标题： 太赫兹频段中采用机器学习增强半自感知混合RIS赋能的ISAC系统和速率 显示英文标题

标题： Sum Rate Enhancement using Machine Learning for Semi-Self Sensing Hybrid RIS-Enabled ISAC in THz Bands

摘要： 本文提出了一种新颖的半自感应混合可重构智能表面（SS-HRIS）在太赫兹（THz）频段的应用方案，其中RIS配备了反射元件，这些元件被分为被动元件和主动元件，还包括感应元件。 SS-HRIS结合集成感应和通信（ISAC）能够帮助减轻在THz频段内常见的多路径衰减问题。 在我们提出的方案中，传感器配置在SS-HRIS上以接收来自目标的雷达回波信号。 提出了一个联合基站（BS）波束成形和HRIS预编码矩阵优化问题，旨在最大化通信用户的总速率，同时维持通过估计到达角（AoA）的回波信号和目标处热噪声的方向而得出的克拉默-拉奥界（CRB）所衡量的令人满意的感应性能。 首先推导了CRB表达式，并制定了总速率最大化问题，考虑到了通信和感应性能的约束条件。 为了解决复杂的非凸优化问题，提出了基于深度确定性策略梯度（DDPG）的深度强化学习（DRL）算法，其中奖励函数、动作空间和状态空间均被建模。 仿真结果显示，所提出的基于DDPG的DRL算法收敛良好，比几种基线方法表现更好，如软演员评论家（SAC）、近端策略优化（PPO）、贪婪算法以及随机BS波束成形和HRIS预编码矩阵方案。 此外，它还表明采用HRIS显著提升了可实现的总速率，与被动RIS和随机BS波束成形及HRIS预编码矩阵方案相比。 显示英文摘要

摘要： This paper proposes a novel semi-self sensing hybrid reconfigurable intelligent surface (SS-HRIS) in terahertz (THz) bands, where the RIS is equipped with reflecting elements divided between passive and active elements in addition to sensing elements. SS-HRIS along with integrated sensing and communications (ISAC) can help to mitigate the multipath attenuation that is abundant in THz bands. In our proposed scheme, sensors are configured at the SS-HRIS to receive the radar echo signal from a target. A joint base station (BS) beamforming and HRIS precoding matrix optimization problem is proposed to maximize the sum rate of communication users while maintaining satisfactory sensing performance measured by the Cramer-Rao bound (CRB) for estimating the direction of angles of arrival (AoA) of the echo signal and thermal noise at the target. The CRB expression is first derived and the sum rate maximization problem is formulated subject to communication and sensing performance constraints. To solve the complex non-convex optimization problem, deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are modeled. Simulation results show that the proposed DDPG-based DRL algorithm converges well and achieves better performance than several baselines, such as the soft actor-critic (SAC), proximal policy optimization (PPO), greedy algorithm and random BS beamforming and HRIS precoding matrix schemes. Moreover, it demonstrates that adopting HRIS significantly enhances the achievable sum rate compared to passive RIS and random BS beamforming and HRIS precoding matrix schemes.