Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks

2026-06-26

AI Summary

This work addresses the challenge of channel aging in current wireless communication systems, where channel state information (CSI) compression and prediction are typically handled in isolation. The paper proposes the first unified compression-prediction framework that integrates contrastive predictive coding (CPC) into a 3GPP-compliant CSI feedback pipeline. By jointly optimizing reconstruction fidelity and temporal prediction consistency, the approach enables time-aware representation learning without increasing feedback overhead. Two variants—CPC-before-Compression and CPC-after-Compression—are designed and evaluated on 3GPP-compliant datasets from Nokia, Oppo, and CATT. The former achieves over 90% reconstruction accuracy with 32× lower decoder computational complexity, while the latter significantly enhances performance under a fixed 64-bit feedback budget and unchanged encoder size.

Abstract

Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate problems, both in academia and in current 3GPP studies. Consequently, channel aging remains insufficiently addressed within standardized CSI feedback pipelines. In this article, we propose a unified compression-prediction framework that integrates Contrastive Predictive Coding (CPC) directly into the 3GPP-compliant CSI compression architecture. Instead of predicting high-dimensional CSI matrices, our approach forecasts future latent representations and jointly optimizes reconstruction fidelity and temporal predictive coherence via a combined 1-SGCS and InfoNCE objective. This design enables temporal representation learning without increasing feedback overhead. We present two variants: CPC-before-Compression, which performs autoregressive modeling on encoded features prior to quantization, and CPC-after-Compression, which shifts temporal modeling to the base-station to reduce the complexity of users'devices. Evaluations on 3GPP-compliant datasets from Nokia, Oppo, and CATT show that CPC-before-Compression achieves over 90% reconstruction accuracy with 32x lower decoder GFLOPs than the 3GPP baseline, while CPC-after-Compression preserves an identical encoder footprint and the same 64-bit feedback overhead. By unifying compression and prediction within a standardized pipeline, the proposed framework provides an age-aware, computationally efficient CSI feedback solution. The source code is publicly available at: https://github.com/AhmedRadwan02/cpc-3gpp

Problem

• Channel State Information
• CSI compression
• CSI prediction
• channel aging
• wireless networks

Innovation

• Contrastive Predictive Coding
• Channel State Information
• Joint Compression-Prediction
• 3GPP-compliant
• Temporal Representation Learning