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Modes power equalization based-singular value decomposition in mode division multiplexing systems for multi-hungry bandwidth applications
Optical Fiber Technology ( IF 2.7 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.yofte.2020.102389
Yousef Fazea , Angela Amphawan , Y.A. Al-Gumaei , Ahmed M. Al-Samman , Waleed Mugahed Al-Rahmi

Abstract Optical fiber tendencies are pushing for changes towards upgrading scalability, agility, and unwavering quality for multi-hungry bandwidth applications. In the quest for future proof of those multi-hungry bandwidth applications, is vital to take advantage of new multiplexing technologies as the surge of network traffic that soon will overwhelm the capacity of multimode fiber (MMF). Due to the issue of MMF modal dispersion and mode coupling that caused Intersymbol Interference (ISI) which result in bandwidth degradation and limited range of length. Thus, Mode division-multiplexing (MDM) is a significant and elegant emerging technology, which harnesses the symmetry of modes by transmitting in parallel data through different modes. This paper models and analysis novel four-mode group demultiplexing MDM-based Singular Value Decomposition (SVD) to isolate the signals and fairly distribute the power to the system sub-channels, each to their respective groups. The novel MDM based-SVD system achieved an aggregated data rate of 100 Gbit/s on wavelengths 1550.12 nm over an existed graded-index MMF length of 3000 m. The performance of the proposed system has been evaluated through channel impulse response, channel spectrum, eye diagram, and Bit-Error-Rate (BER) matrices.

中文翻译:

用于多饥饿带宽应用的模分复用系统中基于模式功率均衡的奇异值分解

摘要 光纤趋势正在推动面向多频带宽应用升级可扩展性、敏捷性和坚定不移的质量的变革。在寻求这些多频带宽应用的未来证明时,利用新的多路复用技术至关重要,因为网络流量的激增很快将淹没多模光纤 (MMF) 的容量。由于 MMF 模态色散和模式耦合问题会导致符号间干扰 (ISI),从而导致带宽退化和长度范围受限。因此,模分复用 (MDM) 是一项重要而优雅的新兴技术,它通过通过不同模式并行传输数据来利用模式的对称性。本文对新型四模式组解复用基于 MDM 的奇异值分解 (SVD) 进行建模和分析,以隔离信号并将功率公平地分配到系统子信道,每个子信道分配到各自的组。新型基于 MDM 的 SVD 系统在 3000 m 的现有渐变索引 MMF 长度上实现了 100 Gbit/s 的聚合数据速率,波长为 1550.12 nm。建议系统的性能已通过信道脉冲响应、信道频谱、眼图和误码率 (BER) 矩阵进行评估。
更新日期:2021-01-01
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