Jul 2026· Annals of Telecommunications· 0 citations· 6 references
TL;DR
The Enhanced RDAP (e-RDAP), a multi-criteria association policy that combines RSSI, Packet Delivery Ratio (PDR), and communication delay with an adaptive deployment strategy is introduced, indicating that QoS-aware multi-criteria association provides additional gains beyond load-aware association alone.
Abstract
Unmanned Aerial Vehicles (UAVs) have become a flexible alternative for providing temporary communication support in scenarios where fixed infrastructure may be overloaded or unavailable. This paper investigates UAV-based auxiliary networks for large-scale events, where sudden user density spikes can degrade the performance of conventional cellular systems. A key challenge in this context is maintaining stable UAV-user associations under high user density and limited UAV resources. Building on our previous Received Signal Strength Indicator-driven Association Policy (RDAP), this work introduces the Enhanced RDAP (e-RDAP), a multi-criteria association policy that combines RSSI, Packet Delivery Ratio (PDR), and communication delay with an adaptive deployment strategy. The proposed policy is evaluated using Network Simulator 3 (NS-3) under static and dynamic mobility scenarios, including realistic human mobility patterns. Results show that e-RDAP improves the reliability-efficiency trade-off compared with RDAP. In the highest-density dynamic scenario, e-RDAP achieves a
$$3.8\%$$
3.8
%
improvement in PDR and an
$$11.8\%$$
11.8
%
reduction in latency compared with RDAP. It also reduces reassociations by
$$17.3\%$$
17.3
%
and controls overhead by
$$27\%$$
27
%
compared with RDAP. Compared with the UAV-specific MULB-LA benchmark, e-RDAP also provides higher PDR, lower delay, fewer reassociations, and lower signaling overhead, indicating that QoS-aware multi-criteria association provides additional gains beyond load-aware association alone.
Unmanned Aerial Vehicle (UAV) swarm networks play a vital role as real-time communication relays. It provides communication services in applications such as disaster management, intelligent transport, and environmental monitoring. The inherent characteristics of Unmanned Swarm Networks (USNs), such as flexible deployment and high operational adaptability, make them valuable in environments with limited infrastructure. However, the highly dynamic and decentralized nature of USNs presents major challenges in designing efficient and reliable routing protocols. Traditional approaches often struggle with frequent topology changes and high mobility of USNs. To address these challenges, this study proposes an intelligent Q-learning-enhanced Evolutionary Game Theory (QEGT) routing mechanism for USNs. The proposed scheme leverages game-theoretic incentives and Q-learning to adaptively select strategies. The optimal next-hop UAV is selected based on residual energy, queue length, and proximity to the destination. Extensive simulations in NS-3 demonstrate the effectiveness of the proposed QEGT scheme. Simulation results show that the proposed QEGT scheme outperforms the state-of-the-art approaches in terms of network survival time, the number of successfully delivered packets, average hop count, and average delay.
Anita Murmu, Saurabh Kumar Srivastava, Nuthan Chingeetham et al.· IEEE Open Journal of the Com...· 0 citations
Unmanned Aerial Vehicle (UAV) networks are increasingly deployed in dynamic environments where reliable and low‐latency communication is critical. However, high mobility, intermittent connectivity, spectrum limitations, and energy constraints make conventional static communication protocols inadequate for maintaining stable, dependable links. To address these challenges, this paper proposes TRC‐MAPPO, a topology‐aware reliability‐constrained multi‐agent deep reinforcement learning framework for adaptive UAV swarm communication. The routing problem is formulated as a constrained decision‐making task that jointly considers packet delivery reliability, end‐to‐end delay, link stability, bandwidth usage, and energy consumption. Unlike single‐agent DRL baselines, TRC‐MAPPO represents the swarm as a dynamic communication graph and combines local relay selection with centralized training, enabling cooperative routing decisions under time‐varying network conditions. Simulations are conducted in a controlled, dynamic UAV environment, with the same mobility and traffic settings used for all methods. The proposed framework is compared with DQN and PPO over different swarm densities. Results show that TRC‐MAPPO achieves a higher packet delivery ratio, lower end‐to‐end delay, and more efficient energy behaviour, particularly in dense deployments. These findings indicate that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks.
