum signals are integrated with classical signals in both feeder and drop fibers to save deployment cost since access networks are cost-sensitive. To increase the secure key rate (SKR) of QKD, we show that wavelength-time division multiplexing is suitable for quantum signals, which are required to support a large number of quantum users. Also, we propose a core and wavelength assignment scheme to alleviate spontaneous Raman scattering and intercore cross talk noise, which are the main impairment sources to QKD in this quantum-classical network. Finally, we experimentally show the performance of QKD in the proposed quantum-classical access network in terms of noise and channel loss. The experiment results are consistent with our analysis. The characteristics of the SKRs prove the superiority of wavelength-time division multiplexing. The properties of spontaneous Raman scattering in the feeder and drop fibers are shown in the experiments through the quantum bit error rates in different experimental conditions, which verifies the effectiveness of the proposed core and wavelength assignment scheme.

As a promising platform for multi-functional terahertz devices, metasurface absorbers have received widespread attention in recent years. However, due to the existence of manufacturing difficulties, high cost, fragility, single or narrow absorption and other disadvantages, their application ranges are severely limited. Therefore, to effectively solve these problems, we have designed a flexible and high-precision terahertz metasurface absorber based on the micro-template assisted self-assembly method. Free from high cost, complicated process and time-consumption, the sandwich structure terahertz metasurface absorber consisting of a ceramic microspheres layer, a dielectric spacer layer, and a metal copper film is fabricated economically. On the one hand, through assembling the microspheres on the dielectric spacer in a periodic pattern arrangement, multiple resonances can be observed with a maximum absorption rate of up to 92.5% at 0.745 THz and are insensitive to the polarization of incident light. On the other hand, by attaching the microspheres to the dielectric layer in a compact configuration, 90% absorption bandwidth beyond 1.2 THz can be observed with a central frequency of 1.8 THz. The theoretical model of multiple reflection and interference is employed to explain these absorption characteristics. Considering the flexible design and high-throughput manufacturing processes, this work provides a promising platform for the development of high-efficiency and multi-functional terahertz devices.

An optical conversion node scheme for direct detection of complex modulation format is proposed to bridge long-haul transmissions and short-reach interconnects. A noisy 10G Baud quadrature phase shift keying signal is converted into a 10G Baud normal 4-level pulse amplitude modulation (PAM4) signal by the node. The conversion node is realized mainly relies on four-wave mixing-based phase-sensitive amplifiers. The power ratio and constellation shape of the converted PAM4 both can be flexibly designed based on network demands and five kinds of uniform or non-uniform PAM4s are generated to verify the shaping functionality. With the input optical signal-to-noise ratio range of (10 dB∼30 dB), the key indicators of the signals went through every part are measured, includes constellations, eye diagrams, error vector magnitudes, bit error rates, normalized impact factors of phase and amplitude. The proposed node scheme has great application potential in intermediate nodes for bridging long-haul transmissions and short-reach interconnects, hierarchical modulation and flexible constellations design for advanced format signals.