As the Internet of Things (IoT) continue to advance, the integration of massive devices into wireless networks for communication, sensing, and energy harvesting tasks is becoming increasingly prevalent. This trend has led to the emergence of Integrated Sensing, Communication, and Power Transmission (ISCPT) systems. However, with the proliferation of functionalities within these systems, power consumption has become a critical concern. In this paper we aim to minimize power consumption by optimizing transmit beamforming while ensuring quality of service (QoS), meeting the Cramér–Rao bound (CRB) for radar target sensing accuracy, subject to energy harvesting constraints. To solve the non-convex problem, semidefinite relaxation (SDR) is proposed. Numerical results show that our proposed algorithm can reduce power consumption and satisfy the QoS and CRB constraints.

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.

The Integrated Sensing, Communication, and Power Transmission (ISCPT) system is crucial for next-generation intelligent communication, enabling efficient spectrum management. By utilizing movable antennas (MAs), ISCPT enhances reliability and flexibility, supporting various intelligent Internet of Things (IoT) scenarios. While joint optimization of parameters like beamforming and antenna positioning improves performance, it also introduces computational complexity that may affect efficiency. To enable flexible beamforming, we propose a novel deep reinforcement learning (DRL) architecture where heterogeneous agents independently adjust antenna configurations. This approach improves sensing accuracy, communication reliability, and power transfer efficiency, enhancing system capabilities and adaptability to dynamic environments. This work lays the foundation for wireless systems that integrate intelligent communication, sensing, and power transfer, offering improved performance in real-world applications.

Next-generation communication systems demand the integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACSs) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS-based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF)-based target detection and parameter estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system’s sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by an iterative method. Furthermore, to maximize the system’s sensing performance under communication and PT quality of service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, which is solved using through the SDR technique. The simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), the waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and the beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS.