基于深度置信网络算法的CO-OFDM光传输系统信道均衡器设计
首发时间:2015-09-25
摘要:由于光纤通信系统具有高速长距离传输的能力,其被认为是解决全球急剧增长的流量需求最有前景的解决方案。然而,光纤通信系统在长距离传输时,由信道非线性效应所引起的符号间干扰限制了其传输能力。解决这一问题的一种方案是,在接收端做自适应信道均衡以抵消由信道特性所引起的非线性响应从而恢复原始信号。本文提出了一种基于深度置信网络的光信道均衡器,并且通过仿真光纤通信系统以比较和验证该设想。
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Deep Belief Network Based Channel Equalizer for Coherent Optical OFDM Transmission System
Abstract:With the capacity of high speed and long haul transmission, optical communication system has been considered to be the most promising solution for the increasing data traffic requirements around the world. Despite these advantages, it is suffering some problems that limit the full use of its capacity, such as the inter-symbol interference caused by optical channel nonlinear effects under the long distance transmission circumstance. An effective method to mitigate this bad influence is performing adaptive equalization in the receiver to compensate for the nonlinear response caused by channel dispersion and restore the originally transmitted signal. This paper propose a novel optical channel equalizer based on deep belief network, which is a branch of machine learning. The proposal is validated through an simulated optical communication system and compared with other adaptive equalization techniques.?
Keywords: Optical channel equalization Deep Belief Network Nonlinear Effects
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