Accurate and low-complexity indoor localization is important for location-based services in fifth generation (5G) and sixth generation (6G) networks, where positioning devices operate under limited computational budgets and non conditions. Indoor localization has been studied widely using traditional signal-level localization approaches. However, these techniques often show degraded performance in on-line-of-sight (NLoS) scenarios. Recently, artificial intelligence (AI)-based techniques, including transformers, have been applied to address these challenges. While transformer-based architectures can capture the dependencies within the measurements collected from distributed access points (APs), their high computational complexity results in a large number of multiply-accumulate operations and long inference time. In contrast, lightweight recurrent and convolutional models trade this cost for degraded accuracy. In this paper, we propose LocAttMamba, a low-complexity localization framework in which the channel impulse response (CIR) and time-based features of each AP are processed by a separate Mamba encoder with near-linear complexity, and the resulting per-AP embeddings are fused through a multi-head attention layer that weights each AP according to its importance at every time step. The framework jointly predicts the user location and its per-axis uncertainty, which is refined through a post-hoc calibration step. We evaluate the proposed framework using two real-world 5G and ultra-wideband (UWB) measurement datasets. Our numerical results reveal that LocAttMamba obtains a mean two-dimensional (2-D) positioning error of 1.004 m and 0.599 m, respectively, on 5G and UWB datasets, outperforming the second-best benchmark by 9.79% and 5.82%, while requiring the fewest multiply-accumulate operations among all evaluated models and being 4.4-16 times faster than the transformer-based benchmarks.

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