arXiv subjects
Publications and source records attributed to Minghui Shi.
We propose an AI Agent tailored for link power management in multi-band systems. In S+C+L band span-level study, the agent efficiently solves various optimization objectives. In network-wide evaluation, it delivers 689.0 Tbps gain in total allocated traffic with merely 303 average interactions per power profile.
In this paper, the quasi-constant modulus (QCM) property is analyzed and leveraged in the design of nonlinearity-tolerant four-dimensional (4D) modulation formats. Accordingly, we propose a family of QCM-based quadrature amplitude modulation (QCM-QAM) constellations with high spectral efficiencies (SEs) of 9, 11, and 13 bit/4D-sym, respectively. The quasi-constant modulus design theoretically enhances tolerance to fiber nonlinearities. Meanwhile, QCM-QAM is evaluated in an unrepeatered wavelength-division multiplexing (WDM) system over both standard single-mode fiber (SSMF) and non-zero dispersion-shifted fiber (NZDSF). Across all SEs, QCM-QAM demonstrates robust nonlinear tolerance in both SSMF and NZDSF. This is evidenced by a consistent shift of the optimal launch power toward higher values and a significant improvement in effective signal-to-noise ratio (SNR). QCM-QAM also delivers generalized mutual information (GMI) gains of 0.22, 0.09, and 0.21 bit/4D-sym in SSMF, and 0.24, 0.10, and 0.22 bit/4D-sym, in NZDSF at the optimal transmission power, corresponding to the SEs of 9, 11, and 13 bit/4D-sym. Furthermore, QCM-QAM achieves transmission reach extensions of 1.6%, 0.9%, and 1.7% in SSMF, and 1.7%, 1.5%, and 1.8% in NZDSF, respectively, for the three SE levels.
Fast and accurate waveform simulation is critical for understanding fiber channel characteristics, developing digital signal processing (DSP) technologies, optimizing optical network configurations, and advancing the optical fiber transmission system towards wideband. Deep learning (DL) has emerged as a powerful tool for waveform modeling, offering high accuracy and low complexity compared to traditional split-step Fourier method (SSFM), due to its strong nonlinear fitting capabilities and efficient parallel computation. However, most DL methods are designed for few-channel and low-rate WDM systems, leaving their scalability to wideband systems uncertain. Moreover, the lack of a standardized accuracy evaluation method and the inconsistent results between waveform errors and transmission performance errors, hinders fair comparisons of various DL schemes. In this paper, we introduce a DSP-assisted accuracy evaluation method integrated with nonlinear DSP, providing a fair benchmark for evaluating the accuracy of DL models. Using this method, we conduct a comprehensive comparison of DL schemes, ranging from simple configurations to more complex wideband setups. The feature decoupled distributed method combining with bidirectional long short-term memory achieves the better performance compared to other DL schemes. Furthermore, in scenarios with more-channel and higher-rate, the performance advantages of FDD-BiLSTM will be further improved. However, as the number of channels and symbol rates increase, the performance of FDD-BiLSTM still gradually deteriorate. We analyze these challenges from three perspectives: the more intricate linear and nonlinear effects, the higher sampling rate required for SSFM. To address these challenges, we discuss potential solutions from two aspects: incorporating more prior physical knowledge and optimizing the structure of DL models.
We utilize the Feature Decoupling Distributed (FDD) method to enhance the capability of deep learning to fit the Nonlinear Schrodinger Equation (NLSE), significantly reducing the NLSE loss compared to non decoupling model.
The creation of whole 3D objects in one shot is an ultimate goal for rapid prototyping, most notably biofabrication, where conventional methods are typically slow and apply mechanical or chemical stress on biological cells. Here, we demonstrate one-step assembly of matter to form compact 3D shapes using acoustic forces, which is enabled by the superposition of multiple holographic fields. The technique is contactless and shown to work with solid microparticles, hydrogel beads and biological cells inside standard labware. The structures can be fixed via gelation of the surrounding medium. In contrast to previous work, this approach handles matter with positive acoustic contrast and does not require opposing waves, supporting surfaces or scaffolds. We envision promising applications in tissue engineering and additive manufacturing.
Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.
We present a linear regression method for predictions on a small data set making use of a second possibly biased data set that may be much larger. Our method fits linear regressions to the two data sets while penalizing the difference between predictions made by those two models. The resulting algorithm is a shrinkage method similar to those used in small area estimation. We find a Stein-type finding for Gaussian responses: when the model has 5 or more coefficients and 10 or more error degrees of freedom, it becomes inadmissible to use only the small data set, no matter how large the bias is. We also present both plug-in and AICc-based methods to tune our penalty parameter. Most of our results use an $L_2$ penalty, but we obtain formulas for $L_1$ penalized estimates when the model is specialized to the location setting. Ordinary Stein shrinkage provides an inadmissibility result for only 3 or more coefficients, but we find that our shrinkage method typically produces much lower squared errors in as few as 5 or 10 dimensions when the bias is small and essentially equivalent squared errors when the bias is large.