Semantics-Aware Unified Terrestrial Non-Terrestrial 6G Networks

TL;DR

This work tackles the data-management bottleneck in Semantics-aware unified TN-NTNs by introducing a content- and query-aware framework that optimizes information utility in a multi-hop LEO satellite network. It formalizes semantic attributes (timeliness, relevance, and utility) and metrics (AoI, VAoI, QAoI, QVAoI), and develops an infinite-horizon MDP solved via Relative Value Iteration to yield a threshold-based optimal policy for transmission scheduling. The results show that semantics-aware policies, particularly those based on QVAoI, deliver fresher and more informative updates while dramatically reducing transmissions and energy consumption (up to ~73% savings) compared with state-of-the-art methods. These findings highlight the practical potential of semantic-driven control to enable energy-efficient, QoS-assured 6G TN-NTNs, motivating future extensions to multi-device scenarios and broader network components.

Abstract

The integration of Terrestrial and Non-Terrestrial Networks (TN-NTNs), introduced in 5G, is advancing toward a unified and seamless network of networks in Sixth-Generation (6G). This evolution markedly increases the volume of generated and exchanged data, imposing stringent technical and operational requirements along with higher cost and energy consumption. Consequently, efficient management of data generation and transmission within this unified architecture has become essential. In this article, we investigate semantics-aware information handling in unified TN-NTNs, where data communication between distant TN nodes is enabled via an NTN. We consider an Internet of Things (IoT) monitoring system in which status updates from a remote Energy Harvesting (EH) device are delivered to a destination monitor through a network of Low Earth Orbit (LEO) satellites. We leverage semantic metrics, such as Query Version Age of Information, which collectively capture the timeliness, relevance, and utility of information. This approach minimizes the transmission of stale, uninformative, or unusable information, thereby reducing the volume of data that must be transmitted and processed. The result is a substantial reduction in energy consumption and data exchange within the network-achieving up to 73% lower energy-charging requirements and fewer transmission demands than the state of the art-without compromising the conveyed information.

Figures (6)

• Figure 1: An end-to-end status update system equipped with a semantic agent that considers the generation, transmission, and utilization of information. The semantic metric $\Delta(t)$ quantifies the timeliness, relevance, and utility of information at the destination node.
• Figure 2: Semantic metrics: AoI captures the timeliness of information, while VAoI and QAoI further encompass its relevance and utility. QVAoI integrates all three semantic attributes.
• Figure 3: Status updates via the LEO satellite network: A remote IoT device equipped with a semantic agent transmits update packets to a monitoring node through a Connected Satellite (CS) and relay satellites. The agent schedules transmissions to optimize semantic metrics at the destination.
• Figure 4: QVAoI at the CS (left $y$-axis) and at the destination monitor (right $y$-axis), $N$ hops from the CS, for $20\%$ (solid bars) and $40\%$ (dashed bars) query rates: Incorporating additional semantic attributes yields fresher and more informative network data.
• Figure 5: Transmission regions for the greedy and semantics-aware policies: The semantics-aware policy employs a threshold-based mechanism, triggering updates when the QVAoI exceeds the threshold. The optimal policy raises thresholds at lower energy charging rates, particularly under low battery states. By contrast, the greedy policy disregards QVAoI, transmitting solely when the battery is non-empty.