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صفحه اصلی
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سی و سومین کنفرانس بین المللی مهندسی برق
Soft Decision Adaptive Deep Learning Detection for Enhanced Massive MIMO Performance
نویسندگان :
Farnaz Sedaghati
1
Mojtaba Amiri
2
Ali Olfat
3
1- University of Tehran - Electrical and Computer Engineering
2- University of Tehran - Electrical and Computer Engineering
3- University of Tehran - Electrical and Computer Engineering
کلمات کلیدی :
Massive MIMO،Deep Learning،Signal Detection،Iterative Method،Loss Function،Activation Function
چکیده :
Massive MIMO is a key technology for next-generation wireless systems, offering enhanced spectral efficiency and reliability. However, its high-dimensional nature poses significant signal detection challenges. Conventional detection methods are often ineffective, and optimal schemes such as maximum likelihood (ML) become computationally infeasible. This paper presents a deep learning-based MIMO detection scheme that achieves superior performance while maintaining lower computational complexity. The proposed approach builds on iterative soft-thresholding algorithms, incorporating soft decision mapping to the nearest constellation and an adaptive loss function, and uses a novel training algorithm to enhance detection efficiency and robustness. The results demonstrate a significant improvement in detection performance, as the proposed approach outperforms both conventional techniques and previous state-of-the-art methods. This emphasizes the effectiveness and practicality of the proposed method.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.0.4