## 1 Introduction  
With the rapid growth of mobile data consumption driven by smartphones and 5G networks, network congestion, communication delays, and connection failures often occur in areas with excessive traffic demand [1]. These issues may cause minor inconveniences on normal days. If numerous base stations are deployed to improve the traffic capacity, energy consumption will increase. Therefore, making accurate traffic predictions is important to improve the Quality of Service and enable efficient energy consumption through dynamic resource allocation [2, 3, 4, 5]. Moreover, in critical situations such as wartime, where rapid situational awareness and response are important, delays or disconnections in receiving and transmitting attack warnings can lead to severe consequences, even if the issues occur in a short period. To mitigate these issues, it is crucial to hand over devices to alternative base stations or communication stations seamlessly to ensure stable network communication, even during periods of congestion. For this reason, accurate mobile traffic prediction is important for optimizing network performance and enhancing the user experience [6]. Since mobile traffic data is time series data, traditional time series prediction methods like linear regression [7; 8] and support vector machines (SVMs) [9; 10; 11] try to achieve accurate predictions. However, mobile traffic data has spatial and temporal characteristics. So, it is necessary to consider both spatial and temporal characteristics and their relationship for accurate data prediction. To effectively capture these complicated relationships, more sophisticated modeling techniques are required. In this context, deep learning approaches such as stacked autoencoders and Long Short-Term Memory (LSTM) networks [12; 13] have emerged as promising solutions for traffic prediction. However, these methods suffer from information loss during encoding and struggle to capture spatial dependencies. Recently, many studies [14; 15; 16; 17; 18; 19; 20; 21] have solved these challenges by applying a convolutional network block structure. This structure enables the model to capture local and global spatial dependencies through convolution layers. And some of them [14; 15] use the DenseNet [22] structure, which is free from the gradient-vanishing problem because of its dense connection between layers. Also, they receive inputs separately as hour-based and date-based data, enabling more effective temporal trend prediction. HSTNet [15] further enhances this approach by incorporating deformable convolutions [23] in dense blocks to capture long-range spatial dependencies and introducing an attention module to compute attention scores for input data to weigh data in crucial locations. However, HSTNet calculates an attention map only from inputs in an attention branch and applies directly to prediction, so the branch cannot deal with various intermediate features. Due to this, the model has limitations in reflecting fine-grained details. And the trained model using deformable convolution, which can be considered a kind of attention as it applies convolution to offset-adjusted pixels, may not always be optimal. Since the offsets in deformable convolution are learned through gradients, reaching pixels with large offsets can be challenging. So, there is a potential risk that some important pixels may not be referenced at all, even if they have more valuable information than their neighboring pixels when training. For this reason, we propose Attention-DenseNet (ADNet), a deep learning model that integrates an attention module into a DenseNet-based framework. We explore optimal strategies for utilizing attention mechanisms to improve traffic prediction performance. We determine the impact of attention module placement and the most effective types of attention mechanisms for different tasks.