Joint channel and data estimation for multiuser extremely large-scale MIMO systems

This paper proposes a joint channel and data estimation (JCDE) algorithm for uplink multiuser XL-MIMO systems. The initial channel estimation is formulated as a sparse reconstruction problem based on the angle and distance sparsity under the near-field propagation condition. This problem is solved using non-orthogonal pilots through an efficient low complexity two-stage compressed sensing algorithm. Furthermore, the initial channel estimates are refined by employing a JCDE framework driven by both non-orthogonal pilots and estimated data. The JCDE problem is solved by sequential EP algorithms, where the channel and data are alternately updated in an iterative manner.

Research paper

#6G #XLMIMO

In the channel estimation phase, integrating Bayesian inference with a model-based deterministic approach provides precise estimations to effectively exploit the near-field characteristics in the beam-domain. In the data estimation phase, a LMMSE-based filter is designed at each sub-array to address the correlation due to energy leakage in the beam-domain arising from the near-field effects. Numerical simulations reveal that the proposed initial channel estimation and JCDE algorithm outperforms the state-of-the-art approaches in terms of channel estimation, data detection, and computational complexity.

Available in IEEEXplore DOI: 10.1109/TWC.2025.3597278

Authors

Kabuto Arai, Koji Ishibashi – Advanced Wireless and Communication Research Center (AWCC), University of Electro-Communications, Japan

Hiroki Iimori, Paulo Valente Klaine, Szabolcs Malomsoky – Ericsson Research, Japan

Published in the IEEE Transactions on Wireless Communications ( Volume: 25)

Open Access, shared under a Creative Commons License

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Joint Channel and Data Estimation for Multiuser Extremely Large-Scale MIMO Systems

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