Sub-terahertz (sub-THz) multi-user multiple-input multiple-output (MU-MIMO) systems unlock immense bandwidth for 6G wireless communications. However, practical deployment of wireless systems in sub-THz bands faces critical challenges such as increased atmospheric absorption, reduced channel coherence time due to increased Doppler spread at higher carrier frequencies, and hardware bottlenecks as low-loss sub-THz phase shifters are difficult to realize. To overcome the hardware and channel estimation challenges of sub-THz systems, this paper proposes a hybrid beamforming (BF) framework that integrates reconfigurable liquid crystal (LC) antennas with a liquid neural network (LNN) for transmitter. Specifically, we employ an LC antenna as the analog BF stage of a hybrid BF architecture, exploiting its voltage-driven permittivity tunability to achieve high-gain beam steering without the need for lossy phase shifters. For digital BF, we utilize an ordinary differential equations-defined LNN to learn temporal channel dynamics, and use a manifold optimization technique to compress the search space. We validated the proposed method on simulated site-specific 108 GHz ray-tracing channels in an urban scenario using NYURay, a ray-tracing simulator validated against 142 GHz propagation measurements. The 108 GHz carrier frequency matches the operating band of the LC antenna hardware. The proposed method achieves an 88.6\% spectral efficiency (SE) gain and higher robustness to imperfect channel estimation compared to the learning-aided gradient descent and gated recurrent unit machine learning baselines, and 1.9 times higher SE than the 3GPP TR~38.901 standard antenna model, highlighting the potential of LC-based hardware for sub-THz communications.

翻译：亚太赫兹（sub-THz）多用户多输入多输出（MU-MIMO）系统为6G无线通信解锁了巨大的带宽。然而，在sub-THz频段无线系统的实际部署面临严峻挑战，例如大气吸收增强、因更高载波频率下多普勒扩展导致的信道相干时间缩短，以及低损耗sub-THz移相器难以实现等硬件瓶颈。为克服sub-THz系统的硬件与信道估计挑战，本文提出一种混合波束赋形（BF）框架，该框架在发射端集成了可重构液晶（LC）天线与液态神经网络（LNN）。具体而言，我们采用LC天线作为混合BF架构的模拟BF级，利用其电压驱动介电常数可调性实现高增益波束控制，无需损耗型移相器。在数字BF方面，我们利用由常微分方程定义的LNN学习时域信道动态，并采用流形优化技术压缩搜索空间。该方法利用NYURay射线追踪模拟器（该模拟器经142 GHz传播测量验证）在城市场景中仿真特定位置108 GHz射线追踪信道进行了验证。108 GHz载波频率与LC天线硬件的工作频段匹配。与基于学习辅助的梯度下降和门控循环单元机器学习基线相比，所提方法在信道估计不完善情况下实现了88.6%的频谱效率（SE）增益及更高鲁棒性，且SE较3GPP TR 38.901标准天线模型提升1.9倍，凸显了基于LC硬件的sub-THz通信潜力。