TY - GEN
T1 - Multitask Semantic-Coded Image Communication for UAV Integrated Sensing and Communication
T2 - 2026 IEEE Conference on Computer Communications, INFOCOM 2026
AU - Mao, Chen
AU - Zhang, Zhongqiang
AU - Zhang, Shuhang
AU - Ma, Shuai
AU - Shi, Guangming
AU - Yang, Zhihua
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - In integrated sensing and communication (ISAC) unmanned aerial vehicle (UAV) networks, the single-to-noise ratio (SNR) and available data rate continuously vary with altitude, distance, blockage, and maneuvering, which heavily challenges conventional image compression and channel coding methods with layered and separate design. In this paper, we propose a multitask semantic-coded image communication (MSCIC) framework for UAV-ISAC with a channel-wise feature enhancement (CFE) mechanism, which could leverage involution and adjustment blocks to emphasize robust informative features under varying channel states. Besides, a transmission-rate adaptive design is developed to dynamically control the amount of transmitted features through a rate adjustment module, together with a corresponding decoding module to support multitask inference. The simulation results show that the proposed framework performs better with adaptive UAV image transmission in varying SNRs compared with typical algorithms.
AB - In integrated sensing and communication (ISAC) unmanned aerial vehicle (UAV) networks, the single-to-noise ratio (SNR) and available data rate continuously vary with altitude, distance, blockage, and maneuvering, which heavily challenges conventional image compression and channel coding methods with layered and separate design. In this paper, we propose a multitask semantic-coded image communication (MSCIC) framework for UAV-ISAC with a channel-wise feature enhancement (CFE) mechanism, which could leverage involution and adjustment blocks to emphasize robust informative features under varying channel states. Besides, a transmission-rate adaptive design is developed to dynamically control the amount of transmitted features through a rate adjustment module, together with a corresponding decoding module to support multitask inference. The simulation results show that the proposed framework performs better with adaptive UAV image transmission in varying SNRs compared with typical algorithms.
KW - ISAC
KW - Semantic communications
KW - UAV communications
KW - channel
UR - https://www.scopus.com/pages/publications/105044513109
U2 - 10.1109/INFOCOM59046.2026.11571237
DO - 10.1109/INFOCOM59046.2026.11571237
M3 - 会议稿件
AN - SCOPUS:105044513109
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2026 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 May 2026 through 21 May 2026
ER -