计算机科学
无源光网络
人工神经网络
自适应均衡器
均衡(音频)
发射机
数字信号处理
信号处理
电力预算
电子工程
电信
功率(物理)
光学
波分复用
解码方法
人工智能
物理
功率控制
量子力学
计算机硬件
工程类
波长
频道(广播)
作者
Luyao Huang,Wenqing Jiang,Yongxing Xu,Weisheng Hu,Lilin Yi
出处
期刊:Optics Letters
[The Optical Society]
日期:2022-10-27
卷期号:47 (21): 5692-5692
被引量:5
摘要
One of the most promising solutions for 100 Gb/s line-rate passive optical networks (PONs) is intensity modulation and direct detection (IMDD) technology together with a digital signal processing- (DSP-) based equalizer for its advantages of system simplicity, cost-effectiveness, and energy-efficiency. However, due to restricted hardware resources, the effective neural network (NN) equalizer and Volterra nonlinear equalizer (VNLE) have the drawback of high implementation complexity. In this paper, we incorporate an NN with the physical principles of a VNLE to construct a white-box low-complexity Volterra-inspired neural network (VINN) equalizer. This equalizer has better performance than a VNLE at the same complexity and attains similar performance with much lower complexity than a VNLE with optimized structural hyperparameter. The effectiveness of the proposed equalizer is verified in 1310 nm band-limited IMDD PON systems. A 30.5-dB power budget is achieved with the 10-G-class transmitter.
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