[論文レビュー] TinySense: Effective CSI Compression for Scalable and Accurate Wi-Fi Sensing

Toan Gian, Dung Thi Phuong Tran+3 more|arXiv (Cornell University)|Jan 22, 2026

Indoor and Outdoor Localization Technologies被引用数 0

ひとこと要約

TinySense は VQGAN ベースの CSI 圧縮フレームワークを導入し、適応コードブックと Transformer ベースの復元モジュールにより、帯域幅と遅延を低減したスケーラブルで高精度な Wi-Fi センシングを実現します。

ABSTRACT

With the growing demand for device-free and privacy-preserving sensing solutions, Wi-Fi sensing has emerged as a promising approach for human pose estimation (HPE). However, existing methods often process vast amounts of channel state information (CSI) data directly, ultimately straining networking resources. This paper introduces TinySense, an efficient compression framework that enhances the scalability of Wi-Fi-based human sensing. Our approach is based on a new vector quantization-based generative adversarial network (VQGAN). Specifically, by leveraging a VQGAN-learned codebook, TinySense significantly reduces CSI data while maintaining the accuracy required for reliable HPE. To optimize compression, we employ the K-means algorithm to dynamically adjust compression bitrates to cluster a large-scale pre-trained codebook into smaller subsets. Furthermore, a Transformer model is incorporated to mitigate bitrate loss, enhancing robustness in unreliable networking conditions. We prototype TinySense on an experimental testbed using Jetson Nano and Raspberry Pi to measure latency and network resource use. Extensive results demonstrate that TinySense significantly outperforms state-of-the-art compression schemes, achieving up to 1.5x higher HPE accuracy score (PCK20) under the same compression rate. It also reduces latency and networking overhead, respectively, by up to 5x and 2.5x. The code repository is available online at here.

研究の動機と目的

• Motivate scalable, privacy-preserving Wi-Fi sensing via efficient CSI transmission.
• Develop a task-aware compression framework that preserves HPE accuracy under high compression.
• Enable adaptive bitrate control through codebook resizing and clustering.
• Improve robustness to packet loss with a Transformer-based missing-index recovery mechanism.

提案手法

• Encode CSI on-device using a VQGAN-based latent representation to produce VQ indices.
• Compress the VQ indices by mapping to a shared codebook and transmitting a compact bitstream.
• Dynamically resize the codebook with K-means to achieve variable bitrate and quality trade-offs.
• Decode on the server to reconstruct CSI and perform HPE via an estimator and a decoder.
• Use a second-stage Transformer to predict missing VQ indices under transmission losses for robustness.

Figure 1 : Overview of Cloud-based Wi-Fi Sensing.

実験結果

リサーチクエスチョン

• RQ1 How can CSI data be compressed to minimize bandwidth while preserving HPE accuracy?
• RQ2 Can a shared learned codebook enable flexible Bitrate control and robust reconstruction under network conditions?
• RQ3 Does introducing a Transformer-based missing-index prediction improve robustness to packet loss?
• RQ4 What are the end-to-end latency and networking overhead benefits of TinySense compared to state-of-the-art methods?

主な発見

• TinySense は同じ圧縮率で EfficientFi および RSCNet より最大 1.5x 高い PCK20 を達成。
• EfficientFi と比較して待ち時間を最大 5x、ネットワーキングオーバーヘッドを最大 2.5x 削減。
• K-means による適応コードブックのリサイズは、再構成品質を維持しつつ可変ビットレートを実現。
• 欠落インデックスが存在する場合、Transformer ベースの復元は非 Transformer 変種より性能をはるかに良く維持。
• MM-Fi および Wi-Pose データセットでの実験は、NMSE および HPE 指標の一貫した利得を示す。

Figure 2 : Illustration of the proposed $\mathtt{TinySense}$ framework: (a) the overall compression pipeline, (b) the K-means clustering algorithm to adjust the codebook size for variable bitrates, and (c) a transformer approach for predicting lost VQ indices.