# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 5 Applications of Photonic Neural Networks for Equalizing Optical Communication Signals
In the sequence, Table 1 gathers the scientific investigations that more deeply evaluated the performance of photonic neural networks for equalizing optical communication signals. This table highlights the characteristics of the equalized signals, the structures of the photonic neural  
Figure 11: Experimental results of the photonic neural network. (**a–c**) Eye diagrams of (**a**) the input signal, (**b**) the transmitted signal without equalization, and (**c**) the transmitted signal with the optical equalization. (**d–f**) Histograms of the power levels for (**d**) the input signal, (**e**) the transmitted signal without equalization, and (**f**) the transmitted signal with the optical equalization. (**g**) Time sequences for the transmitted signal. Reproduced without modification from [PERSON]. [PERSON] et al. [80], © 2023, licensed under the terms of the OSA Open Access Publishing Agreement. networks used and their respective training algorithms, and the BER values achieved before and after equalization. From Table 1, it can be concluded that perceptron photonic neural networks perform slightly better in terms of BER, equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. The main difference between perceptron and reservoir photonic neural networks is their architectural design, which directly affects their ability to handle specific signal equalization tasks. Specifically, all weights in the perceptron neural network structure are adjusted during the training phase. On the other hand, in photonic reservoir computing neural networks, only the output layer weights are adjusted, which can be achieved through a simple linear regression, making the training process  
Figure 12: BER versus the optical input power with and without equalization with the photonic neural network (PNN) for the OFDM signal at 10 GHz. Reproduced without modification with permission from [PERSON] et al. [81]. Figure 13: (**a**) Constellation diagram measured of the OFDM signal without equalization. (**b**) Constellation diagram measured in the same scenario, but with the optical equalization performed by the photonic neural network device. Reproduced without modification with permission from [PERSON] et al. [81]. way, the perceptron architecture is more controllable, with targeted weights learned for specific tasks, than reservoir computing photonic architectures. Since the perceptron photonic neural networks have a fully trainable architecture, they can provide more targeted optimization, enabling them to handle signal distortions more effectively, which translates to better performance in terms of BER when compared to reservoir photonic neural networks. This may partially explain why the perceptron performs slightly better in terms of BER, equalizing signals transmitted over greater distances than reservoir computing, according to the results shown in Figure 10.0 However, detailed investigations comparing the performance of the perceptron with reservoir computing are still needed to answer this point better. Moreover, although there is currently a consistent body of knowledge about equalization techniques in the scientific literature [130; 131; 132; 133; 134], investigations have shown that adaptive equalizers dominate receiver power consumption [135]. Thus, one of the main advantages of photonic neural networks compared to classical neural networks for equalizing optical communication signals is their high energy efficiency [77]. Table 2 objectively summarizes the advantages and disadvantages of photonic neural networks for the application of optical communication signal equalization. Particularly, some efforts have already been directed to overcome the disadvantages of integrated photonic devices, such as high insertion loss and low flexibility.