Unmanned aerial vehicles (UAVs) are increasingly considered as a key technology for the evolution of 6 G network, addressing limitations related to the fixed deployment of conventional base stations (BSs). In this paper, we investigate the dynamic optimization of an Area Spectral Efficiency (ASE) for UAV-enabled BSs over a realistic 3D terrain model. The goal is to maximize ASE by Line-of-Sight (LoS) queries approach to estimate the coverage area between UAV-enabled BS with ambient active user equipment (UEs), which are distributed within the 3D terrain model. Then proposed route selection strategy is able to successfully seek a minimum path and balance path efficiency. The results reveal that the proposed algorithm is able to successfully seek a minimum path and optimize the LoS coverage simultaneously, achieving similar performance in terms of ASE compared to the PSO method, highlighting its potential for enhancing future wireless communication networks in dynamic and challenging scenarios.

The widespread adoption of Unmanned Aerial Vehicles (UAVs) or drones has introduced significant security and privacy challenges, particularly concerning unauthorized drone activities near sensitive areas. To address these concerns, we propose a novel drone detection model, WRN-YOLO, which integrates the Wide Residual Network (WRN) architecture with the You Only Look Once (YOLO) object detection framework. This integration enhances feature extraction capabilities, leading to improved detection accuracy. Through comprehensive ablation studies, we have identified the optimal YOLO variant that synergizes with our backbone modifications, ensuring superior performance in diverse scenarios. Recognizing the complexities of real-world environments, we have also developed a synthetic dataset designed to train our WRN-YOLO. This dataset encompasses a variety of challenging conditions, including intricate backgrounds and the presence of confounding elements, to robustly assess the model's efficacy. Experimental results demonstrate that our method significantly outperforms existing models in accurately detecting drones amidst complex scenes, offering a promising solution for real-time UAV threat mitigation. The proposed approach ranked Top 3 in the 8th WOSDETC Drone-vs-Bird Detection Challenge. Our source code and synthetic dataset are publicly available at https://github.com/yjwong1999/IJCNN2025-DvB.

The use of aerial base stations (ABSs) has gained significant interest due to their deployment flexibility for coverage and capacity enhancements. However, the load balancing problem in multi-ABS networks, which is crucial for quality of service (QoS) provisioning and fair ABS utilization, remains largely unresolved. This paper investigates the load balancing problem for multi-ABS networks with consideration of backhaul limitations, while taking into account the delay-QoS performance of the networks. Firstly, we quantify the load of each ABS with the effective capacity (EC), which is characterized by a latency parameter known as the delay-QoS exponent. Then, we design a load balancing utility function based on the generalized α-fairness concept, which provides the flexibility to the network to achieve various degrees of load balance among ABSs. Next, a joint ABS placement and user association problem is formulated with the aim to maximize the load balancing utility function, with consideration of backhaul capacity constraints, physical isolation between ABSs and ABSs’ limited user capacity. Since the problem is a mixed-integer programming problem which is generally difficult to solve optimally, we develop an efficient solution framework, which optimizes the 3D placements of ABSs using swarm intelligence and user association via convex optimization. Simulation results show that our proposed scheme outperforms existing schemes in terms of load balance, probability of blocking, and EC.

Recently, Deep Reinforcement Learning (DRL) has been applied to enhance the Quality of Experience (QoE) of Adaptive Bitrate Streaming (ABR) by adjusting the video quality level in real time based on instantaneous network conditions. To build a state-of-the-art DRL-based ABR (DRLABR) algorithm, it must learn from the clients’ actual network and video streaming behavior. However, collecting such data directly from clients introduces several challenges, including privacy concerns, high bandwidth consumption, and the straggler effect—where poor network conditions of certain clients delay the training process, as DRLABR’s performance is highly dependent on network interactions. To overcome these limitations, we propose a decentralized training approach for DRLABR using a Federated Learning (FL) framework. Instead of gathering raw data, clients train their local DRLABR models independently and send only model updates to the central server. To address the straggler issue, we propose to desynchronize the FL update rules, allowing clients to contribute their model updates at their own pace, regardless of varying network conditions. In addition, we design a DRL-based client selection mechanism to prevent oversampling of high-bandwidth clients, which could lead to model divergence, thereby ensuring balanced participation and improving the overall training efficiency. We validate our approach through a comprehensive simulation encompassing diverse video content and real-world network traces, simulating a wide range of streaming activities. Our results show that the proposed framework significantly outperforms conventional FedAvg and FedAsync methods, achieving the highest average QoE score of 2.02 and reducing the total training latency by 21.26%.

