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arXiv 2102.09196cs.ITcs.LGmath.IT
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DeepMuD: Multi-user Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning

Zonguldak Bulent Ecevit University(宗古尔达克布伦特·埃杰维特大学);Carleton University(卡尔顿大学)

Ahmet Emir, Ferdi Kara, Hakan Kaya, Halim Yanikomeroglu

英文摘要:

In this letter, we propose a deep learning-aided multi-user detection (DeepMuD) in uplink non-orthogonal multiple access (NOMA) to empower the massive machine-type communication where an offline-trained Long Short-Term Memory (LSTM)-based network is used for multi-user detection. In the proposed DeepMuD, a perfect channel state information (CSI) is also not required since it is able to perform a joint channel estimation and multi-user detection with the pilot responses, where the pilot-to-frame ratio is very low. The proposed DeepMuD improves the error performance of the uplink NOMA significantly and outperforms the conventional detectors (even with perfect CSI). Moreover, this gain becomes superb with the increase in the number of Internet of Things (IoT) devices. Furthermore, the proposed DeepMuD has a flexible detection and regardless of the number of IoT devices, the multi-user detection can be performed. Thus, an arbitrary number of IoT devices can be served without a signaling overhead, which enables the grant-free communication.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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