## 1 Introduction  
The exponential growth in data traffic in Beyond 5G (B5G) cellular networks necessitates intelligent infrastructure management solutions, as evidenced by Cisco's observation of a nearly threefold traffic increase for 5G connections compared to 4G [1]. Traditional network expansions face cost barriers and complex management issues, including inter-cell interference [2; 3]. Innovations are essential to enhance network performance without incurring disproportionate expenses [2; 4; 5; 6]. To address this, traffic offloading to mixed networks, supported by B5G technology, offers a promising solution for enhancing coverage and efficiency. Self-organizing network (SON) technology is crucial for the intelligent management of these networks, and its effectiveness depends on accurate network traffic prediction [7; 8; 9; 10]. The precise forecasting of internet traffic is crucial for optimizing real-time mobile network performance. This practice can prevent network congestion, manage traffic, and enhance offloading processes while also facilitating the prediction of traffic patterns for future network designs. Analyzing and predicting mobile internet traffic using big data are crucial to implementing SON technology effectively. This is essential for proactively managing traffic offload in mixed networks. By leveraging historical data from Milan Telecom, we can predict future network traffic and determine the number of small base stations (BSs) that need activation. We propose using a deep learning model to tackle the challenges of mobile internet traffic, which varies across space and time. Deep learning methods have shown promise in uncovering hidden patterns in vehicle traffic flows and traffic management where they have already been used to predict internet traffic flow [7; 11; 12]. With the ongoing advancements in deep learning, it is possible to solve various problems [13]. Deep learning models have successfully analyzed complex data, including traffic flows. This paper introduces advanced deep learning models designed to address the complexities of predicting and managing network traffic in B5G networks. We investigate how these models can improve network traffic prediction by integrating multiple data sources, aiming to contribute significantly to cellular network management through advanced computational techniques. These techniques employ a data fusion approach to overcome the limitations of single-source analysis. However, integrating diverse data sources can present significant challenges. Recent advancements in transformer-based architectures offer powerful tools for understanding intricate data dependencies, making them ideal for this task [10; 14; 15; 16]. Building upon our previous research [13], this study introduces a transformer-enhanced CNN-RNN (TE-CNN-RNN) that focuses on INT data analysis and surpasses previous methods by harnessing the power of transformers for refined feature extraction. Also, it introduces a multi-source CNN-RNN (MSCR) that incorporates diverse data sources to provide a more comprehensive analysis of network traffic patterns. Our models leverage the latest transformer advancements, convolutional and recurrent layers, and multi-source data integration, reflecting recent trends in deep learning. This approach positions our work at the forefront of B5G network traffic management and demonstrates the transformative potential of integrating transformer networks and data fusion for improved predictive accuracy. The contributions of this paper are as follows:  
* We introduce two novel deep learning models specifically tailored for B5G cell site management:
* Transformer-enhanced CNN-RNN (TE-CNN-RNN), which demonstrates the significant impact of transformers, surpassing conventional CNN-RNNs as well as recent transformer-based methods like TSMixer [17] and SAMformer [18] using INT data alone. * Multi-source CNN-RNN (MSCR) leverages diverse data (periodic, weather, news, social) and a unique architecture for comprehensive traffic analysis.