Accurate and timely Channel State Information (CSI) is essential for next-generation wireless systems. However, existing works treat CSI compression and prediction as separate problems, leading to insufficient addressing of channel aging within standardized CSI feedback pipelines. This article proposes 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
Blogger's Review: This framework effectively integrates CSI prediction and compression, addressing the channel aging issue that traditional methods overlook. The innovative design leveraging CPC significantly enhances feedback efficiency and merits further attention and research in the wireless communication domain.