Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System
<|reference_start|>Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System: Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission. This paper proposes a practical channel estimation approach for the first time to an IRS-assisted multiuser ISAC system. The estimation problem in such a system is challenging since the sensing and communication (SAC) signals interfere with each other, and the passive IRS lacks signal processing ability. A two-stage approach is proposed to transfer the overall estimation problem into sub-ones, successively including the direct and reflected channels estimation. Based on this scheme, the ISAC base station (BS) estimates all the SAC channels associated with the target and uplink users, while each downlink user estimates the downlink communication channels individually. Considering a low-cost demand of the ISAC BS and downlink users, the proposed two-stage approach is realized by an efficient neural network (NN) framework that contains two different extreme learning machine (ELM) structures to estimate the above SAC channels. Moreover, two types of input-output pairs to train the ELMs are carefully devised, which impact the estimation accuracy and computational complexity under different system parameters. Simulation results reveal a substantial performance improvement achieved by the proposed ELM-based approach over the least-squares and NN-based benchmarks, with reduced training complexity and faster training speed.<|reference_end|>
@article{liu2024extreme,
    title={Extreme Learning Machine-based Channel Estimation in IRS-Assisted
  Multi-User ISAC System},
    author={Yu Liu, Ibrahim Al-Nahhal, Octavia A. Dobre, Fanggang Wang, and
  Hyundong Shin},
    journal={Published in IEEE Transactions on Communications, vol. 71, no. 12,
  pp. 6993-7007, Dec. 2023},
    year={2024},
    doi={10.1109/TCOMM.2023.3308150},
    archivePrefix={arXiv},
    eprint={2402.09440},
    primaryClass={eess.SP cs.LG}
}