mlsanityData to 14 Oct 2026

Universal Auto-encoder Framework for MIMO CSI Feedback

arXiv 2403.00299 ↗First posted 1 Mar 2024cs.IT cs.AI cs.LGcited by 0

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Abstract (arXiv)

Existing auto-encoder (AE)-based channel state information (CSI) frameworks have focused on a specific configuration of user equipment (UE) and base station (BS), and thus the input and output sizes of the AE are fixed. However, in the real-world scenario, the input and output sizes may vary depending on the number of antennas of the BS and UE and the allocated resource block in the frequency dimension. A naive approach to support the different input and output sizes is to use multiple AE models, which is impractical for the UE due to the limited HW resources. In this paper, we propose a universal AE framework that can support different input sizes and multiple compression ratios. The proposed AE framework significantly reduces the HW complexity while providing comparable performance in terms of compression ratio-distortion trade-off compared to the naive and state-of-the-art approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 11 references that are in the map. First posted on arXiv 1 Mar 2024.

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References · 2

  1. 2022Overview of Deep Learning-based CSI Feedback in Massive MIMO Systemscited by 37
  2. 2022A Spatially Separable Attention Mechanism for massive MIMO CSI Feedbackcited by 7

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