Deep Learning for Joint Pilot, Channel Feedback and Sub-Array Hybrid Beamforming in FDD Massive MU-MIMO-OFDM Systems
Year: 2026
Type: article
Abstract: In frequency division duplex (FDD) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the sub-array multi-user (MU) hybrid beamforming architecture is highly attractive because of its low hardware cost and high energy efficiency. However, downlink channel state information (CSI) acquisition and hybrid beamformer optimization remain challenging due to the large feedback overhead and the non-convexity of the beamforming design. To address these issues, we propose an end-to-end deep learning (DL) framework that jointly optimizes pilot training, CSI feedback, and hybrid beamforming, overcoming the limitations of conventional independently designed modules. At the core of the network, we introduce the star efficient location attention (StarELA) module, which combines the implicit high-dimensional representation capability of star operations (element-wise multiplication) with the fine-grained feature localization of efficient location attention (ELA). In addition, for wideband digital beamformer generation, we exploit inter-subcarrier correlation and design a frequency–domain seed generation and interpolation upsampling strategy, which significantly reduces network parameters. Experimental results show that the proposed method approaches the upper-bound performance of conventional hybrid beamforming with ideal CSI, while consistently outperforming existing benchmark methods.
Source: Electronics
Authors: Kai Zhao, Haiyi Wu, Wei Yao, Yong Xiong
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7138269361