  Complementary to the fine-grained channel state information (CSI) from the
physical layer and coarse-grained received signal strength indicator (RSSI)
measurements, the mid-grained spatial beam attributes (e.g., beam SNR) that are
available at millimeter-wave (mmWave) bands during the mandatory beam training
phase can be repurposed for Wi-Fi sensing applications. In this paper, we
propose a multi-band Wi-Fi fusion method for Wi-Fi sensing that hierarchically
fuses the features from both the fine-grained CSI at sub-6 GHz and the
mid-grained beam SNR at 60 GHz in a granularity matching framework. The
granularity matching is realized by pairing two feature maps from the CSI and
beam SNR at different granularity levels and linearly combining all paired
feature maps into a fused feature map with learnable weights. To further
address the issue of limited labeled training data, we propose an
autoencoder-based multi-band Wi-Fi fusion network that can be pre-trained in an
unsupervised fashion. Once the autoencoder-based fusion network is pre-trained,
we detach the decoders and append multi-task sensing heads to the fused feature
map by fine-tuning the fusion block and re-training the multi-task heads from
the scratch. The multi-band Wi-Fi fusion framework is thoroughly validated by
in-house experimental Wi-Fi sensing datasets spanning three tasks: 1) pose
recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to
four baseline methods (i.e., CSI-only, beam SNR-only, input fusion, and feature
fusion) demonstrates the granularity matching improves the multi-task sensing
performance. Quantitative performance is evaluated as a function of the number
of labeled training data, latent space dimension, and fine-tuning learning
rates.
