DNN Based Beam Selection in mmW Heterogeneous Networks

TL;DR

This paper proposes a deep neural network-based method for mmW beam and base station selection in heterogeneous networks, reducing latency and achieving high accuracy without relying on UE location information.

Contribution

It introduces a joint DNN architecture that predicts mmW base station and beam selection using sub-6GHz CSI features, avoiding exhaustive search.

Findings

Achieves at least 85% accuracy in selection tasks

Reduces latency compared to exhaustive search

Demonstrates better performance in simulations

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.