Abstract
Intelligent reconfigurable surface (IRS) becomes a promising technology for improving spectrum efficiency and reducing the energy consumption in the next-generation networks. To reap the passive beamforming gain of IRS, accurate channel estimation is essentially required. However, the existing channel estimation methods for the IRS-assisted orthogonal frequency division multiplexing (OFDM) systems suffer from serious performance loss, due to using the least-squares (LS) estimator and ignoring the channel sparsity in the time domain. Another drawback of the existing methods is that the channel estimation and data detection are separated from each other so that any inaccurate channel estimation would cause an error accumulation problem for the data detection. To overcome these drawbacks, we propose a novel joint sparse channel estimation and data detection method for the IRS-assisted OFDM systems. The novelties of the proposed method are twofold: (i) present a new common sparse representation model for the joint channel estimation and data detection, which can utilize the common channel sparsity between the pilots and data streams to significantly improve the channel estimation performance and reduce the training overhead; and (ii) propose an efficient variational Bayesian inference (VBI) framework to simultaneously perform the Bayesian inference for the sparse channel recovery and data detection, where the coupling effect caused by the reflection pattern is trickly evaded by adopting an additional column-independent factorization. Besides exploiting the common sparsity for the channel estimation and data detection, the proposed method also avoids using the LS estimator, and thus its performance can be greatly enhanced. Simulation results verify the superiority of the proposed method.
Published Version
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