Optimization of Short Packet Communication Resources for UAV Assisted Power Inspection

CHU Hang; DONG Zhihao; CAO Jie; SHI Huaifeng; ZENG Haiyong; ZHU Xu

doi:10.11999/JEIT250852

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CHU Hang, DONG Zhihao, CAO Jie, SHI Huaifeng, ZENG Haiyong, ZHU Xu. Optimization of Short Packet Communication Resources for UAV Assisted Power Inspection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250852

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CHU Hang, DONG Zhihao, CAO Jie, SHI Huaifeng, ZENG Haiyong, ZHU Xu. Optimization of Short Packet Communication Resources for UAV Assisted Power Inspection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250852

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Optimization of Short Packet Communication Resources for UAV Assisted Power Inspection

doi: 10.11999/JEIT250852 cstr: 32379.14.JEIT250852

1.
School of Information Science and Technology, Harbin Institute of Technology, Shenzhen Shenzhen 518055, China
2.
School of Electronics and Information Engineering‌, Nanjing University of Information Science and Technology Nanjing 210044, China
3.
School of Electronics and Information Engineering, Guangxi Normal University, Guilin 541001, China

Received Date: 2025-08-31
Accepted Date: 2025-11-05
Rev Recd Date: 2025-09-25

Available Online: 2025-11-15

Abstract

Abstract

Objective In Unmanned Aerial Vehicles(UAV)-assisted power grid inspection, real-time collection and transmission of multi-modal data (key parameters, images, and videos) are critical for secure grid operation. These tasks present heterogeneous communication demands, including ultra-reliable low-latency and real-time high-bandwidth. However, the scarcity of wireless communication resources and UAV energy constraints make these demands difficult to meet, which in turn compromises data timeliness and overall task effectiveness. To address these challenges, this article aims to develop a collaborative optimization framework for data transmission scheduling and communication resource allocation, thereby minimizing system overhead while strictly satisfying task performance and reliability requirements. Methods To address the challenges mentioned above, this article constructs a collaborative optimization framework for data transmission scheduling and communication resource allocation. In terms of data transmission scheduling, it is modeled as a Markov Decision Process (MDP), incorporating communication consumption into the decision cost. At the resource allocation level, Non-Orthogonal Multiple Access (NOMA) technology is introduced to improve spectral efficiency. This method can significantly reduce communication costs while ensuring transmission reliability, providing effective support for heterogeneous data transmission in UAV-assisted power inspection scenarios. Results and Discussions To verify the effectiveness of the proposed framework, comprehensive simulations were conducted. A scenario was established where the task of the drone is to collect data from multiple distributed power towers within a designated area. There is a trade-off between reliability and speed (Fig. 3). At the same transmission rate, the bit error rate can be reduced by about an order of magnitude. When the minimum long-packet signal-to-noise ratio threshold of 7 dB is adopted in the simulation, the optimized transmission system can reduce the bit error rate from the 10^–3 level to the 10^–5 level while sacrificing only about 0.4 Mbps of transmission rate. After algorithm optimization, a lower effective signal-to-noise ratio is required at the same bit error rate; under the same signal-to-noise ratio, the short-packet error rate is better, which means that the system performance is more stable and the transmission efficiency is higher (Fig. 4). Conclusions This paper proposes a novel collaborative optimization framework that effectively addresses the challenges of limited resources and heterogeneous demands in UAV power inspection. By establishing a coordinated framework that deeply integrates MDP-based adaptive scheduling with NOMA-based joint resource allocation, it successfully balances the trade-off between communication performance and system overhead. This work provides a valuable theoretical and practical foundation for achieving efficient, low-cost, and reliable data transmission in future intelligent autonomous aerial systems..
- Short-Packet Communication,
- Non-Orthogonal Multiple Access,
- Data Timeliness,
- MDP

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References(15)

