人工智能、环境反向散射通信与非地面网络：一种6G融合
Artificial Intelligence, Ambient Backscatter Communication and Non-Terrestrial Networks: A 6G Commixture
AI总结：
本文探讨了人工智能（AI）、环境反向散射通信（AmBC）与非地面网络（NTN）三者融合以促进NTN与地面网络（TN）集成的方法，并分析表明该三元关系显著提升了网络的能效（EE）。
摘要：
Non-Terrestrial Networks (NTN)（非地面网络）的出现是对 International Mobile Telecommunications 2030 (IMT-2030) 框架的强有力响应，能够提供支持可靠、可持续和弹性通信系统的先进无缝连接。然而，将 NTN 与 Terrestrial Networks (TN)（地面网络）集成需要对现有蜂窝基础设施进行大量改动，以应对 NTN 实施固有的挑战。此外，Ambient Backscatter Communication (AmBC)（环境反向散射通信）通过改变和反射环境 Radio Frequency (RF) 信号，将其传输给目标接收者，展现出有效集成 NTN 和 TN 的巨大潜力。同时，AmBC 也受到其在功率、干扰及其他相关因素方面的限制。相比之下，Artificial Intelligence (AI)（人工智能）在无线网络中的应用展现出通过利用海量数据集进行预测性分析的巨大潜力。AI 技术能够实现网络参数的实时优化，缓解 AmBC 中的干扰和功率限制。这些预测模型还增强了 NTN 和 TN 的自适应集成，推动了网络可靠性和 Energy Efficiency (EE)（能效）的显著提升。本文全面研究了 AI、AmBC 和 NTN 的融合如何促进 NTN 和 TN 的集成。我们还提供了详尽的分析，表明基于这种三元关系，EE 实现了显著提升。
英文摘要：
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 Artificial Intelligence (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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
