Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach

TL;DR

This paper presents a novel multi-modal sensing and federated learning framework for online precoding in wireless systems, significantly reducing pilot sequence length and adapting to data heterogeneity.

Contribution

It introduces a heterogeneous multi-vehicle sensing scheme combined with vertical federated learning and semi-supervised online updating for efficient precoding.

Findings

Achieves 90.6% reduction in pilot sequence length.

Closely approximates traditional optimization performance.

Effectively handles data heterogeneity with VFL.

Abstract

This paper investigates a heterogeneous multi-vehicle, multi-modal sensing (H-MVMM) aided online precoding problem. The proposed H-MVMM scheme utilizes a vertical federated learning (VFL) framework to minimize pilot sequence length and optimize the sum rate. This offers a promising solution for reducing latency in frequency division duplexing systems. To achieve this, three preprocessing modules are designed to transform raw sensory data into informative representations relevant to precoding. The approach effectively addresses local data heterogeneity arising from diverse on-board sensor configurations through a well-structured VFL training procedure. Additionally, a label-free online model updating strategy is introduced, enabling the H-MVMM scheme to adapt its weights flexibly. This strategy features a pseudo downlink channel state information label simulator (PCSI-Simulator), which is trained using a semi-supervised learning (SSL) approach alongside an online loss function. Numerical results show that the proposed method can closely approximate the performance of traditional optimization techniques with perfect channel state information, achieving a significant 90.6\% reduction in pilot sequence length.