Integrating Terrestrial and Non-Terrestrial Networks for Sustainable 6G Operations: A Latency-Aware Multi-Tier Cell-Switching Approach

TL;DR

This paper proposes a multi-tier, AI-enhanced cell-switching strategy that integrates terrestrial and non-terrestrial networks, including satellites, HAPS, and UAVs, to improve energy efficiency, coverage, and flexibility in 6G networks.

Contribution

It introduces a novel multi-tier cell-switching approach that incorporates non-terrestrial networks and AI for dynamic offloading and energy optimization in future cellular networks.

Findings

Significant reduction in network power consumption.

Enhanced coverage and capacity through NTN integration.

Improved user satisfaction and network flexibility.

Abstract

Sustainability is paramount in modern cellular networks, which face significant energy consumption challenges from rising mobile traffic and advancements in wireless technology. Cell-switching, well-established in literature as an effective solution, encounters limitations such as inadequate capacity and limited coverage when implemented through terrestrial networks (TN). This study enhances cell-switching by integrating non-terrestrial networks (NTN), including satellites (used for cell-switching for the first time), high altitude platform stations (HAPS), and uncrewed aerial vehicles (UAVs) into TN. This integration significantly boosts energy savings by expanding capacity, enhancing coverage, and increasing operational flexibility. We introduce a multi-tier cell-switching approach that dynamically offloads users across network layers to manage energy effectively and minimize delays, accommodating diverse user demands with a context aware strategy. Additionally, we explore the role of artificial intelligence (AI), particularly generative AI, in optimizing network efficiency through data compression, handover optimization between different network layers, and enhancing device compatibility, further improving the adaptability and energy efficiency of cell-switching operations. A case study confirms substantial improvements in network power consumption and user satisfaction, demonstrating the potential of our approach for future networks.