# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning  
[PERSON]  
1 Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea  
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[PERSON]  
2 School of Physics and Electronic Engineering, Qlu Normal University, Jinan 250200, China;  
[EMAIL_ADDRESS]  
[PERSON]  
1 Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea  
1 [EMAIL_ADDRESS]  
Footnote 1: [[https://github.com](https://github.com)]([https://github.com](https://github.com))  
Traditional indoor positioning methods, such as trilateration and the weighted centroid localization algorithm, also have limitations [8; 9; 10]. Trilateration accuracy is degraded by obstacles, while traditional methods often overlook factors such as height differences between access points (APs) and terminals, leading to significant positioning errors, particularly in multi-story buildings [11]. In contrast, machine learning approaches, particularly convolutional neural networks (CNNs), have shown potential in improving the accuracy of RSSI-based indoor positioning systems [12]. However, CNNs also face challenges, such as handling uneven data distributions and localized feature variations, which can impact predictive stability. Environmental noise and dynamic changes, such as multipath effects, contribute to data inconsistencies, reducing positioning accuracy [13; 14; 15]. CNNs further struggle with classification near region boundaries, and their effectiveness depends heavily on large, high-quality training datasets. Batch normalization (BN), introduced by [PERSON] and [PERSON] in 2015, addresses some of these challenges by normalizing the mean and variance of activations within a mini-batch, stabilizing the input distribution during training [16]. This process mitigates internal covariate shifts, accelerates convergence, and supports the use of higher learning rates without risking divergence. BN enhances gradient flow, particularly in deep architectures, by incorporating learnable parameters that preserve the network's representation capacity. Additionally, BN serves as an effective regularizer, facilitates the use of non-linear activation functions, and mitigates saturation issues, improving CNN performance in various applications, including indoor positioning [17]. However, theoretical analyses of BN often rely on simplified assumptions that fail to capture the complexities of modern deep neural network (DNN) training [18]. BN is also sensitive to uneven data distributions and local feature variations, which can limit its effectiveness in noisy and dynamic environments [19]. To address the limitations of BN, several alternative normalization techniques have been developed. Layer normalization (LN), proposed by [PERSON] et al., normalizes across all features for each training example, making it particularly effective for recurrent neural networks by stabilizing their hidden state dynamics [20]. Instance normalization (IN), introduced by [PERSON] et al., normalizes each channel independently for each sample, excelling in tasks like style transfer due to its ability to capture fine-grained local statistics [21]. Group normalization (GN), developed by [PERSON] and [PERSON], divides channels into groups and normalizes within each group, demonstrating superior performance in tasks with small batch sizes, such as object detection and video classification [22]. Within the context of these methods, LN was chosen as a representative alternative normalization technique for comparative analysis in our ablation study. While these methods aim to stabilize training and improve generalization, they often struggle in scenarios with highly localized feature variations, such as RSSI-based indoor positioning systems, where signal strength significantly varies across locations.