  We propose a deep learning-based method that uses spatial and temporal
information extracted from the sub-6GHz band to predict/track beams in the
millimeter-wave (mmWave) band. In more detail, we consider a dual-band
communication system operating in both the sub-6GHz and mmWave bands. The
objective is to maximize the achievable mutual information in the mmWave band
with a hybrid analog/digital architecture where analog precoders (RF precoders)
are taken from a finite codebook. Finding a RF precoder using conventional
search methods incurs large signalling overhead, and the signalling scales with
the number of RF chains and the resolution of the phase shifters. To overcome
the issue of large signalling overhead in the mmWave band, the proposed method
exploits the spatiotemporal correlation between sub-6GHz and mmWave bands, and
it predicts/tracks the RF precoders in the mmWave band from sub-6GHz channel
measurements. The proposed method provides a smaller candidate set so that
performing a search over that set significantly reduces the signalling overhead
compared with conventional search heuristics. Simulations show that the
proposed method can provide reasonable achievable rates while significantly
reducing the signalling overhead.