References

[1]	CAO Lei and WANG Huai. Research on UAV network communication application based on 5G technology[C]. Proceedings of 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), Zhuhai, China, 2022: 125–129. doi: 10.1109/IWECAI55315.2022.00033.
[2]	DIAO Xianbang, CAI Yueming, YU Baoquan, et al. Location and complex status update strategy optimization in UAV-assisted IoT[J]. IEEE Internet of Things Journal, 2023, 10(13): 11588–11604. doi: 10.1109/JIOT.2023.3244541.
[3]	KAUL S, YATES R, and GRUTESER M. Real-time status: How often should one update?[C]. Proceedings of 2012 IEEE INFOCOM, Orlando, USA, 2012: 2731–2735. doi: 10.1109/INFCOM.2012.6195689.
[4]	ZHANG X, CHANG Z, HÄMÄLÄINEN T, et al. AoI-energy tradeoff for data collection in UAV-assisted wireless networks[J]. IEEE Transactions on Communications, 2024, 72(3): 1849–1861. doi: 10.1109/TCOMM.2023.3337400.
[5]	HUANG Xiong and FU Xiuwen. Fresh data collection for UAV-assisted IoT based on aerial collaborative relay[J]. IEEE Sensors Journal, 2023, 23(8): 8810–8825. doi: 10.1109/JSEN.2023.3253920.
[6]	NG B K, LAM C T, and LAW E K L. Blocklength minimization in NOMA systems with hybrid long and short packets[C]. Proceedings of 2021 IEEE Latin-American Conference on Communications (LATINCOM), Santo Domingo, Dominican Republic, 2021: 1–5. doi: 10.1109/LATINCOM53176.2021.9647829.
[7]	ZENG Haiyong, ZHANG Rui, ZHU Xu, et al. Frame structure and resource optimization for hybrid NOMA-based data collection in IIoT with imperfect SIC[J]. IEEE Internet of Things Journal, 2024, 11(23): 37799–37812. doi: 10.1109/JIOT.2024.3444462.
[8]	VILNI S S, MOLTAFET M, LEINONEN M, et al. Multi-source AoI-constrained resource minimization under HARQ: Heterogeneous sampling processes[J]. IEEE Transactions on Vehicular Technology, 2024, 73(1): 1084–1099. doi: 10.1109/TVT.2023.3310190.
[9]	CHEN He, GU Yifan, and LIEW S C. Age-of-information dependent random access for massive IoT networks[C]. Proceedings of the IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, Toronto, Canada, 2020: 930–935. doi: 10.1109/INFOCOMWKSHPS50562.2020.9162973.
[10]	QIN Meng, CHENG Nan, JING Zewei, et al. Service-oriented energy-latency tradeoff for IoT task partial offloading in MEC-enhanced multi-RAT networks[J]. IEEE Internet of Things Journal, 2021, 8(3): 1896–1907. doi: 10.1109/JIOT.2020.3015970.
[11]	WANG Kunlun, NIYATO D, CHEN Wen, et al. Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(7): 2000–2012. doi: 10.1109/JSAC.2023.3280965.
[12]	POLYANSKIY Y, POOR H V, and VERDU S. Channel coding rate in the finite blocklength regime[J]. IEEE Transactions on Information Theory, 2010, 56(5): 2307–2359. doi: 10.1109/TIT.2010.2043769.
[13]	JINDAL N and LOZANO A. A unified treatment of optimum pilot overhead in multipath fading channels[J]. IEEE Transactions on Communications, 2010, 58(10): 2939–2948. doi: 10.1109/TCOMM.2010.083110.090696.
[14]	ADEMAJ F, TARANETZ M, and RUPP M. 3GPP 3D MIMO channel model: A holistic implementation guideline for open source simulation tools[J]. EURASIP Journal on Wireless Communications and Networking, 2016, 2016(1): 55. doi: 10.1186/s13638-016-0549-9.
[15]	LAI Xiazhi, ZHANG Qi, and QIN Jiayin. Downlink NOMA networks with hybrid long-packet and short-packet communications in flat Rayleigh fading channels[J]. IEEE Systems Journal, 2020, 14(3): 3410–3413. doi: 10.1109/JSYST.2019.2931843.