Report
On the Parameter Selection of Phase-transmittance Radial Basis Function Neural Networks for Communication Systems
| العنوان: | On the Parameter Selection of Phase-transmittance Radial Basis Function Neural Networks for Communication Systems |
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| المؤلفون: | Soares, Jonathan A., Mayer, Kayol S., Arantes, Dalton S. |
| المصدر: | IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024) |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing, I.2.6 |
| الوصف: | In the ever-evolving field of digital communication systems, complex-valued neural networks (CVNNs) have become a cornerstone, delivering exceptional performance in tasks like equalization, channel estimation, beamforming, and decoding. Among the myriad of CVNN architectures, the phase-transmittance radial basis function neural network (PT-RBF) stands out, especially when operating in noisy environments such as 5G MIMO systems. Despite its capabilities, achieving convergence in multi-layered, multi-input, and multi-output PT-RBFs remains a daunting challenge. Addressing this gap, this paper presents a novel Deep PT-RBF parameter initialization technique. Through rigorous simulations conforming to 3GPP TS 38 standards, our method not only outperforms conventional initialization strategies like random, $K$-means, and constellation-based methods but is also the only approach to achieve successful convergence in deep PT-RBF architectures. These findings pave the way to more robust and efficient neural network deployments in complex digital communication systems. Comment: IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024) |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/ICMLCN59089.2024.10624891 |
| URL الوصول: | http://arxiv.org/abs/2408.07692 |
| رقم الانضمام: | edsarx.2408.07692 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/ICMLCN59089.2024.10624891 |
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