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arXiv 2104.09799cs.ITeess.SPmath.IT
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基于深度学习的MU-MISO系统符号级高效预编码设计

Deep Learning based Efficient Symbol-Level Precoding Design for MU-MISO Systems

Zhu Bo, Rang Liu, Ming Li, Qian Liu

摘要:

近年来出现的符号级预编码(Symbol-Level Precoding,SLP)技术能够将有害的多用户干扰(Multi-User Interference,MUI)转化为有益的信号,从而提高系统性能,被认为是多用户无线通信系统中一种很有前途的解决方案。然而,传统符号级预编码设计的巨大计算复杂性严重阻碍了实际实现。为了解决这个困难,我们提出了一种新的基于深度学习(DL)的方法来有效地设计符号级预编码器。特别地,在这种对应关系中,我们考虑多用户多输入单输出(MU-MISO)下行链路系统。提出了一种高效的预编码神经网络(EPNN),用于在功率约束下优化符号级预编码器,以最大化所有用户的最小服务质量(QoS)。仿真结果表明,与传统的基于凸优化的SLP设计方法相比,基于EPNN的SLP设计方法在性能损失较小的情况下,可以显著减少计算时间.

英文摘要:

The recently emerged symbol-level precoding (SLP) technique has been regarded as a promising solution in multi-user wireless communication systems, since it can convert harmful multi-user interference (MUI) into beneficial signals for enhancing system performance. However, the tremendous computational complexity of conventional symbol-level precoding designs severely hinders the practical implementations. In order to tackle this difficulty, we propose a novel deep learning (DL) based approach to efficiently design the symbol-level precoders. Particularly, in this correspondence, we consider a multi-user multi-input single-output (MU-MISO) downlink system. An efficient precoding neural network (EPNN) is introduced to optimize the symbol-level precoders for maximizing the minimum quality-of-service (QoS) of all users under the power constraint. Simulation results demonstrate that the proposed EPNN based SLP design can dramatically reduce the computing time at the price of slight performance loss compared with the conventional convex optimization based SLP design.

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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