In this paper, a joint equalization and decoding method based on recurrent neural networks (RNNs) is proposed for trellis coded modulation (TCM) systems. For traditional methods based on concatenated equalizers and Viterbi decoder, the information may be lost between the equalization and decoding. However, our proposed joint method can obtain the information bits directly from the received symbols and the error also can be corrected in iterations. In two-dimensional eight-level pulse amplitude modulation (2D-PAM8) links, we experimentally demonstrate that, compared with the traditional method based on Volterra filter and Viterbi decoder, the proposed joint method based on RNNs with similar complexity can achieve bit-error rate (BER) performance improvements of 1dB, 1dB, and 2dB in 87.5Gb/s, 100Gb/s, and 112.5Gb/s transmissions at 7% hard-decision forward-error-correction (HD-FEC) limit, respectively. For the 10km standard single-mode fiber (SSMF) link at 87.5Gb/s, the traditional method cannot get the BER below the 7% HD-FEC limit whereas the joint RNNs method with similar complexity can get the BER below the 7% HD-FEC limit. As the nonlinearity increases, the proposed joint method can achieve greater improvement of BER performance.

Advanced digital signal processing (DSP) is viewed as an inevitable technology to deal with nonlinear distortions in cost-sensitive optical interconnection systems. Classic methods such as equalization based on Volterra series turn out to be quite effective solutions to compensate signal impairments. In practical implementations, a great deal of efforts focus on reducing the computational complexity. Several model selection and dimensionality reduction approaches have been proposed to simplify the structure, which employ backward strategies. However, the threshold settings for these pruning approaches are environment-sensitive. In this paper, we propose a least angle regression-based nonlinear equalization method (LaNLE). We extend the non-intrusive least angle regression technique to generalized linear models. Through an incremental process by stagewise feature selection and weight fitting, we obtain the low-complexity nonlinear structure with significant feature basis. Experimental verification is performed based on 120 Gbps PAM8-modulated 850 nm VCSEL plus MMF optical link. The results show that, compared to fully-connected Volterra nonlinear equalizer structure (VNLE), up to 60.4% and 70.1% reduction in complexity can be achieved with similar BER performance in back-to-back and 100 m transmission scenarios, respectively. In addition, the LaNLE method has a complete path of segmented linear results where the performance shows stable growth with increasing feature basis. We experimentally demonstrate 225 Gbps per lane PAM-8 signal transmission over 100 m MMF in the simple and cost-effective implementation manner based on mass-production 850 nm VCSEL and proposed LaNLE method.

Long quasi-cyclic low-density parity check (QC-LDPC) codes have been chosen as one of the forward error correction (FEC) schemes in passive optical network (PON) systems benefiting from the excellent performance. About how to get the appropriate LDPC codes, some traditional methods which focus on obtaining large girth or flat approximated cycle extrinsic message degree (ACE) spectrums are proposed for the design of LDPC codes. These methods which may still make the existence of small stopping sets containing short cycles probable are not so useful for long QC-LDPC codes. Recently, the parallel vector message passing (PMP) algorithm oriented-to decreasing the number of short cycles is proved to be an effective method to optimize LDPC codes. Since ignoring short cycles with poor connectivity which can induce error floor, this method can not guarantee its effectiveness for long QC-LDPC codes. In this letter, an edge exchange optimization method based on the global properties of ACE and girth called EEA method is proposed, which tries to reduce cycles with low ACE in the order of cycle length. The numerical results prove that our EEA method can bring the improvements of error floor up to two orders of magnitude to long QC-LDPC codes and obviously outperforms PMP algorithm.

Based on Prophet-guided neural network, P-ConvLSTM is proposed for traffic prediction. Proved by multi-group real traffic data in OTN, P-ConvLSTM has high accuracy and strong generalization.

Accurate quality-of-transmission (QoT) estimation prior to lightpath deployment is essential for minimizing design margins in optical networks. Owing to their high precision and strong generalization capabilities, artificial neural networks (ANNs) have emerged as a promising approach for lightpath QoT estimation. However, focusing exclusively on prediction accuracy is inadequate for maximizing global network capacity. Conventional models employing symmetric loss functions apply identical penalties to both overestimation and underestimation errors, thereby precluding controlled bias in predictions and their impact on overall network capacity. This paper investigates the margin configuration for the whole network capacity and proposes a novel QoT estimation method with asymmetric loss functions, which jointly considers the assessment of global network capacity and gives different penalties for overestimation and underestimation. We further present an iterative search algorithm grounded in network capacity considerations to optimize the parameters of these asymmetric loss functions. Simulation results confirm that our ANN-based models facilitate efficient modulation format assignment, leading to corresponding increases in network capacity.

With the continuous emergence of the concept of 6G, the rapid development of industrial application scenarios, and the rise in demand for dedicated line services, there has been a strong and diverse demand for network capacity. The introduction of fine-granularity FlexE technology provides a new approach to addressing the diversification of Quality of Service (QoS) and the resource waste caused by large granularity (5 Gbps). However, the reduction in granularity leads to a significant increase in the number of slots, posing a serious challenge to deterministic latency assurance, especially in scenarios with dynamic fluctuations in service bandwidth. Sudden bandwidth variations can lead to service interruptions and fluctuations, resulting in uncontrolled network blocking rates. To address this issue, this paper proposes a traffic-driven proactive FlexE slot orchestration algorithm called PMFAN-GDSO. Based on this algorithm, bandwidth variations are calculated in advance based on the bandwidth change provided via the traffic prediction algorithm before service fluctuations occur. This avoids the time-consuming slot orchestration calculations and service abrupt changes. The real-world network datasets indicate that, based on our proposed algorithm, significant time savings of up to 46.8% are achieved compared to non-proactive prediction.