arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2301.00516cs.ITcs.LGmath.IT
收藏

模型驱动的深度学习用于非相干大规模机器类型通信

Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications

Tsinghua University(清华大学);Peng Cheng Laboratory(鹏城实验室);University of Waterloo(滑铁卢大学)

Zhe Ma, Wen Wu, Feifei Gao, Xuemin, Shen

摘要:

在本文中,我们调查的联合设备活动和数据检测在大规模机器类型通信(mMTC)与一个阶段的非相干方案,其中数据比特被嵌入在导频序列和基站同时检测活跃的设备和它们的嵌入式数据比特没有显式信道估计。由于非相干传输方案引入的相关稀疏模式,传统的近似消息传递(AMP)算法不能获得令人满意的性能。因此,我们提出了一种深度学习(DL)改进的AMP网络(DL-mAMPnet),通过有效利用导频活动相关性来提高检测性能。通过将AMP算法展开为前馈神经网络来构建DL-mAMPnet,该网络将AMP算法的原理数学模型与强大的学习能力相结合,从而受益于两种技术的优点。在DL-mAMPnet中引入可训练的参数来近似相关稀疏模式和大尺度衰落系数。此外,细化模块的设计,以进一步提高性能,通过利用相关的稀疏模式所造成的空间特征。仿真结果表明,所提出的DL-mAMPnet算法在符号出错率性能上明显优于传统算法。

英文摘要:

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.

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。

↑