Clustering is considered an important issue in Vehicular Ad‐hoc Networks (VANETs) to ensure the network robustness and throughput. Nevertheless, in the literature, there is still a lack of understanding of how relative mobility between cluster members and the cluster head affects the throughput of VANET clusters. In this letter, we present a model to characterize the throughput of VANET clusters, by taking into account the relative mobility effect, which implicitly accounts for the Doppler effect. The numerical analysis shows that there is a large gap between the throughput of the model with/without consideration of relative mobility, which indicates the necessity to develop clustering algorithms that minimize intra‐cluster mobility.
Self‐Organized Networks (SON) are a crucial feature to make 5G more efficient in 3GPP Release 16. 5G SON inherits from LTE self‐configuration, self‐optimization, and self‐healing functions, and also expands to new use cases and relies on a service‐based architecture and data analytics network functions. SON algorithms are, however, not part of the standard. In this article, we thus present a novel reinforcement learning approach for distributed load balancing in heterogeneous networks that use cell range expansion for user association and Almost Blank Subframe (ABS) for interference management. We model the interactions among the base stations for load balancing as a near‐potential game. Using the proposed distributed learning algorithms, players reach an optimal Pure Nash Equilibrium (PNE). We provide sufficient conditions under which the learning algorithms converge to the optimal PNE. By running extensive simulations, we show that the proposed algorithms converge within few hundreds of iterations. Finally, we show that outage can be controlled and a better load balancing can be achieved by introducing ABS.
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