Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Semantic-Aware Command and Control Transmission for Multi-UAVs

Boya Li, Xiaonan Liu, Dongzhu Liu, Dusit Niyato, Zhu Han

TL;DR

This work addresses the limitation of bit‑oriented UAV C&C networks under URLLC by introducing semantic‑aware downlink transmission. It defines semantic similarity across UAV commands and a QoS trigger to drive multicast opportunities and reduce redundancy, then solves the joint scheduling and RB allocation problem with a PPO policy in a POMDP setting. Key contributions include an end‑to‑end semantic framework for unicast/multicast/idle decisions, a QoS formulation that couples semantic importance with AoI, and a DRL solution that achieves substantial gains in transmission efficiency and effectiveness over traditional bit‑oriented approaches. The results demonstrate that leveraging semantic information can significantly improve resource efficiency and reliability in multi‑UAV C&C scenarios, making it practical for scalable UAV fleets.

Abstract

Uncrewed aerial vehicles (UAVs) have played an important role in the low-altitude economy and have been used in various applications. However, with the increasing number of UAVs and explosive wireless data, the existing bit-oriented communication network has approached the Shannon capacity, which cannot satisfy the quality of service (QoS) with ultra-reliable low-latency communication (URLLC) requirements for command and control (C\&C) transmission in bit-oriented UAV communication networks. To address this issue, we propose a novel semantic-aware C\&C transmission for multi-UAVs under limited wireless resources. Specifically, we leverage semantic similarity to measure the variation in C\&C messages for each UAV over continuous transmission time intervals (TTIs) and capture the correlation of C\&C messages among UAVs, enabling multicast transmission. Based on the semantic similarity and the importance of UAV commands, we design a trigger function to quantify the QoS of UAVs. Then, to maximize the long-term QoS and exploit multicast opportunities of C\&C messages induced by semantic similarity, we develop a proximal policy optimization (PPO) algorithm to jointly determine the transmission mode (unicast/multicast/idle) and the allocation of limited resource blocks (RBs) between a base station (BS) and UAVs. Experimental results show that our proposed semantic-aware framework significantly increases transmission efficiency and improves effectiveness compared with bit-oriented UAV transmission.

Semantic-Aware Command and Control Transmission for Multi-UAVs

TL;DR

This work addresses the limitation of bit‑oriented UAV C&C networks under URLLC by introducing semantic‑aware downlink transmission. It defines semantic similarity across UAV commands and a QoS trigger to drive multicast opportunities and reduce redundancy, then solves the joint scheduling and RB allocation problem with a PPO policy in a POMDP setting. Key contributions include an end‑to‑end semantic framework for unicast/multicast/idle decisions, a QoS formulation that couples semantic importance with AoI, and a DRL solution that achieves substantial gains in transmission efficiency and effectiveness over traditional bit‑oriented approaches. The results demonstrate that leveraging semantic information can significantly improve resource efficiency and reliability in multi‑UAV C&C scenarios, making it practical for scalable UAV fleets.

Abstract

Uncrewed aerial vehicles (UAVs) have played an important role in the low-altitude economy and have been used in various applications. However, with the increasing number of UAVs and explosive wireless data, the existing bit-oriented communication network has approached the Shannon capacity, which cannot satisfy the quality of service (QoS) with ultra-reliable low-latency communication (URLLC) requirements for command and control (C\&C) transmission in bit-oriented UAV communication networks. To address this issue, we propose a novel semantic-aware C\&C transmission for multi-UAVs under limited wireless resources. Specifically, we leverage semantic similarity to measure the variation in C\&C messages for each UAV over continuous transmission time intervals (TTIs) and capture the correlation of C\&C messages among UAVs, enabling multicast transmission. Based on the semantic similarity and the importance of UAV commands, we design a trigger function to quantify the QoS of UAVs. Then, to maximize the long-term QoS and exploit multicast opportunities of C\&C messages induced by semantic similarity, we develop a proximal policy optimization (PPO) algorithm to jointly determine the transmission mode (unicast/multicast/idle) and the allocation of limited resource blocks (RBs) between a base station (BS) and UAVs. Experimental results show that our proposed semantic-aware framework significantly increases transmission efficiency and improves effectiveness compared with bit-oriented UAV transmission.
Paper Structure (12 sections, 11 equations, 4 figures, 1 table)

This paper contains 12 sections, 11 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Communication Model
  4. Semantic Similarity and Transmission mode
  5. Unicast Rate
  6. Multicast Rate
  7. Idle
  8. Quality of Service
  9. Problem Formulation
  10. DRL-Based Multi-UAVs Scheduling and Resource Allocation
  11. Experimental results
  12. Conclusion

Figures (4)

  • Figure 1: Downlink C&C transmission model between BS and UAVs. In each TTI, the transmission mode between the BS and UAVs can be unicast, multicast, or idle.
  • Figure 2: The PPO Framework for Multi-UAVs scheduling. Based on the observations from the environment, the agent outputs the corresponding actions to maximize the long-term QoS.
  • Figure 3: (a) Number of C&C transmissions of both bit-oriented communication and semantic-aware communication with different repeat times. (b) Window-level effectiveness versus various repeat times.
  • Figure 4: (a) Number of C&C transmissions of both bit-oriented communication and semantic-aware communication with different number of UAVs. (b) Window-level effectiveness versus number of UAVs.