In high mobility applications of millimeter wave (mmWave) communications, e.g., vehicle-to-everything communication and next-generation cellular communication, frequent link configuration can be a source of significant overhead. We use the sub-6 GHz channel covariance as an out-of-band side information for mmWave link configuration. Assuming: 1) a fully digital architecture at sub-6 GHz and 2) a hybrid analog–digital architecture at mmWave, we propose an out-of-band covariance translation approach and an out-of-band aided compressed covariance estimation approach. For covariance translation, we estimate the parameters of sub-6 GHz covariance and use them in theoretical expressions of covariance matrices to predict the mmWave covariance. For out-of-band aided covariance estimation, we use weighted sparse signal recovery to incorporate out-of-band information in compressed covariance estimation. The out-of-band covariance translation eliminates the in-band training completely, whereas out-of-band aided covariance estimation relies on in-band as well as out-of-band training. We also analyze the loss in the signal-to-noise ratio due to an imperfect estimate of the covariance. The simulation results show that the proposed covariance estimation strategies can reduce the training overhead compared to the in-band only covariance estimation.

中文翻译：

---

使用带外信息的毫米波混合系统空间协方差估计

在毫米波 (mmWave) 通信的高移动性应用中，例如车对万物通信和下一代蜂窝通信，频繁的链路配置可能是大量开销的来源。我们使用低于 6 GHz 的信道协方差作为毫米波链路配置的带外辅助信息。假设：1) 低于 6 GHz 的全数字架构和 2) 毫米波的混合模拟-数字架构，我们提出了带外协方差转换方法和带外辅助压缩协方差估计方法。对于协方差转换，我们估计低于 6 GHz 协方差的参数，并将它们用于协方差矩阵的理论表达式来预测毫米波协方差。对于带外辅助协方差估计，我们使用加权稀疏信号恢复将带外信息合并到压缩协方差估计中。带外协方差转换完全消除了带内训练，而带外辅助协方差估计依赖于带内和带外训练。我们还分析了由于协方差的不完美估计而导致的信噪比损失。仿真结果表明，与仅带内协方差估计相比，所提出的协方差估计策略可以减少训练开销。我们还分析了由于协方差的不完美估计而导致的信噪比损失。仿真结果表明，与仅带内协方差估计相比，所提出的协方差估计策略可以减少训练开销。我们还分析了由于协方差的不完美估计而导致的信噪比损失。仿真结果表明，与仅带内协方差估计相比，所提出的协方差估计策略可以减少训练开销。