Xie Chenfei, Xiu Yue, Yang Songjie, Miao Qilong, Chen Lu, Gao Yong, Zhang Zhongpei
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel). 2025 Jan 10;25(2):388. doi: 10.3390/s25020388.
A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI.
一个集成多种模式的通信网络能够有效地支持下一代无线通信的可持续发展。集成传感、通信和功率传输（ISCPT）代表了一种新兴的技术范式，它不仅有助于信息传输，还能实现环境感知和无线功率传输。为了在传输中实现最优波束成形，在确保基本系统性能的同时，满足包括服务质量（QoS）、雷达传感精度和功率传输效率在内的多个约束条件至关重要。多个参数约束的存在使得该问题成为一个非凸优化挑战，这突出了需要一种在低计算复杂度和高精度之间取得平衡的解决方案。此外，信道状态信息（CSI）的准确性对于确定可实现速率至关重要，因为不完美或不完整的CSI会显著降低系统性能和波束成形效率。深度强化学习（DRL）是一种机器学习技术，其中智能体通过与其环境交互来学习，它提供了一种有前景的方法，可以通过自适应决策策略动态优化系统性能。在本文中，我们提出了一个基于DRL的ISCPT框架，该框架有效地管理复杂的环境状态，并不断调整与传感、通信和能量收集相关的变量，以提高整体系统效率和可靠性。在ISCPT系统中，可以通过稳健的、可学习的波束成形来推断可实现速率的上限。我们的结果表明，基于DRL的算法显著改善了资源分配、功率管理和信息传输，特别是在具有不完美CSI的动态和不确定环境中。
