Abstract:

In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7 GHz channel state information (CSI) and 60 GHz beam SNR for more challenging down- stream tasks such as continuous regression. To handle the timing disparity between the two channel measurements, we extend our recently proposed dual-decoder neural dynamic (DDND) framework with latent ordinary differential equations (ODEs), align the distinct latent dynamic states at the same time instances, and introduce a post-ODE fusion framework. The resulting neural dynamic fusion (NDF) framework is trained in an end-to-end fashion with a modified variational autoencoder loss function. Evaluation over a newly collected in-house multi-band Wi-Fi dataset shows the advantage of the proposed NDF method over frame-based and DDND methods.