Abstract
We consider a heterogeneous cellular network wherein multiple small cell millimeter wave (mmW) base stations (BSs) coexist with legacy sub-6GHz macro BSs. In the mmW band, small cells use multiple narrow beams to ensure sufficient coverage and User Equipments (UEs) have to select the best small cell and the best beam in order to access the network. This process usually based on exhaustive search may introduce unacceptable latency. In order to address this issue, we rely on the sub-6GHz macro BS support and propose a deep neural network (DNN) architecture that utilizes basic components from the Channel State Information (CSI) of sub-6GHz network as input features. The output of the DNN is the mmW BS and beam selection that can provide the best communication performance. In the set of features, we avoid using the UE location, which may not be readily available for every device. We formulate a mmW BS selection and beam selection problem as a classification and regression problem respectively and propose a joint solution using a branched neural network. The numerical comparison with the conventional exhaustive search results shows that the proposed design demonstrate better performance than exhaustive search in terms of latency with at least 85% accuracy.
Supported by the EXTRANGE4G project with company ETELM funded by DGA. This work has been performed at LINCS laboratory.
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Notes
- 1.
UEs may be equipped with several antennas but we don’t address in this paper the beam alignment problem and we focus on the beam selection at the BS. Once the BS beam is known, the UE may for example perform exhaustive search to select its own beam.
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Jagyasi, D., Coupechoux, M. (2021). DNN Based Beam Selection in mmW Heterogeneous Networks. In: Lasaulce, S., Mertikopoulos, P., Orda, A. (eds) Network Games, Control and Optimization. NETGCOOP 2021. Communications in Computer and Information Science, vol 1354. Springer, Cham. https://doi.org/10.1007/978-3-030-87473-5_16
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