Makale detayı · 2020
Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approach
- Yıl
- 2020
- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- YÖKSİS dergi adı IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Katalog eşleşmesi (ISSN) IEEE Transactions on Vehicular Technology
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
OpenAlex · İngilizce
In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.
Konular
Atıflar
OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.
165 atıf
OpenAlex cited_by_count (önbellek / veritabanı)
Yerel katalogda bu makaleye atıf yapan 11 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).
- Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems 2020
- Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO 2022
- Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO 2022
- A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO 2022
- A Unified Approach for Beam-Split Mitigation in Terahertz Wideband Hybrid Beamforming 2023
- Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO 2020
- Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO 2020
- Federated Dropout Learning for Hybrid Beamforming with Spatial Path Index Modulation in Multi-User Mmwave-Mimo Systems 2021
- Innovative Channel Estimation Methods for Massive MIMO Using GAN Architectures 2025
- Federated Learning for Wireless Communications 2024