🤖 AI Summary
This study addresses the challenge of high energy consumption and the difficulty of implementing energy-saving base station shutdowns in dense urban cellular networks. To overcome this, the authors propose leveraging High-Altitude Platform Stations (HAPS) to form a “Hypercell” that replaces the coverage of multiple terrestrial macrocells, thereby enabling coordinated shutdown of both coverage-layer and capacity-layer base stations for network-wide energy savings. Innovatively repositioning HAPS from its conventional role in non-terrestrial networks (NTN) as a mere coverage extender to an enabler of joint coverage-and-capacity shutdown, the work introduces two HAPS–Hypercell pairing architectures to support distributed carrier shutdown mechanisms. Evaluations based on 3GPP-compliant modeling and realistic channel simulations demonstrate substantial reductions in network power consumption while also revealing limitations of direct HAPS integration, offering critical insights for future green communication strategies.
📝 Abstract
Energy consumption remains a dominant operational challenge for current and future cellular systems, especially in dense urban deployments. This paper investigates a novel role for non terrestrial network (NTN) high-altitude platform station (HAPS) as an enabler of energy-efficient operation rather than only coverage extension. We define the HAPS-Hypercell as a wide-area non-terrestrial layer that can assume the coverage role of multiple terrestrial macro-cells, enabling, for the first time, the shutdown of both capacity and coverage macro-cells. We develop a comprehensive third generation partnership project (3GPP)-compliant system model, along with two HAPS-Hypercell pairing architectures that capture the interplay among multiple layers, realistic channel conditions, and distributed carrier shutdown (CS) mechanisms. Our results show that the HAPS-Hypercell can effectively reduce overall network power consumption. We then identify key limitations of a straightforward HAPS integration, laying the groundwork for future optimization and providing key insights for next-generation CS operations.