Currently, two primary types of integrated communication and sensing systems exist, which are forward optical sensing and backward reflected optical sensing. The former faces limitations in sensing signal accuracy, the latter encounters nonlinear effects like four-wave mixing when communication and sensing signals occupy separate bands in dense wavelength division multiplexing systems. When both communication and sensing signals share the same frequency band, crosstalk becomes a significant issue. To maintain communication quality, higher input power is often utilized, leading to increased power consumption and nonlinear noise generation. To address these challenges, a noise equalizer is proposed, based on optical reservoir computing (PhRC) chips. The majority of computations are efficiently executed using silicon-based integrated optical devices, offering the dual advantages of reduced latency and expanded bandwidth. This innovative approach holds promise in enhancing the performance and reliability of integrated communication and sensing systems in modern optical fiber networks.This article employs numerical simulation techniques to establish an integrated Rayleigh sensing and 56 Gb/s pulse amplitude modulation (PAM4) communication system (Fig.1). Both the communication and sensing signals utilize a common wavelength of 1 550 nm. The sensing signal uses linear frequency modulation (LFM) modulation pulse with a pulse duration of 248 μs. Initially, the sensing pulse is modulated using a modulator. Subsequently, an optical bandpass filter converts the signal into a single-sideband signal. Then, the PAM4 communication signal is modulated using the modulator and transmitted through an optical fiber. At the optical circulator, sensing signals are detected, while the receiver captures communication signals contaminated by crosstalk and noise. The simulation of the PhRC chip, weight training, as well as the computation of bit error rate (BER) and symbol error rate (SER), were executed using Python (Fig.2). Distorted communication signals are fed into the PhRC to achieve noise equalization.The weight of the PhRC optical chip noise equalizer is trainable, enabling adaptive equalization for multiple parameters. Under a 20 km fiber condition, the scheme explores the BER equalization capability at incident powers ranging from 5 to 19 dBm. There is a three-order-of-magnitude difference in the BER and SER between the signals before and after equalization with 7 dBm incident power. At an incident power of 10 dBm, the equalization performance is discussed for fiber transmission distances of 15-24 km, a four-order-of-magnitude improvement in BER and SER with 19 km fiber. When using a 20 km fiber and 10 dBm incident power, the scheme is also evaluated for its ability to improve the BER of communication signals affected by 0.5-2.0 GHz LMF sensing pulses. There is a three-order-of-magnitude difference in the BER between the signals before and after equalization at 1.9 G bandwidth of LMF signal. Simulation results indicate that this scheme maintains stable equalization capabilities even when channel conditions change, especially when the input power decreases.This article proposes a signal processing scheme for a PhRC chip-based communication and sensing system, utilizing optical computing to achieve noise equalization for a fiber Raman sensing and 56 Gb/s PAM4 communication integrated system. The PhRC chip is capable of equalizing impairments caused by different incident fiber powers, fiber lengths, and LMF bandwidths. It offers advantages such as high integration, large computational bandwidth, low processing delay, and low computational power consumption. Compared to unequalized signals, the equalized signals achieved a three-order-of-magnitude improvement in BER at a lower incident fiber power of 7 dBm, eliminating the need for optimal higher incident power 16 dBm. The scheme supports fiber transmission lengths of 15-24 km and 0.5-2.0 G LFM sensing pulses, achieving over a two-order-of-magnitude improvement in SER and BER. This communication noise processing scheme can achieve low-power and high-quality signal noise recovery, providing a solution for the future development of integrated optical communication and sensing systems.