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Many wireless networks, including 5G NR (New Radio) and future beyond 5Gcellular systems, are expected to operate on multiple frequency bands. Thispaper considers the band assignment (BA) problem in dual-band systems, wherethe basestation (BS) chooses one of the two available frequency bands(centimeter-wave and millimeter-wave bands) to communicate with the userequipment (UE). While the millimeter-wave band might offer higher data rate,there is a significant probability of outage during which the communicationshould be carried on the (more reliable) centimeter-wave band. With mobility,the BA can be perceived as a sequential problem, where the BS uses previouslyobserved information to predict the best band for a future time step.  We formulate the BA as a binary classification problem and propose supervisedMachine Learning (ML) solutions. We study the problem when both the BS and theUE use (i) omnidirectional antennas and (ii) both use directional antennas. Inthe omnidirectional case, we derive analytical benchmark solutions based on theGaussian Process (GP) assumption for the inter-band shadow fading. In thedirectional case, where the labeling is shown to be complex, we propose anefficient labeling approach based on the Viterbi Algorithm (VA). We compare theperformances for two channel models: (i) a stochastic channel and (ii) aray-tracing based channel.

Supervised ML Solution for Band Assignment in Dual-Band Systems with Omnidirectional and Directional Antennas