.png){kind=icon}
返回
Decoder Design for Massive-MIMO Systems Using Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/JSYST.2022.3191192.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Designing a massive multiple-input multiple-output (M-MIMO) system is complex, time-consuming, and challenging. Conventional MIMO decoders are either inefficient or complex to implement for M-MIMO scenarios. Deep learning (DL) has recently emerged to perform many complex operations more efficiently within a shorter time. In this article, a DL-based iterative network (DLNet) has been proposed for signal decoding in M-MIMO systems. The proposed DLNet decoder is a 40 layer deep neural network (DNN) that can decode M-MIMO signals for uplink Rayleigh and correlated MIMO channels. With the knowledge of the received signals and the M-MIMO channels, the proposed DLNet decoder decodes the messages of all the users. The DLNet considers complex-valued Rayleigh and correlated MIMO channels while decoding. Its training converges faster than other available deep learning (DL) based MIMO decoders. During simulations, the required number of DNN layers, training iterations, and other network parameters are estimated to improve the proposed decoder's performance. In the M-MIMO perspective, the proposed DLNet has been evaluated for symbol-error-rate (SER) performance, algorithm complexity, and runtime requirement. Simulation results show that the SER of the proposed DLNet decoder performs better by at least 2 dB than other M-MIMO decoding techniques. The DLNet consumes 800 times less time during decoding than other MIMO decoders for equal transmitting and receiving antenna configurations. At the same time, it has 13 times fewer trainable parameters than the available detection-network algorithm.
Keyword:
Decoding
MIMO communication
Training
Transmitting antennas
Symbols
Iterative decoding
Receiving antennas
Deep learning (DL)
iterative network
massive multiple-input multiple-output (MIMO) system
projected gradient descent
signal decoding
期刊
I
IF:
2.4
论文数:
4.5K
被引数:
387
机构
引用论文
.png){kind=icon}
Cross-linkable Polymer Matrix for Enhanced Thermal Stability of Succinonitrile-based Polymer Electrolyte in Lithium Rechargeable Batteries可交联聚合物基质，用于增强锂可充电电池中基于丁二腈的聚合物电解质的热稳定性
OAAI
OAAI分享收藏Structural study of lanthanides(III) in aqueous nitrate and chloride solutions by EXAFS通过EXAFS对硝酸盐和氯化物水溶液中镧系元素 (III) 的结构研究
PREAI
分享收藏
查看更多内容