基于深度强化学习的LiDAL辅助RLNC-NOMA光无线通信系统功率分配
DRL-based Power Allocation in LiDAL-Assisted RLNC-NOMA OWC Systems
AI总结：
本文提出一种结合LiDAL和RLNC的NOMA光无线通信系统，采用深度强化学习优化动态功率分配，提升平均总速率并应对用户定位误差。
摘要：
非正交多址接入（NOMA）是一种有前景的光无线通信（OWC）技术，通过功率域使多个用户同时共享光谱。然而，不完美的信道状态信息（CSI）和残余解码错误会恶化NOMA性能，尤其是在现实密集用户室内场景中。本文建模了一个整合光检测与定位（LiDAL）和随机线性网络编码（RLNC）的OWC系统，其中LiDAL利用时空信息提高用户CSI，RLNC增强解码过程中的数据鲁棒性，从而形成LiDAL辅助的RLNC-NOMA OWC系统。由于多用户之间复杂的相互作用以及编码和检测过程，功率分配（PA）至关重要，但优化连续PA动态可能计算上很昂贵。为此，本文采用深度强化学习（DRL）框架高效学习近优PA策略。特别是，提出了一种基于DRL的归一化优势函数（NAF）算法以最大化平均总速率，并将其性能与深度确定性策略梯度（DDPG）、增益比PA（GRPA）和穷举搜索进行比较。结果表明，NAF接近穷举搜索，比DDPG快39%，在GRPA上提高平均总速率4.6%，同时考虑用户定位误差。
英文摘要：
Non-orthogonal multiple access (NOMA) is a promising technique for optical wireless communication (OWC), enabling multiple users to share the optical spectrum simultaneously through the power domain. However, imperfect channel state information (CSI) and residual decoding errors deteriorate NOMA performance, especially in realistic dense-user indoor scenarios. In this work, we model an OWC system that integrates light detection and localization (LiDAL) and random linear network coding (RLNC) within a NOMA framework. LiDAL exploits spatio-temporal information to improve user CSI, while RLNC enhances data resilience in the successive decoding process, resulting in a LiDAL-assisted RLNC-NOMA OWC system. Power allocation (PA) is crucial in this system due to complex interactions between multiple users and the coding and detection processes, but optimizing continuous PA dynamically can be computationally prohibitive. To address this, we adopt a deep reinforcement learning (DRL) framework to efficiently learn near-optimal PA strategies. In particular, a DRL-based normalized advantage function (NAF) algorithm is proposed to maximize the average sum rate, and its performance is compared to deep deterministic policy gradient (DDPG), gain ratio PA (GRPA), and exhaustive search. The results indicate that NAF closely matches exhaustive search, is 39% faster than DDPG, and improves the average sum rate by 4.6% over GRPA, while accounting for user location estimation errors.
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