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Millimeter-wave communication is widely seen as a promising option toincrease the capacity of vehicular networks, where it is expected thatconnected cars will soon need to transmit and receive large amounts of data.Due to harsh propagation conditions, mmWave systems resort to narrow beams toserve their users, and such beams need to be configured according to trafficdemand and its spatial distribution, as well as interference. In this work, weaddress the beam management problem, considering an urban vehicular networkcomposed of gNBs. We first build an accurate, yet tractable, system model andformulate an optimization problem aiming at maximizing the total network datarate while accounting for the stochastic nature of the network scenario. Thenwe develop a graph-based model capturing the main system characteristics anduse it to develop a belief propagation algorithmic framework, called CRAB, thathas low complexity and, hence, can effectively cope with large-scale scenarios.We assess the performance of our approach under real-world settings and showthat, in comparison to state-of-the-art alternatives, CRAB provides on averagea 50% improvement in the amount of data transferred by the single gNBs and upto 30% better user coverage.

A Belief Propagation Solution for Beam Coordination in MmWave Vehicular Networks