# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 5 Applications of Photonic Neural Networks for Equalizing Optical Communication Signals
Among them, we can highlight investigations with a photonic integrated circuit-based erbium-doped amplifier [136], low-loss photonic integrated circuits using lithium niobate [137; 138] and silicon nitride [139; 140]. Furthermore, investigations for reconfigurable photonic devices have also been performed to realize a spatially resolving detector of amplitude and phase information [141], for partial differential equation solving [142], and for optical pulse processing [143]. Table 3 summarizes the main challenges and their respective solutions for improving the performance of future photonic neural networks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Signal** & **Data Rate** & \begin{tabular}{c} **Fiber** \\ **Length** \\ \end{tabular}  & **BER 1** & **BER 2** & **Neural Network Structure** & **Training Algorithm** & **Ref. ** \\ \hline
2018 & PAM2 & 25 Gb/s & 45 km & \(1\times 10^{-1}\) & \(7\times 10^{-3}\) & Reservoir computing & Ridge regression & [73] \\
2019 & OOK & 40 Gb/s & 15 km & \(1\times 10^{-1}\) & \(2\times 10^{-3}\) & Reservoir computing & Ridge regression & [74] \\
2021 & OOK & 32 Gb/s & 25 km & \(2\times 10^{-1}\) & \(2\times 10^{-4}\) & Reservoir computing & Ridge regression & [75] \\
2022 & OOK & 16 Gb/s & -Evaluating classical and photonic neural networks comparatively with definitive numbers is challenging because classical neural networks are far more mature, with decades of research and development. Photonic neural networks are still at a relatively early stage, with ongoing research and diverse implementations. From this perspective, we have gathered, in Table 4, a qualitative comparison highlighting the general trends and potential advantages/disadvantages of classical and photonic neural networks. It is important to emphasize that classical neural networks (operating in the electronic domain) are currently dominant for general-purpose artificial intelligence tasks due to their high accuracy, mature training methodologies, and well-established hardware and software ecosystems. On the other hand, photonic neural networks hold great promise for specific applications where speed and energy efficiency are critical, such as high-bandwidth communication, signal processing, and certain types of pattern recognition. Hybrid electronic-photonic neural network devices that combine the strengths of both domains are certainly another promising direction for future research. However, artificial intelligence technologies are a rapidly evolving field, and future advancements in materials, devices, and architectures could significantly shift the balance. Several fiber-optic communication impairments encompass linear or nonlinear forms [144]. Linear impairments include chromatic dispersion, polarization mode dispersion, and symbol timing offset. Nonlinear impairments include laser phase noise, self-phase modulation, cross-phase modulation, four-wave mixing, and nonlinear phase noise. Cross-phase modulation and four-wave mixing do not apply in single-wavelength communication systems. Furthermore, the inelastic nonlinearities of stimulated Brillouin scattering and stimulated Raman scattering can occur at certain power levels but are generally avoided in optical communications links. We can also mention additive noise introduced by optical amplifiers like erbium-doped fiber amplifiers. Among these impairments, one of the most severe is chromatic dispersion, which occurs when different wavelengths (frequencies) of light travel at different speeds through an optical fiber. It