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

Area of Science:

• Wireless Communications
• Signal Processing
• Information Theory

Background:

• Accurate channel state information (CSI) is crucial for advanced wireless systems.
• Current methods treat CSI compression and prediction separately, neglecting channel aging in standardized pipelines.

Purpose of the Study:

• To propose a unified framework integrating contrastive predictive coding (CPC) into 3GPP-compliant CSI compression.
• To address channel aging by enabling temporal representation learning without increasing feedback overhead.

Main Methods:

• Developed a unified compression-prediction framework using contrastive predictive coding (CPC).
• Employed a combined 1-SGCS and InfoNCE objective for joint optimization of reconstruction fidelity and temporal predictive coherence.
• Introduced two variants: CPC-before-compression and CPC-after-compression.

Main Results:

• CPC-before-compression achieved over 90% reconstruction accuracy with 32x lower decoder GFLOPs.
• CPC-after-compression maintained an identical encoder footprint and 64-bit feedback overhead.
• Evaluations on 3GPP datasets demonstrated significant computational efficiency and age-aware feedback.

Conclusions:

• The unified framework provides an age-aware, computationally efficient CSI feedback solution.
• Integrating CPC directly into the 3GPP pipeline enhances CSI compression and prediction capabilities.
• The proposed method offers a practical solution for next-generation wireless systems.