@inproceedings{fe153ab5089644e296b175ae7527891f,
title = "Optimization of Power Allocation for OFDM Based ISAC Systems",
abstract = "Integrated sensing and communication (ISAC) has emerged as a promising technology for the sixth-generation (6G) mobile communication system. This paper investigates the optimization of power allocation for an orthogonal frequency division multiplexing (OFDM)-based ISAC system, aimed at minimizing the symbol error rate (SER) for communication and the Cram{\'e}r-Rao lower bounds (CRLBs) for joint delay and Doppler estimation. We first reveal the conflicting requirements for power allocation by communication and sensing objectives by finding the optimal power allocation for each individual objective. Then, we resort to deep learning to learn the power allocation that minimizes the weighted sum of the three objective functions. Simulation results validate the theoretical analysis and demonstrate the gain of the proposed scheme in reducing SER and CRLBs over baseline schemes.",
keywords = "Integrated sensing and communication, OFDM, deep learning, power allocation",
author = "Xuan Wang and Shengqian Han",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Global Communications Conference, GLOBECOM 2024 ; Conference date: 08-12-2024 Through 12-12-2024",
year = "2024",
doi = "10.1109/GLOBECOM52923.2024.10901655",
language = "英语",
series = "Proceedings - IEEE Global Communications Conference, GLOBECOM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5387--5392",
booktitle = "GLOBECOM 2024 - 2024 IEEE Global Communications Conference",
address = "美国",
}