Artificial Intelligence, Ambient Backscatter Communication and Non-Terrestrial Networks: A 6G Commixture

Abstract: The advent of Non-Terrestrial Networks (NTN) represents a compelling response to the International Mobile Telecommunications 2030 (IMT-2030) framework, enabling the delivery of advanced, seamless connectivity that supports reliable, sustainable, and resilient communication systems. Nevertheless, the integration of NTN with Terrestrial Networks (TN) necessitates considerable alterations to the existing cellular infrastructure in order to address the challenges intrinsic to NTN implementation. Additionally, Ambient Backscatter Communication (AmBC), which utilizes ambient Radio Frequency (RF) signals to transmit data to the intended recipient by altering and reflecting these signals, exhibits considerable potential for the effective integration of NTN and TN. Furthermore, AmBC is constrained by its limitations regarding power, interference, and other related factors. In contrast, the application of AI within wireless networks demonstrates significant potential for predictive analytics through the use of extensive datasets. AI techniques enable the real-time optimization of network parameters, mitigating interference and power limitations in AmBC. These predictive models also enhance the adaptive integration of NTN and TN, driving significant improvements in network reliability and Energy Efficiency (EE). In this paper, we present a comprehensive examination of how the commixture of AI, AmBC, and NTN can facilitate the integration of NTN and TN. We also provide a thorough analysis indicating a marked enhancement in EE predicated on this triadic relationship.