Intelligent Reflecting Surface Enabled Integrated Sensing, Communication and Computation

Xu, Sai; Du, Yanan; Zhang, Jiliang; Liu, Jinlong; Wang, Jiangzhou; Zhang, Jie

doi:10.1109/twc.2023.3296519

Cited by 8 publications

(2 citation statements)

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“…We use the SINR as the sensing performance indicator [ 33 ]. Therefore, the SINR of the radar can be given by

…”

Section: System Modelmentioning

confidence: 99%

“…Prioritized sampling At each time slot, the evaluate network obtains environmental state s t and then outputs the action a t . The Q value is adopted to describe the long-term reward of executing a t , which can be calculated by the Bellman equation [38] Q µ (s t , a t ) = E[r t (s t , a t ) + γQ µ (s t+1 , a t+1 )], (33) where µ : S ← A is the deterministic policy function, and the actor function µ(s|ω µ ) works by mapping a state to an action to specific current policy. The DRL agent interacts with the environment to find the optimal action corresponding to the maximum Q value…”

Section: Agent Environmentmentioning

confidence: 99%

See 1 more Smart Citation

Joint Task Offloading and Resource Allocation for Intelligent Reflecting Surface-Aided Integrated Sensing and Communication Systems Using Deep Reinforcement Learning Algorithm

Yang,

Wei,

Wang

2023

Sensors

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This paper investigates an intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework to cope with the problem of spectrum scarcity and poor wireless environment. The main goal of the proposed framework in this work is to optimize the overall performance of the system, including sensing, communication, and computational offloading. We aim to achieve the trade-off between system performance and overhead by optimizing spectrum and computing resource allocation. On the one hand, the joint design of transmit beamforming and phase shift matrices can enhance the radar sensing quality and increase the communication data rate. On the other hand, task offloading and computation resource allocation optimize energy consumption and delay. Due to the coupled and high dimension optimization variables, the optimization problem is non-convex and NP-hard. Meanwhile, given the dynamic wireless channel condition, we formulate the optimization design as a Markov decision process. To tackle this complex optimization problem, we proposed two innovative deep reinforcement learning (DRL)-based schemes. Specifically, a deep deterministic policy gradient (DDPG) method is proposed to address the continuous high-dimensional action space, and the prioritized experience replay is adopted to speed up the convergence process. Then, a twin delayed DDPG algorithm is designed based on this DRL framework. Numerical results confirm the effectiveness of proposed schemes compared with the benchmark methods.

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“…We use the SINR as the sensing performance indicator [ 33 ]. Therefore, the SINR of the radar can be given by

…”

Section: System Modelmentioning

confidence: 99%

Section: Agent Environmentmentioning

confidence: 99%