Time-Frequency Resource Optimization Method for UAV-Assisted IAB Networks in Complex Building Emergency Scenarios

NIE Tong; WANG Bowen; CHEN Ruirui; ZHANG Guowei; JI Renxin

doi:10.11999/JEIT260339

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NIE Tong, WANG Bowen, CHEN Ruirui, ZHANG Guowei, JI Renxin. Time-Frequency Resource Optimization Method for UAV-Assisted IAB Networks in Complex Building Emergency Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260339

Citation:

NIE Tong, WANG Bowen, CHEN Ruirui, ZHANG Guowei, JI Renxin. Time-Frequency Resource Optimization Method for UAV-Assisted IAB Networks in Complex Building Emergency Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260339

Citation:

NIE Tong, WANG Bowen, CHEN Ruirui, ZHANG Guowei, JI Renxin. Time-Frequency Resource Optimization Method for UAV-Assisted IAB Networks in Complex Building Emergency Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260339

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Time-Frequency Resource Optimization Method for UAV-Assisted IAB Networks in Complex Building Emergency Scenarios

doi: 10.11999/JEIT260339 cstr: 32379.14.JEIT260339

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
IoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou 221116, China
3.
Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518000, China
4.
China Fire and Rescue Institute, Beijing 102202, China

Funds: The National Natural Science Foundation of China (62571530), Jiangsu Young Elite Scientists Sponsorship Program (JSTJ-2025-053), Shenzhen Science and Technology Program (KCXFZ20240903094204007)

Received Date: 2026-03-25
Accepted Date: 2026-10-10
Rev Recd Date: 2026-10-09

Available Online: 2026-10-19

Abstract

Abstract

Objective Disasters in complex, high-rise, and ultra-high-rise buildings occur frequently in China, and their incidence has continued to rise. This trend creates an urgent need for emergency rescue command systems adapted to these environments. The complex structures of these buildings can severely attenuate wireless signals, creating communication blind spots at indoor-outdoor transitions and floor boundaries. To address these coverage limitations, the deployment of “dual-use” emergency communication networks designed for both routine operations and disaster response has become an important strategy for improving urban safety and resilience. Unmanned Aerial Vehicles (UAVs), with their lightweight design, high payload capacity, and mobility, provide a reliable platform for deploying these networks. However, UAV-assisted networks in complex building emergency scenarios remain constrained by limited network coverage and backhaul link capacity. Integrated Access and Backhaul (IAB) technology offers a potential architectural solution by supporting wireless access and backhaul over shared spectrum resources. Nevertheless, deploying UAV-assisted IAB networks in emergency scenarios remains challenging. Concurrent user access creates localized traffic surges, while partial infrastructure damage further intensifies network interference. Dense node deployment, high UAV mobility, and complex channel conditions also cause interference relationships to vary over time and become non-stationary. Existing resource allocation methods generally assume stationary environments. Therefore, their allocation decisions may become mismatched with the evolving network topology and interference structure. This study therefore proposes a joint time-frequency resource optimization method to reduce network conflicts under time-varying and non-stationary interference conditions. Methods A segmented composite channel model is established to characterize electromagnetic propagation in complex buildings affected by disasters. The model accounts for line-of-sight links between IAB nodes within indoor or outdoor environments, as well as penetration links between indoor and outdoor nodes. To capture abrupt changes in channel conditions caused by complex environmental factors, a Rician small-scale fading model is introduced. The Rician factor is assumed to follow a piecewise-constant probability distribution, with abrupt changes at selected system frames and constant values between change points. Graph theory is then used to represent the time-varying network topology and interference relationships as a non-stationary directed conflict graph. Communication links are modeled as vertices, and interference relationships are represented as edges. Two types of constraints are modeled: link-scheduling constraints and resource mutual-exclusion constraints. The joint time-frequency resource allocation problem is subsequently formulated as a non-stationary directed dual-graph coloring problem. A minimum-conflict optimization model with soft constraints is developed, in which both constraint types are incorporated as penalty terms in the objective function. An Exponential Forgetting Upper Confidence Bound (EF-UCB) algorithm is proposed within a multi-player Multi-Armed Bandit (MAB) framework to solve this problem. Each communication link is treated as an independent player, and each joint time-frequency resource combination is treated as an available action. An exponential forgetting mechanism updates historical action counts and accumulated rewards. By assigning exponentially decreasing weights to past observations through a forgetting factor, the algorithm reduces the influence of outdated information and supports adaptive, distributed online decision-making under non-stationary interference conditions. Results and Discussions The proposed EF-UCB algorithm is evaluated through Monte Carlo simulations in a three-dimensional emergency scenario involving complex buildings and compared with the Greedy algorithm, the Color Diffusion Algorithm (CDA), Q-Learning (QL), and an ε-greedy-based MAB algorithm. Simulation results show that increasing the number of time slots reduces all three evaluated metrics: the link-scheduling constraint conflict rate, the resource mutual-exclusion constraint conflict rate, and the average total network conflict rate (Fig. 3). As the number of subchannels increases, the resource mutual-exclusion constraint conflict rate and the average total network conflict rate also decrease, whereas the link-scheduling constraint conflict rate changes relatively little (Fig. 4). Across the tested resource configurations, EF-UCB achieves the lowest average total network conflict rate among the compared algorithms (Figs. 3 and 4). In terms of computational efficiency, EF-UCB completes online decisions at the millisecond scale and has a shorter average decision time than the Greedy and CDA algorithms (Figs. 3 and 4). As the number of UAVs increases, the average decision time of EF-UCB remains relatively low and rises only slightly, indicating good scalability for dense emergency deployments (Fig. 6). Compared with the other learning-based methods, EF-UCB reduces the average total network conflict rate more rapidly as the number of system frames increases (Fig. 5). When the number of environmental change points increases, EF-UCB exhibits the smallest increase in the average total network conflict rate among the compared algorithms (Fig. 7). The exponential forgetting mechanism downweights outdated observations, enabling EF-UCB to adapt more effectively to increasingly non-stationary interference conditions (Fig. 7). Conclusions This study addresses the joint time-frequency resource optimization problem for UAV-assisted IAB networks subject to time-varying and non-stationary interference in complex building emergency scenarios. By integrating a segmented composite channel model with a non-stationary directed dual-graph coloring framework, the proposed method represents the propagation characteristics and dynamic interference relationships of these environments within a unified resource allocation framework. The EF-UCB algorithm uses exponential forgetting to reduce the influence of outdated observations and adapt resource allocation to changing interference conditions. Simulation results demonstrate that the proposed method reduces the average total network conflict rate while maintaining relatively low computational complexity and short average decision times. Its polynomial per-frame time complexity further supports online decisions within a single system frame. These results indicate that the proposed method can support timely resource allocation in UAV-assisted emergency communication networks operating under non-stationary interference conditions.
- Complex building,
- Emergency communication,
- Integrated Access and Backhaul (IAB),
- Non-stationary environment,
- Resource scheduling

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References(23)

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