概括
一个新的并行决策深度神经网络 (DNN) 均衡器通过同时处理多个符号来改善高速光学传输. 这种无反的设计可以在减少硬件资源的情况下提供更快的培训和竞争性表现.
科学领域:
- 光学通信是指光学通信.
- 数字信号处理 数字信号处理
- 机器学习 机器学习
背景情况:
- 高速光学线缆传输在很大程度上依赖于有效的均等.
- 由于反路的时间限制,传统的等分器可能面临处理速度限制.
- 深度神经网络 (DNN) 为无反信号提供了一个有前途的数字信号处理方法.
研究的目的:
- 为高速光学传输提出一个资源高效的DNN等分器架构.
- 为了研究一个平行决策DNN,减少硬件复杂性.
- 评估拟议的均衡器的性能和培训趋同.
主要方法:
- 开发了一个并行决策DNN架构,用硬决策层取代软-max层.
- 这种设计可以在单个神经网络中实现多符号处理.
- 神经元增量与层数线性地扩大,与重复方法不同.
主要成果:
- 拟议的并行决策DNN显示了与传统均衡器 (例如,15点的前均衡器与2点的决策反均衡器) 相比的竞争性性能.
- 实现了28GBd和56GBd的性能,具有四级脉冲振幅调制和30dB的损失.
- 与传统同行相比,表现出明显更快的培训趋同.
- 基于前向错误纠正的自适应机制被探索.
结论:
- 平行决策DNN等分器是高速光学传输的高效架构.
- 它在性能,硬件资源和训练速度之间提供了有利的权衡.
- 这种方法推进了光通信系统的无反信号传输.
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