{"ID":2879367,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16218","arxiv_id":"2508.16218","title":"Hybrid Precoding Revisited: Low-Dimensional Subspace Perspective for MU-MIMO Systems","abstract":"This letter presents a low-complexity hybrid precoding framework for multiuser multiple-input multiple-output (MIMO) systems by leveraging a low-dimensional subspace property. Under the low-dimensional subspace perspective, we first identify an unconstrained optimal radio-frequency (RF) precoder. We then optimize a hybrid precoder via a reduced-complexity precoding method. We further extend the proposed framework to (i) a dynamic-subarray antenna partitioning algorithm that adaptively allocates subsets of antennas associated with RF chains, and (ii) a channel covariance-based approach to exploit statistical channel state information at a transmitter (CSIT), ensuring robustness with partial CSIT. Simulations validate that our proposed algorithms achieve superior performance while significantly reducing complexity compared to existing methods.","short_abstract":"This letter presents a low-complexity hybrid precoding framework for multiuser multiple-input multiple-output (MIMO) systems by leveraging a low-dimensional subspace property. Under the low-dimensional subspace perspective, we first identify an unconstrained optimal radio-frequency (RF) precoder. We then optimize a hyb...","url_abs":"https://arxiv.org/abs/2508.16218","url_pdf":"https://arxiv.org/pdf/2508.16218v2","authors":"[\"Mintaek Oh\",\"Jinseok Choi\"]","published":"2025-08-22T08:42:51Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}