Power Source Allocation for RIS-aided Integrating Sensing, Communication, and Power Transfer Systems Based on NOMA
This paper proposes a novel communication system framework based on a reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transmission (ISCPT) communication system. RIS is used to improve transmission efficiency and sensing accuracy. In addition, non-orthogonal multiple access (NOMA) technology is incorporated in RIS-aided ISCPT systems to boost the spectrum utilization efficiency of RIS-aided ISCPT systems. We consider the power minimization problem of the RIS-aided ISCPT-NOMA system. Power minimization is achieved by jointly optimizing the RIS phase shift, decoding order, power splitting (PS) factor, and transmit beamforming while satisfying quality of service (QoS), radar target sensing accuracy, and energy harvesting constraints. Since the objective function and constraints in the optimization problem are non-convex, the problem is an NP-hard problem. To solve the non-convex problem, this paper proposes a block coordinate descent (BCD) algorithm. Specifically, the non-convex problem is divided into four sub-problems: i.e. the transmit beamforming, RIS phase shift, decoding order and PS factor optimization subproblems. We employ semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques to address the transmit beamforming optimization sub-problem. Subsequently, we leverage the alternating direction method of multipliers (ADMM) algorithm to solve the RIS phase shift optimization problem. As for the decoding order optimization, we provide a closed-form expression. For the PS factor optimization problem, the SCA algorithm is proposed. Simulation results illustrate the effectiveness of our proposed algorithm and highlight the balanced performance achieved across sensing, communication, and power transfer.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors
Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT). In this paper, we propose a UAV-aided ISAC framework and investigate three ro…
Integrated sensing and communicationISACRobust DesignResource Allocation for ISAC Networks with Application to Target Tracking
Future 6G networks are expected to empower communication systems by integrating sensing capabilities, resulting in integrated sensing and communication (ISAC) systems. However, this integration may exacerbate the data tr…
Integrated sensing and communicationISACJoint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G
User location is a piece of critical information for network management and control. However, location uncertainty is unavoidable in certain settings leading to localization errors. In this paper, we consider the user lo…
ClusteringDeep Reinforcement LearningManagementreinforcement-learning+1Dynamic Power Allocation in OFDM ISAC for Time of Arrival Estimation
Resource allocation in Integrated Sensing and Communication (ISAC) systems is critical for balancing communication and sensing performance. This paper introduces a novel dynamic power allocation strategy for Orthogonal F…
Integrated sensing and communicationISACGraph Neural Network Aided Deep Reinforcement Learning for Resource Allocation in Dynamic Terahertz UAV Networks
Terahertz (THz) unmanned aerial vehicle (UAV) networks with flexible topologies and ultra-high data rates are expected to empower numerous applications in security surveillance, disaster response, and environmental monit…
Deep Reinforcement LearningDisaster ResponseGraph Neural Network