Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz)-band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged jointly access spectrum and reduced hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection.

翻译：下一代无线网络致力于实现更高通信速率、超低延迟、无缝连接及高分辨率感知能力。为满足这些需求，太赫兹频段信号处理凭借其宽带宽和亚毫米波长的特性，被视作关键技术方向。此外，太赫兹集成感知与通信范式通过统一平台实现频谱共享与硬件成本降低。为应对太赫兹传播挑战，太赫兹-集成感知与通信系统采用超大规模天线阵列，以提升通信高数据速率与感知高分辨率所需的波束赋形增益。然而，全数字波束赋形器的实现成本与功耗过高。尽管混合模拟/数字波束赋形是潜在解决方案，但子载波独立模拟波束赋形器的使用会导致波束斜视现象——由于所有子载波采用相同模拟波束赋形器，不同子载波将观察到不同方向。本文针对具有混合波束赋形的太赫兹-集成感知与通信系统，开发了一种稀疏阵列架构以提供经济高效的解决方案。我们分析了波束斜视影响下的天线选择问题，并引入流形优化方法进行混合波束赋形设计。为降低计算与存储成本，提出基于分组子阵列、量化性能指标及顺序优化的新型算法。这些方法显著减少了可能的子阵列配置数量，从而实现基于分类模型的神经网络精确执行天线选择。