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

arXiv: https://arxiv.org/abs/2607.05419 · HTML: https://mlsanity.com/paper/2607.05419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2003.04684/) (2020)
- [Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2011.07242/) (2020)
- [An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting](https://mlsanity.com/paper/1912.10608/) (2019)
- [Learning the CSI Denoising and Feedback Without Supervision](https://mlsanity.com/paper/2104.05002/) (2021)
- [Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG](https://mlsanity.com/paper/2403.02651/) (2024)
- [Deep Learning for CSI Feedback: One-Sided Model and Joint Multi-Module Learning Perspectives](https://mlsanity.com/paper/2405.05522/) (2024)
- [On the Combination of AI and Wireless Technologies: 3GPP Standardization Progress](https://mlsanity.com/paper/2407.10984/) (2024)
- [AI for CSI Prediction in 5G-Advanced and Beyond](https://mlsanity.com/paper/2504.12571/) (2025)
- [Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework](https://mlsanity.com/paper/2306.01787/) (2023)
- [Molecular Absorption-Aware User Assignment, Spectrum, and Power Allocation in Dense THz Networks with Multi-Connectivity](https://mlsanity.com/paper/2408.03451/) (2024)
- [TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation](https://mlsanity.com/paper/2411.06291/) (2024)

## Abstract (arXiv)

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