一种基于空地协同的无线电地图共享生成方法

doi:10.7527/S1000-6893.2026.33638

Abstract

Abstract:

To address the limited real-time performance of existing radio map construction methods in measurement-free scenarios， this work proposes an air-ground collaborative method for shared radio map generation. A diffusion model is introduced to formulate a conditional generation task， thereby meeting the requirement for real-time radio map generation among large-scale distributed user nodes. First， an edge computing architecture enabling shared denoising between Unmanned Aerial Vehicles （UAVs） and ground base stations is constructed. The radio map generation task is formulated as a mixed-integer nonlinear programming problem with the objective of minimizing the total generation latency for all users. Subsequently， the original problem is decomposed into two subproblems： the joint optimization of user-server service policies and shared denoising steps， and UAV trajectory planning. These two subproblems are efficiently solved via the branch-and-bound method and the model-based reinforcement learning algorithm RGCN-DRL， respectively. In particular， a prediction module based on relational graph convolutional networks is designed to effectively capture user mobility intentions， which assists in UAV flight decision-making. Simulation results demonstrate that compared with the baseline method of local radio map generation， the proposed method reduces radio map generation latency by 53.08% while guaranteeing the quality of radio map generation.

Key words: radio maps, air-ground cooperation, mobile edge computing, diffusion model, reinforcement learning

CLC Number:

V249

Junyi LIN, Tao WU, Hongjun WANG, Chao ZHAN, Yaqianwen SU. An air-ground collaborative shared generation method for radio maps[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(18): 533638.

Figures/Tables 12

Fig.1

Table 1

Symbol description

变量符号	变量含义
$U : = {1,2, …, U}$	无人机集合
$B : = {1,2, …, B}$	基站集合
$M : = {1,2, …, M}$	用户集合
$N : = U ⋃ B$	边缘服务器集合
$T : = {1,2, …, T}$	时隙集合
$ϑ t u, v t u$	无人机方向、速度
$a m n (t) ∈ {0,1}$	共享去噪策略
$p t u = (x t u, y t u, h)$	无人机的坐标
$p b = (x b, y b, 0)$	基站的坐标
$p t m = (x t m, y t m, 0)$	用户的坐标
$S m a x$	总去噪步骤数
$S m n (t)$	用户请求的共享去噪步骤数
$S t n$	边缘服务器执行的总共享去噪步骤数
$M u p$	同时服务用户上限
$r m n (t)$	下行传输速率
$ψ e m$	提示词嵌入的浮点运算次数
$ψ$	单步去噪的浮点运算次数
$ψ V A E$	VAE解码器的浮点运算次数
$f m$	用户计算能力
$f n$	服务器计算能力
$E t u$	无人机能耗
$T l o c a l m (t)$	本地生成时延
$T s h a r e m (t)$	共享生成时延
$Q m t$	RM生成质量

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 2

Fig.9

Table 3

References 32

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方法	RMSE	构建时延/s
RadioDiff^［13］	0.021 5	32.410
RadioUNet^［8］	0.025 8	0.822
克里金插值^［9］	0.032 9	0.751
射线追踪		120~300

An air-ground collaborative shared generation method for radio maps

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