MAP-Based Task-Oriented Precoding for Multiuser Communication

2026-06-24

AI Summary

This work addresses the trade-off between communication efficiency and classification performance in distributed classification tasks over wireless channels impaired by channel distortions. It proposes a task-oriented multi-user communication framework driven by maximum a posteriori (MAP) inference, which uniquely integrates the MAP criterion with a class-mean separation objective. By jointly optimizing learnable feature extraction and low-complexity precoding design, the method directly enhances class separability after channel distortion, bypassing conventional approaches that rely on covariance estimation or signal reconstruction. The proposed scheme eliminates the need for repeated covariance inversion and eigendecomposition, achieving significantly reduced computational complexity while outperforming existing joint communication-and-learning designs in classification accuracy.

Abstract

We propose a task-oriented multiuser wireless communication framework for distributed classification based on a MAP-driven system design under wireless channel impairments. By deriving a tractable class-mean separation objective, the proposed approach enables low-complexity design of both learning-based feature extraction and precoding strategies. Unlike existing covariance-based and reconstruction-oriented methods, the proposed formulation avoids repeated covariance inversions and eigen-decomposition operations while directly improving class separability after channel distortion. Simulation results demonstrate that the proposed method achieves higher classification accuracy than existing schemes, while simultaneously reducing computational complexity.

Problem

• task-oriented communication
• multiuser precoding
• class separability
• wireless channel impairments
• distributed classification

Innovation

• task-oriented precoding
• MAP-based design
• class-mean separation
• multiuser communication
• low-complexity optimization