## 1 Introduction
Furthermore, phase noise and nonlinear distortion demonstrate a coupling effect; nonetheless, current methods frequently treat them separately, overlooking their interrelations. This oversight results in diminished compensation efficiency and inadequate enhancement of system performance. Two-dimensional convolutional neural networks (2D-CNNs) have been very successful in areas like image processing and computer vision, showing strong abilities to identify features over time and space [15]. Their basic structure includes convolutional layers, pooling layers, and fully connected layers, which help in effectively handling grid-like data and finding patterns and relationships in the information. Research teams have successfully adapted 2D-CNNs for wireless communication, leading to better results in improving signal quality, lowering bit error rates (BER), and increasing data capacity [16; 17]. This work offers novel insights for concurrently tackling nonlinear distortion and phase noise in D-band RoF systems. The 2D-CNN can learn the spatial correlations and distribution characteristics of phase noise in signals by adaptively updating weight parameters, dynamically adjusting convolutional kernel weights during backpropagation optimization to effectively model and suppress phase noise. However, putting this into practice faces challenges, such as creating the right network designs and settings that fit the system's specific issues, as well as choosing the right training data and methods to improve performance and reliability when channel conditions change and device parameters vary. In recent years, a series of research achievements have been made in the field of terahertz signal transmission, and the relevant progress is shown in Table 1[18; 19; 20; 21; 22; 23]. This research presents a 2D-CNN post-processing technique grounded in spatiotemporal feature fusion. In a 4600 m experiment using a single-channel photonics-assisted terahertz wireless transmission, the traditional Digital Signal Processing (DSP) technique was first used, and then the new 2D-CNN algorithm was applied to improve the signal. The experiments showed that this new method reduced nonlinear noise better and lowered the bit error rate (BER) compared to traditional DSP algorithms, along with a detailed look at the results. A D-band RoF system using photonics-assisted technology was created, allowing for stable transmission of QPSK signals at a speed of 25 Gbaud (which is 50 Gbps) over a distance of 4600 m at a frequency of 128.75 GHz. The system used a method to create millimeter-wave signals: it takes electrical signals, changes them into optical signals with a Mach-Zehnder modulator (MZM), boosts them with a PA, converts them into high-frequency millimeter-wave signals with a photodetector (PD), and then sends them wirelessly using strong directional antennas. Experimental findings indicate that our photonics-aided millimeter-wave generating method enabled steady transmission of a 50 Gbps (25 Gbaud) QPSK signal across a distance of 4600 m at 128.75 GHz, confirming its viability for long-distance communications in the D-band. In a transmission scenario with an optical power of 8 dBm, the proposed method diminished the system BER to 5.3 \(\times\) 10\({}^{-3}\), markedly surpassing the traditional Volterra series (BER = 1.4 \(\times\) 10\({}^{-2}\)) and DNN approaches (BER = 1.3 \(\times\) 10\({}^{-2}\)) and remaining below the SD-FEC threshold of 1.56 \(\times\) 10\({}^{-2}\).