Terahertz Ul-massive MIMO系统混合球与平流-平流-波流信道建模和DCNN动力估计 (Hybrid Spherical- and Planar-Wave Channel Modeling and DCNN-powered Estimation for Terahertz Ultra-massive MIMO Systems)

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.

翻译：预计Terahertz频段将达到6G无线通信要求的100千兆字节数据率。旨在以低硬件成本、超大质量MIMIM(超大质量MIMIM)和混合波束成形来应对远程限制问题,前景良好。然而,波长、阵列大小和天线间距之间的关系导致波浪频道模型(PWM)的不准确性,而扩大的频道矩阵维度则导致应用球波频道模型(SWM)的参数过大。此外,由于采用了混合波形模型,频道估计(CE)需要从严重压缩的频道观测中恢复高维的频道。在本文件中,对混合球波和波浪频道模型(HSPM)进行了调查,并通过在亚阵列中采用PWM(PWM)和SWMM(SWM)模型,证实其准确性和效率。此外,正在开发一个两阶段的HSPMC-NE机制。在第一个阶段设计了一个深层神经网络(DCNNNN)用于参数的完整参考次阵列的参数估计,而第二个测深深层精度的深度的频道和深深层平面的轨道参照轨道参数关系则在CHFM(CFM(C-S-S-S-S-S-S-S-S-S-S-S-S-S-SLL)轨道的精确级级级级级级级的精确度、CR-S-S-S-S-S-S-S-S-SLVDM-S-S-S-S-Sldal-S-C-C-C-C-C-C-L-L-S-C-C-C-C-C-C-C-C-C-C-C-L-L-C-L-L-L-L-C-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-S-S-S-S-L-S-S-L-L-S-S-L-L-L-L-L-L-L-L-L-L-L-L-L-L