arXiv:2606.16607v1 Announce Type: cross Abstract: This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) systems with limited feedback resources. The main challenge lies in obtaining a compact and efficient representation of the CSI given that it exhibits strong temporal correlation across successive snapshots. Existing memoryless compression models do not exploit this property, while simple temporal extensions often incorporate multiple observations without explicitly mo
The increasing demand for efficient wireless communication and the rise of AI-driven optimization techniques are converging, making neural network-based CSI compression a timely area of research.
Efficient CSI compression is critical for scaling massive MIMO systems and future wireless communication, directly impacting network capacity, latency, and the proliferation of connected devices, which underpins the AI compute cycle.
This research introduces a context-aware Markov VAE that better exploits temporal correlations in CSI, potentially leading to more compact and efficient data transmission in advanced wireless networks.
- · Telecommunications companies
- · AI hardware manufacturers
- · Wireless infrastructure providers
- · Edge computing sector
- · Legacy wireless compression techniques
- · Companies reliant on less efficient spectral usage
Improved spectral efficiency and higher data rates across 5G and future wireless standards.
Reduced operational costs for telecommunication providers due to optimized resource utilization and enhanced network performance.
Accelerated adoption of data-intensive AI applications at the edge enabled by more robust and lower-latency wireless backbones.
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Read at arXiv cs.LG