Details

Autor(en) / Beteiligte

• Zhang, Zufan
• Zheng, Yue
• Gan, Chenquan
• Zhu, Qingyi

Titel

Massive MIMO CSI reconstruction using CNN-LSTM and attention mechanism

Ist Teil von

• IET communications, 2020-11, Vol.14 (18), p.3089-3094

Ort / Verlag

The Institution of Engineering and Technology

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• In massive multiple-input multiple-output (MIMO) systems, the channel state information (CSI) feedback enables performance gain in frequency division duplex networks. However, with the increase in the number of antennas, the feedback overhead of CSI will also enhance. To this end, this study addresses the issue of massive MIMO CSI reconstruction using convolutional neural network (CNN), long short-term memory (LSTM) and attention mechanism, and proposes an efficient network architecture (denoted as CNN-LSTM-A). To achieve a compromise between performance and complexity, the proposed method significantly reduces the number of training parameters by utilising a single-stage network rather than a multiple-stage network. Finally, simulation results show that the authors method can reduce the feedback overhead of CSI effectively, and achieves better performance in terms of CSI compression and recovery accuracy compared with existing state-of-the-art methods.