V. Nam, A. Chehri, Weiwei Jiang et al.· Expert systems· 0 citations
Reliable communication for unmanned aerial vehicles (UAVs) in beyond-5G and 6G cellular networks is challenged by strong line-of-sight interference, limited spatial separability, and inefficient association when UAVs operate along structured aerial highways (AHs). This paper presents a joint UAV association and beam management framework for heterogeneous terrestrial–aerial networks assisted by high-altitude platform stations (HAPS). The approach exploits the spatial structure of AHs through particle swarm optimization (PSO)-based segmentation, enabling consistent segment-level UAV association across network tiers. A tier-aware association metric is combined with an enhanced teaching–learning-based optimization (ETLBO) scheme to jointly optimize synchronization signal block (SSB) beam activation and power allocation. This coordinated design mitigates inter-sector interference and improves beam alignment for aerial users while preserving the performance of ground users. Simulation results based on 3GPP-compliant models demonstrate notable gains in UAV SINR and achievable data rate, particularly at the lower percentiles, with limited impact on terrestrial users. The framework further exhibits robust performance across different AH geometries, confirming its effectiveness in interference-and coverage-limited aerial communication scenarios.
Maadoud Djihane, Hamza Abdelkrim, Houari Keltoum et al.· 2026 International Conferenc...· 0 citations
The increasing frequency of social and emergency situations in modern cities has exposed the limitations of traditional cellular networks, which are often designed based on average traffic demands. These networks struggle to handle sudden demand peaks, leading to service blockages and degraded quality of service. To address this issue, the use of Unmanned Aerial Vehicles (UAV) as mobile base stations has been proposed as a temporary solution to expand network capacity during high-demand periods. However, existing traffic models, such as Erlang-B, fail to capture the dynamic entry, exit, and variability of dwelling times associated with UAVs, limiting their accuracy in real-world scenarios. To overcome these challenges, this work proposes the Erlang-U model, which extends classical traffic analysis by incorporating Markov chains and combining Erlang and Hyperexponential distributions to accurately model the heterogeneous and dynamic nature of UAV sojourn times. This novel approach enables both analytical and computational modeling of UAV mobility and dynamic availability, providing a more realistic estimation of blocking probabilities in cellular networks. Simulation results demonstrate that the adaptive deployment of UAVs, guided by the proposed model, can reduce blocking probability by over 25% compared to conventional solutions. These findings highlight the importance of selecting appropriate sojourn time models to optimize network resilience and efficiency in dynamic and high-demand environments.
Edgar Hernan Rosas Espinosa, M. E. R. Ángeles, R. M. Méndez· Telecom· 0 citations
Smart infrastructures increasingly rely on mobile IoT applications, such as fleet tracking and asset monitoring, which require robust long-range connectivity. Although LoRaWAN is a prominent technology in this context, its standard Adaptive Data Rate mechanism suffers significant performance degradation in dynamic scenarios due to delayed and outdated channel estimation. To address this limitation, this work proposes LoRa-LQE, a link quality estimation scheme specifically designed for mobile environments. By jointly leveraging SNR history and channel stability indicators, the proposed heuristic optimizes the allocation of Spreading Factor and Transmission Power on a per-device basis. Extensive simulations conducted in NS-3 evaluate the approach under varying node densities and mobility profiles. Across the evaluated scenarios, the results show that LoRa-LQE outperforms state-of-the-art solutions, achieving packet delivery ratio improvements of up to 12.1% and energy efficiency improvements of up to 46.6% compared to Mobile ADR, the second-best mobility-aware scheme considered, thereby enabling more reliable communication for mobile IoT assets.
G. S. Neto, T. S. D. Silva, Instituto Federal do Maranhão et al.· Journal of Internet Services...· 0 citations
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations