Timing Recovery for Point-to-Multi-Point Coherent Passive Optical Networks

We propose a timing recovery for point-to-multi-point coherent passive optical networks. The results show that the proposed algorithm has low complexity and better robustness against the residual chromatic dispersion.Comment: The artical have been submitted to SPPCom conferenc

Capacity Limitation and Optimization Strategy for Flexible Point-to-Multi-Point Optical Networks

Point-to-multi-point (PtMP) optical networks become the main solutions for network-edge applications such as passive optical networks and radio access networks. Entropy-loading digital subcarrier multiplexing (DSCM) is the core technology to achieve low latency and approach high capacity for flexible PtMP optical networks. However, the high peak-to-average power ratio of the entropy-loading DSCM signal limits the power budget and restricts the capacity, which can be reduced effectively by clipping operation. In this paper, we derive the theoretical capacity limitation of the flexible PtMP optical networks based on the entropy-loading DSCM signal. Meanwhile, an optimal clipping ratio for the clipping operation is acquired to approach the highest capacity limitation. Based on an accurate clipping-noise model under the optimal clipping ratio, we establish a three-dimensional look-up table for bit-error ratio, spectral efficiency, and link loss. Based on the three-dimensional look-up table, an optimization strategy is proposed to acquire optimal spectral efficiencies for achieving a higher capacity of the flexible PtMP optical networks.Comment: The paper has been submitted to the IEEE Transactions on Communication

Flexible Coherent Optical Access: Architectures, Algorithms, and Demonstrations

To cope with the explosive bandwidth demand, significant progress has been made in the ITU-T standardization sector to define a higher-speed passive optical network (PON) with a 50Gb/s line rate. Recently, 50G PON becomes mature gradually, which means it is time to discuss beyond 50G PON. For ensuring an acceptable optical power budget, beyond 50G PON will potentially use coherent technologies, which can simultaneously promote the applications of flexible multiple access such as time/frequency-domain multiple access (TFDMA). In this paper, we will introduce the architectures, algorithms, and demonstrations for TFDMA-based coherent PON. The system architectures based on an ultra-simple coherent transceiver and specific signal spectra are designed to greatly reduce the cost of ONUs. Meanwhile, fast and low-complexity digital signal processing (DSP) algorithms are proposed for dealing with upstream and downstream signals. Based on the architectures and algorithms, we experimentally demonstrate the first real-time TFDMA-based coherent PON, which can support at most 256 end users, and peak line rates of 100Gb/s and 200Gb/s in the upstream and downstream scenarios, respectively. In conclusion, the proposed technologies for the coherent PON make it more possible to be applied in the future beyond 50G PON.Comment: The paper has been submitted to the Journal of Lightwave Technolog

Construction of a cross-species cell landscape at single-cell level.

Individual cells are basic units of life. Despite extensive efforts to characterize the cellular heterogeneity of different organisms, cross-species comparisons of landscape dynamics have not been achieved. Here, we applied single-cell RNA sequencing (scRNA-seq) to map organism-level cell landscapes at multiple life stages for mice, zebrafish and Drosophila. By integrating the comprehensive dataset of > 2.6 million single cells, we constructed a cross-species cell landscape and identified signatures and common pathways that changed throughout the life span. We identified structural inflammation and mitochondrial dysfunction as the most common hallmarks of organism aging, and found that pharmacological activation of mitochondrial metabolism alleviated aging phenotypes in mice. The cross-species cell landscape with other published datasets were stored in an integrated online portal-Cell Landscape. Our work provides a valuable resource for studying lineage development, maturation and aging

Optimization Algorithms of Neural Networks for Traditional Time-Domain Equalizer in Optical Communications

Neural networks (NNs) have been successfully applied to channel equalization for optical communications. In optical fiber communications, the linear equalizer and the nonlinear equalizer with traditional structures might be more appropriate than NNs for performing real-time digital signal processing, owing to its much lower computational complexity. However, the optimization algorithms of NNs are useful in many optimization problems. In this paper, we propose and evaluate the tap estimation schemes for the equalizer with traditional structures in optical fiber communications using the optimization algorithms commonly used in the NNs. The experimental results show that adaptive moment estimation algorithm and batch gradient descent method perform well in the tap estimation of equalizer. In conclusion, the optimization algorithms of NNs are useful in the tap estimation of equalizer with traditional structures in optical communications

Low Overhead and Application-Oriented Synchronization in Heterogeneous Internet of Things Systems

Recent evolution in the Internet of Things (IoT) and Cyber–physical systems (CPS) is expected to change everyday life of its users by enabling low latency and reliable communication, coordinated task execution and real time data processing among pervasive intelligence through the communication network. Precise time synchronization, as a prerequisite for a chronological ordering of information or synchronous execution, has become a vital constituent for many time-sensitive applications. On one hand, Internet of Things (IoT) systems rely heavily on the temporal coherence among its distributed constituents during data fusion and analysis, however the existing solutions for data synchronization, do not easily tailor to resource-constrained scenarios. On the other hand, timestamping accuracy is of the utmost importance to achieve accurate time synchronization of large-scale connected systems, however the heterogeneity and complexity inherent to Internet of Things (IoT) systems lead to multi-source timestamping uncertainties and significantly deteriorate performance of traditional inflexible synchronization methods. Therefore, this thesis aims at solving these challenges by proposing a low overhead and application-oriented synchronization in heterogeneous IoT systems. First, a low-overhead data synchronization scheme is proposed to achieve accurate temporal consistency prior to fusing the massive data collected from the distributed IoT devices. More specifically, a task period is scheduled for each sensor device to deliver the sampled data to Sink Node (SN). By comparing the difference between the predefined period and the real observed one, the clock parameters can be estimated accurately so that the misalignment of the data can be compensated accordingly. Simulation results show that the proposed scheme can enhance the data fusion accuracy to tens of microseconds with significantly reduced network overhead by up to 90%. Next, a situation-aware hybrid time synchronization protocol is designed based on multi-source timestamping uncertainty modeling and integrated time information exchange mechanism for heterogeneous IoT systems. More specifically, the multi-source timestamping error inherent to the overall synchronization process are accurately modeled by exploring the impact of the multi-faceted operating conditions. By analyzing the real-time timestamping uncertainties, a hybrid time synchronization scheme is actualized, which can achieve optimal synchronization strategy for clock parameters estimation. In addition, an integrated time information exchange mechanism is designed to reduce timestamping redundancy during time synchronization. Simulation results show that the proposed scheme can enhance the synchronization accuracy for heterogeneous operating scenarios

Socializing with Smoker and Social Smoking Behavior among Chinese Male Smokers with Low Nicotine Dependence: The Mediating Roles of Belief of Smoking Rationalization and Smoker Identity

Background: Previous studies have shown that socializing with other smokers is an essential trigger for social smoking among smokers with a low nicotine dependence. This study further explored the mediating effects of the belief of smoking rationalization and smoker identity on the relationship between socializing with smokers and social smoking behavior. Methods: A cross-sectional design was conducted. A total of 696 low-nicotine-dependent smokers in China completed questionnaires that assessed socializing with smokers, social smoking behavior, smoker identity, and the belief of smoking rationalization. The mediating roles of the belief of smoking rationalization and smoker identity on the relationship between socializing with smokers and social smoking behavior were assessed by using SPSS 23 and AMOS 23. Results: The belief of smoking rationalization, smoker identity, socializing with smokers, and social smoking behavior were significantly and positively correlated with each other. In addition, this study found an independently mediated role for smoker identity in the relationship with smoker socialization and social smoking behavior, and a sequentially mediated role for smoking rationalization and smoker identity in this relationship. Conclusion: Reducing the belief of smoking rationalization and smoker identity may be conducive to reducing social smoking behavior for low-nicotine-dependent smokers when socializing with other smokers