Sustainable Vertical Heterogeneous Networks: A Cell Switching Approach With High Altitude Platform Station

The rapid growth of radio access networks (RANs), driven by evolving wireless technologies and increasing mobile traffic, poses significant energy consumption challenges and threatens the sustainability of future networks. In this context, vertical heterogeneous networks (vHetNets) offer a promising architectural solution to address these challenges. The present study explores a vHetNet configuration featuring a high altitude platform station (HAPS) that acts as a super macro base station (SMBS), along with a macro base station (MBS) and multiple small base stations (SBSs), in dense urban environments. We propose a novel HAPS-enhanced cell switching (CS) algorithm that optimizes network performance by selectively deactivating SBSs, considering their traffic load as well as the capacity and channel conditions of both the MBS and HAPS. The algorithm addresses a mixed-integer nonlinear programming (MINLP) problem, reformulated into a mixed-integer programming (MIP) problem, to reduce vHetNet energy consumption while preserving an outage-based quality of service (QoS) constraint. The novelty of this work lies in the formulation of a HAPS-enhanced CS framework and its evaluation under realistic 3GPP channel models. This combination differentiates our study from previous studies that either focused solely on terrestrial CS or relied on simplified propagation assumptions. Extensive simulation results demonstrate that the HAPS-enhanced CS approach achieves substantial energy savings as compared to benchmark methods, including All-ON, Terrestrial CS, and Sorting. In particular, compared to All-ON, the proposed approach reduces power consumption by up to 77% at low traffic loads and about 40% at high loads. Moreover, the HAPS-enhanced CS NoQoS variant, which relaxes the outage-based QoS constraints to allow further SBS deactivation, achieves even larger reductions of up to 90% at low load and 47% at high load. The algorithm also maintains high levels of served traffic, showing adaptability under varying load conditions and outage-based QoS requirements. By setting appropriate thresholds, the proposed algorithm effectively balances power efficiency and outage-based QoS, making it a scalable solution for sustainable 6G networks.