  Wi-Fi channel measurements across different bands, e.g., sub-7-GHz and 60-GHz
bands, are asynchronous due to the uncoordinated nature of distinct standards
protocols, e.g., 802.11ac/ax/be and 802.11ad/ay. Multi-band Wi-Fi fusion has
been considered before on a frame-to-frame basis for simple classification
tasks, which does not require fine-time-scale alignment. In contrast, this
paper considers asynchronous sequence-to-sequence fusion between sub-7-GHz
channel state information (CSI) and 60-GHz beam signal-to-noise-ratio~(SNR)s
for more challenging tasks such as continuous coordinate estimation. To handle
the timing disparity between asynchronous multi-band Wi-Fi channel
measurements, this paper proposes a multi-band neural dynamic fusion (NDF)
framework. This framework uses separate encoders to embed the multi-band Wi-Fi
measurement sequences to separate initial latent conditions. Using a
continuous-time ordinary differential equation (ODE) modeling, these initial
latent conditions are propagated to respective latent states of the multi-band
channel measurements at the same time instances for a latent alignment and a
post-ODE fusion, and at their original time instances for measurement
reconstruction. We derive a customized loss function based on the variational
evidence lower bound (ELBO) that balances between the multi-band measurement
reconstruction and continuous coordinate estimation. We evaluate the NDF
framework using an in-house multi-band Wi-Fi testbed and demonstrate
substantial performance improvements over a comprehensive list of single-band
and multi-band baseline methods.
