Transfer Learning for Angle of Arrivals Estimation in Massive MIMO System
Abstract
Angle-of-arrivals (AoAs) estimation is one of the key tasks of localization in massive multiple-input-multiple-output (MIMO) system. Among all AoAs estimation methods, the class of deep learning (DL) based methods has been recognized effective with high estimation accuracy and low complexity. However, the design for methods in this class requires obtaining adequate labeled training data and training a specific network for each of different channels in MIMO system, which is challenging and costly. To overcome this bottleneck, a transfer learning method is proposed in this paper based on the assumption that some com-mon features exist among different channel models. The proposed method is implemented by a deep residual network (ResNet) composed of transfer layers (to extract the common features) followed by fine-tuning layers (to extract other characteristic features) in cascade, thus without need of a specific network for each different channel. As a realization of the proposed framework, the ResNet is trained on the data from the source domain (i.e. the “basic” channel model) to obtain the weights of transfer layers (fixed thereafter), which are used to initialize the fine-tuning layers. Then, small amounts of data from the target domain (i.e., a more sophisticated channel model like spatial channel model), are used to adjust the weights of fine-tuning layers. Finally, extensive simulation results demonstrate that the AoAs estimation accuracy of the proposed method is comparable to that of supervised DL-based methods, for which a specific trained network is required for each channel.
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Bias Mitigation in AI systems is a critical endeavor to ensure fairness and representation, involving balanced data collection, algorithmic adjustments, and regularization techniques to minimize biases.
“Bias Mitigation in AI systems is a critical endeavor to ensure fairness and representation, which involves not only balanced data collection to avoid skewed perspectives but also involves implementing algorithmic adjustments and regularization techniques to minimize biases [218], [219].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [218]
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“…the training process by allowing models to be efficiently fine-tuned for specific applications, thereby enhancing adaptability and performance across diverse tasks, and proving particularly beneficial in scenarios where acquiring extensive labeled data is impractical or unfeasible [188], [189].”
Background
- Authors
- Guo, Z., Lin, K., Chen, X., & Chit, C.-Y.
- Affiliations
- Sun Yat-sen University (CN)
- National Tsing Hua University (TW)
- Venue
- 2022 IEEE/CIC International Conference on Communications in China (ICCC)
- Publisher
- IEEE
- Published
- 2022-08-11
- Pages
- 506-511
- OpenAlex
- W4297911731
- Cited by
- 9
- References
- 16
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 1
- FWCI
- 3.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CIC International Conference on Communications in China (ICCC) (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 111-2221-E-007-035-MY2,MOST 110-2221-E-007-031)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Keywords
- angle of arrival estimation, massive MIMO, transfer learning, ResNet, spatial channel model, localization, fine-tuning
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