SRS-Aided Joint Multi-Domain CSI-RS Design, Feedback and Precoding for E2E Learning cmWave Massive MIMO

Massive multiple-input multiple-output (MIMO) systems offer high spectral efficiency but generate high-dimensional downlink channel state information (CSI), posing challenges for real-time channel acquisition and precoding, particularly in centimeter-wave (cmWave) vehicle-to-infrastructure (V2I) communications where rapid channel variations due to high mobility further complicate CSI acquisition. To address this, we propose an uplink sounding reference signal (SRS)-aided joint design of downlink CSI reference signal (CSI-RS), CSI feedback, and base-station (BS) precoding with end-to-end (E2E) deep learning. Firstly, we design a multi-axis multi-layer perceptron (MAXIM)-based multi-domain CSI-RS network, which takes the uplink sounding reference signals (SRS) as input and outputs a frequency-, beam-, and port-domain projection matrices. Secondly, user equipment (UE) then compresses/quantizes the received CSI-RS and feeds a compact representation to the BS. Thirdly, at the BS, two complementary branches produce candidate precoders: one is namedfeedback-onlyprecoding network driven by quantized CSI feedback, and the other is namedSRS-onlyprecoding network driven by uplink SRS. These candidate precoders are subsequently combined by a precoding fusion network to yield the final transmit precoder. Finally, all these modules are trained with a spectral-efficiency-oriented loss in an E2E deep learning manner. Simulation results in high-mobility vehicular scenarios demonstrate that the proposed approach effectively harnesses both SRS-derived and CSI-feedback information, achieving markedly better performance than conventional baselines, especially under severe channel aging conditions.