Optimization of Short Packet Communication Resources for UAV Assisted Power Inspection
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摘要: 在无人机辅助电力巡检场景中，为保障电网安全运行，无人机需实时采集并回传电网关键状态参数和图像、视频等多模态数据，控制中心基于此对电网进行调度与调控。无人机巡检任务中的数据采集与回传具有超可靠低时延和实时大带宽等通信需求。然而，无线通信资源的稀缺性和无人机能量约束使得上述异构需求难满足，进而导致巡检数据的时效性和巡检任务的有效性难保障。针对上述挑战，本文提出了数据传输调度与通信资源分配的协同优化算法，在任务性能与约束下，降低系统开销，并基于非正交多址接入技术设计长短包混合帧结构，满足异构通信需求。在无人机数据传输调度方面，将调度决策建模为马尔可夫决策过程，并将通信消耗纳入决策成本。在通信资源优化方面，联合优化长短包功率配置、短包包长和导频长度，进而在保障长包传输需求的前提下，提升短包传输的可靠性，满足异构通信需求，实现低开销的无人机电力巡检策略。仿真结果表明，该方法能够在保障传输可靠性的同时，显著降低通信成本，为无人机辅助电力巡检场景中的异构数据传输提供有效支撑。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.. -
Key words:
- Short-Packet Communication /
- Non-Orthogonal Multiple Access /
- Data Timeliness /
- MDP
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表 1 无人机数据传输调度算法表
算法：基于值迭代的数据传输调度算法 输入：状态$ {s_k} $，动作$ {a_k} $，状态转移概率$ P\left\langle {{{s_k}}} \mathrel{\left | {\vphantom {{{s_k}} {{s_{k - 1}},{a_{k - 1}}}}} \right. } {{{s_{k - 1}},{a_{k - 1}}}} \right\rangle $，AoI阈值$ {\Delta _{{\text{thr}}}} $，语义匹配度阈值$ \zeta $，通信成本${l_k}$，折扣因子$\upsilon \in [0,1)$，收敛
阈值$\varsigma $；输出：调度矩阵$\Pi $； 1：初始化值函数${V^0}({s_k}) = 1$，${V^1}({s_k}) = 0$，迭代轮次$i = 1$； 2://值迭代主循环 3：repeat 4： 初始化：$i = i + 1$，${V^i}({s_k}) = {V^{i + 1}}({s_k})$，$\delta = 0$ 5： for每个时隙状态${s_k}$ do 6： 计算动作价值函数：$ Q({s_k},{a_k}) = {l_k} + \upsilon \cdot \sum\limits_{{s_k}} {P({s_k}|{s_{k - 1}},{a_k})V({s_k})} \} $ 7： 更新值函数：$V({s_k}) = \min \{ Q({s_k},0),Q({s_k},1)\} $ 8： $\delta = \max \{ \delta ,\left| {{V^i}({s_k}) - {V^{i - 1}}({s_k})} \right|$ 9： end for 10：Until $\delta $<$\varsigma $ 11：//策略提取阶段 12：for 每个时隙状态${s_k}$ do 13： 得出最优策略$ \pi *({s_k}) = \mathop {\arg \min }\limits_{{a_k} \in \{ 0,1\} } \{ {l_k} + \upsilon \cdot \sum\limits_{{s_k}} {P\left\langle {{{s_k}}} \mathrel{\left | {\vphantom {{{s_k}} {{s_{k - 1}},0}}} \right. } {{{s_{k - 1}},0}} \right\rangle V({s_k})} \} $ 14：end for 15：return $\pi *$ 
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表 2 基于NOMA的混合帧结构传输优化算法
算法：联合优化短包关联矩阵、导频长度、短包长度以及长短包
功率输入：长包信噪比门限${\gamma _{{\text{th}}}}$，总功率限制${P_{\max }}$，短包信息比特数
$L$，最长帧长${N_{\max }}$，算法收敛阈值$\theta $。1：短包关联矩阵${\Phi _1}$，功率分配${P_1}^S,{P_1}^L$，短包长度${N_{{s_1}}}$，导
频长度${N_{{p_1}}}$，迭代轮次$r = 1$。2：重复步骤(3)到(6)直到满足$\left| {{\eta _r} - {\eta _{r - 1}}} \right| < \theta $。 3：已知${N_{{p_{r - 1}}}},{N_{{S_{r - 1}}}},P_{r - 1}^S,P_{r - 1}^L$求解优化问题${\text{P2}}{\text{.1}}$，得到
${\Phi _r}$。4：已知${\Phi _r},P_r^S,P_r^L$，求解优化问题${\text{P2}}{\text{.2}}$，得到${N_{{p_r}}},{N_{{S_r}}}$。 5：已知${\Phi _r},{N_{{p_r}}},{N_{{S_r}}}$求解优化问题${\text{P2}}{\text{.3}}$，得到$P_r^S,P_r^L$。 6：得到${\eta _r}$，更新迭代次数$r = r + 1$ 7：结束并输出。 输出：短包关联矩阵$\Phi $，功率分配${P^S},{P^L}$，短包长度${N_s}$，导频
长度${N_p}$。
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表 3 仿真参数设置表
参数 数值 混合帧结构最长帧长${N_{\max }}$ 800 bits 每帧结构携带短包数$M$ 10 长包信噪比门限${\gamma _{{\text{th}}}}$ [5, 13] dB 短包信息比特数$L$ [150, 300] bit 传输总功率限制${P_{\max }}$ 1 W 总带宽$B$ 1 MHz 噪声功率谱密度${N_0}$ –170 dBm/Hz 状态转移概率$P$ 0.4 两种环境条件下的频移阈值$ {\zeta _0} $、$ {\zeta _1} $ 0.1 Hz, 0.01 Hz 折扣因子$\upsilon $ 0.95
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