In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks {under multipath propagation}. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modelling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms {both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method} in terms of channel estimation accuracy and achieves performance close to the derived Cramer-Rao lower bound.
@article{arxiv.2503.09393,
title = {Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification},
author = {Qi Wu and Zeping Sui and Hien Quoc Ngo and Qun Wan and Michail Matthaiou},
journal= {arXiv preprint arXiv:2503.09393},
year = {2025}
}