The Terahertz band is envisioned to meet the demanding 100 Gbps data rates for 6G wireless communications. Aiming at combating the distance limitation problem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is promising. However, relationships among wavelength, array size and antenna spacing give rise to the inaccuracy of planar-wave channel model (PWM), while an enlarged channel matrix dimension leads to excessive parameters of applying spherical-wave channel model (SWM). Moreover, due to the adoption of hybrid beamforming, channel estimation (CE) needs to recover high-dimensional channels from severely compressed channel observation. In this paper, a hybrid spherical- and planar-wave channel model (HSPM) is investigated and proved to be accurate and efficient by adopting PWM within subarray and SWM among subarray. Furthermore, a two-phase HSPM CE mechanism is developed. A deep convolutional-neural-network (DCNN) is designed in the first phase for parameter estimation of reference subarrays, while geometric relationships of the remaining channel parameters between reference subarrays are leveraged to complete CE in the second phase. Extensive numerical results demonstrate the HSPM is accurate at various communication distances, array sizes and carrier frequencies.The DCNN converges fast and achieves high accuracy with 5.2 dB improved normalized-mean-square-error compared to literature methods, and owns substantially low complexity.

中文翻译：

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用于太赫兹超大规模 MIMO 系统的混合球面波和平面波信道建模和 DCNN 驱动的估计

太赫兹频段旨在满足 6G 无线通信要求的 100 Gbps 数据速率。旨在以低硬件成本解决距离限制问题，具有混合波束成形的超大规模 MIMO 很有前景。然而，波长、阵列尺寸和天线间距之间的关系导致平面波信道模型（PWM）的不准确，而扩大的信道矩阵维度导致应用球面波信道模型（SWM）的参数过多。此外，由于采用混合波束成形，信道估计（CE）需要从严重压缩的信道观察中恢复高维信道。在本文中，通过在子阵列内采用 PWM 和在子阵列之间采用 SWM，研究了一种混合球面波和平面波通道模型 (HSPM)，并证明其准确有效。此外，开发了两阶段 HSPM CE 机制。第一阶段设计深度卷积神经网络（DCNN）用于参考子阵列的参数估计，而第二阶段利用参考子阵列之间剩余通道参数的几何关系完成CE。大量的数值结果表明 HSPM 在各种通信距离、阵列大小和载波频率下都是准确的。 DCNN 收敛速度快，精度高，与文献方法相比，归一化均方误差提高了 5.2 dB，并且具有相当低的复杂度。同时利用参考子阵列之间剩余通道参数的几何关系来完成第二阶段的CE。大量的数值结果表明 HSPM 在各种通信距离、阵列大小和载波频率下都是准确的。 DCNN 收敛速度快，精度高，与文献方法相比，归一化均方误差提高了 5.2 dB，并且具有相当低的复杂度。同时利用参考子阵列之间剩余通道参数的几何关系来完成第二阶段的CE。大量的数值结果表明 HSPM 在各种通信距离、阵列大小和载波频率下都是准确的。 DCNN 收敛速度快，精度高，与文献方法相比，归一化均方误差提高了 5.2 dB，并且具有相当低的复杂度。