Recent advancements in digital signal processing and lightweight neural architectures have opened new possibilities for developing efficient and interference-resilient indoor localization systems suitable for next-generation wireless networks. This paper proposes an indoor localization system for 6G communication systems with single co-channel interference. Ray tracing technique is used to compute the frequency-domain channel state information (CSI). Next, CSI fingerprints are input into a lightweight back propagation neural network (BPNN) with channel-wise self-attention (CSA) and spatial self-attention (SSA) to improve the model’s resilience to interference and noise. Numerical results demonstrate that the attention-enhanced BPNN models significantly outperform the standard BPNN. In particular, CSA focuses on informative frequency channels and excels under spectral distortion caused by interference, while SSA puts emphasis on spatial features and shows superior performance in spatially stable environments, reducing RMSE by up to 20% in high interference scenarios and 10% in low interference scenarios, respectively. These findings validate the effectiveness of integrating attention mechanisms into neural localization frameworks, making them well-suited for next-generation 6G indoor positioning systems in interference-limited environments.
Chiu et al. (2026) studied this question.