Related papers: E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion

E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion

URL: http://arxiv.org/abs/2509.19312v1
Date: Tue, 09 Sep 2025 11:25:51 GMT
Title: E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion
Authors: Minghui Wu, Zhen Gao,
Abstract summary: We propose an end-to-end (E2E) uplink-downlink CSI fusion precoding network.<n>It jointly models downlink CSI reference signal (CSI-RS) design, CSI feedback, and base-station (BS) precoding.<n>Results show that the proposed approach effectively harnesses both SRS-derived information and UE feedback.
Score: 4.957031375980409
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Massive multiple-input multiple-output (MIMO) promises high spectral efficiency but also leads to high-dimensional downlink channel state information (CSI), which complicates real-time channel acquisition and precoding. To address this, we propose an end-to-end (E2E) uplink-downlink CSI fusion precoding network that jointly models downlink CSI reference signal (CSI-RS) design, CSI feedback, and base-station (BS) precoding within a single E2E neural architecture. Concretely, a projection network built on the MAXIM architecture takes uplink sounding reference signals (SRS) as input and outputs frequency-, beam-, and port-domain projection matrices for designing downlink CSI-RS. User equipment (UE) then compresses/quantizes the resulting CSI-RS observations and feeds back a compact representation. At the base station (BS), two complementary branches produce candidate precoders: one is a feedback-only precoding network driven by quantized downlink observations, and the other is an SRS-only precoding network driven by uplink SRS. These candidate precoders are subsequently combined by a fusion precoding network to yield the final transmit precoder. All the modules are trained with a spectral-efficiency-oriented loss under a three-stage schedule. Simulation results show that the proposed approach effectively harnesses both SRS-derived information and UE feedback, achieving markedly better performance than conventional baselines.

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