标题： 复值神经网络用于超可靠的大规模MIMO 显示英文标题

标题： Complex-Valued Neural Networks for Ultra-Reliable Massive MIMO

摘要： 在5G和6G网络不断发展的背景下，需求已超越高数据速率、超低延迟和广泛覆盖，越来越强调可靠性的需求。 本文提出了一种利用准正交空时块编码（QOSTBC）结合奇异值分解（SVD）进行信道状态信息（CSI）校正的超可靠多输入多输出（MIMO）方案，在分析频谱效率时，显著提升了QOSTBC和传统正交STBC（OSTBC）的性能。 尽管QOSTBC提高了频谱效率，但它也增加了最大似然（ML）解码器的计算复杂度。 为了解决这个问题，还引入了一种基于神经网络的解码方案，使用相位透射径向基函数（PT-RBF）架构来管理QOSTBC的复杂度。 仿真结果表明系统鲁棒性和性能得到了改善，使该方法成为下一代网络中超可靠通信的潜在候选方案。 显示英文摘要

摘要： In the evolving landscape of 5G and 6G networks, the demands extend beyond high data rates, ultra-low latency, and extensive coverage, increasingly emphasizing the need for reliability. This paper proposes an ultra-reliable multiple-input multiple-output (MIMO) scheme utilizing quasi-orthogonal space-time block coding (QOSTBC) combined with singular value decomposition (SVD) for channel state information (CSI) correction, significantly improving performance over QOSTBC and traditional orthogonal STBC (OSTBC) when analyzing spectral efficiency. Although QOSTBC enhances spectral efficiency, it also increases computational complexity at the maximum likelihood (ML) decoder. To address this, a neural network-based decoding scheme using phase-transmittance radial basis function (PT-RBF) architecture is also introduced to manage QOSTBC's complexity. Simulation results demonstrate improved system robustness and performance, making this approach a potential candidate for ultra-reliable communication in next-generation networks.