Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are used as equalizers for bandlimited channels with a memoryless nonlinearity. The NN-equalizers are combined with successive interference cancellation (SIC) to approach the information rates of joint detection and decoding (JDD) with considerably less complexity than JDD and other existing equalizers. Simulations for short-haul optical fiber links with square-law detection illustrate the gains.

翻译：受前向-后向算法（FBA）启发的神经网络（NN）被用作具有无记忆非线性的带限信道的均衡器。该神经网络均衡器与连续干扰消除（SIC）相结合，能以远低于联合检测解码（JDD）及其他现有均衡器的复杂度，逼近JDD的信息速率。针对采用平方律检测的短距离光纤链路的仿真结果验证了其性能优势。