Deep learning has significantly advanced the field of building extraction from remote sensing images, providing robust solutions for identifying and delineating building footprints. However, a major challenge persists in the form of domain adaptation, particularly when addressing cross-city variations. The primary challenge lies in the significant differences in building appearances across cities, influenced by variations in building shapes and environmental characteristics. Consequently, models trained on data from one city often struggle to accurately identify buildings in another city. In this paper, we address this challenge from a data-centric perspective, focusing on diversifying the training set. Our empirical results show that improving data diversity via open-source datasets and diffusion augmentation significantly improved the performance of the segmentation model. Our baseline model, trained with no extra dataset, only achieved a private F1 score of 0.663. On the other hand, our model trained with the additional Las Vegas building footprints extracted from the Microsoft Building Footprint dataset, achieved a high private F1 score of 0.703. Surprisingly, we found that diffusion augmentation helps improve our model score to 0.681 without requiring an extra dataset, which is higher than the baseline model. Finally, we also experimented with the Non-Maximal Suppression (NMS) hyperparameter to improve the model’s performance in segmenting dense and small objects, which gave us a high private F1 score of 0.897. These techniques ultimately led our solution to rank 1st in the competition. Our source code and the pretrained models are publicly available at https://github.com/DoubleY-BEGC2024/OurSolution.

The YOLOv8 model has high detection efficiency and classification accuracy in detecting commutator surface defects, aimed at the problem of low working efficiency of a commutator, caused by commutator surface defects. First, the theoretical framework of Region-based Convolutional Neural Networks (R-CNN), spatial pyramid pooling (SPP)-net, Fast R-CNN, and Faster R-CNN is introduced, and the detection principle and process are described in detail. Secondly, the principle of the YOLOv8 network structure, head structure, neck structure, and C2f module are explained, and the loss function is described. The average precision of the proposed algorithm for detecting cracks and small points is more than 98%, and the frames per second (FPS) is 27. The detection results are mapped to the original image, and the visualization of the commutator surface defect detection is obtained, which has a higher robustness, accuracy, and real-time performance than the R-CNN, SPP-net, Fast R-CNN, and Faster R-CNN algorithms.

Backscatter communications (BC) have recently emerged as a promising technology for sixth-generation (6G) networks with device-to-device (D2D) communications to support energy-efficient Internet of Things communications. This paper investigates the power allocation problem for energy-efficient 6G networks with backscatter-enabled D2D (BC-D2D) communications. To this end, we formulate the power allocation problem as a biobjective optimization problem that jointly maximizes the sum data rate and minimizes the power consumption of the networks, subject to energy-harvesting, reflection coefficient (RC) and power constraints. To solve this problem, suboptimal RCs are first analytically obtained based on the energy-harvesting constraints of the BC-D2D transmitters. Next, the biobjective optimization problem is transformed into a convex, single-objective power allocation optimization problem using the weighted sum approach, which is then solved using convex optimization. Results show that the proposed scheme outperforms the baseline schemes in energy efficiency under scenarios with different numbers of cellular user equipment and BC-D2D pairs.

This paper investigates the physical layer security (PLS) issue in reconfigurable intelligent surface (RIS) aided millimeter-wave rotary-wing unmanned aerial vehicle (UAV) communications under the presence of multiple eavesdroppers and imperfect channel state information (CSI). The goal is to maximize the worst-case secrecy energy efficiency (SEE) of UAV via a joint optimization of flight trajectory, UAV active beamforming and RIS passive beamforming. By interacting with the dynamically changing UAV environment, real-time decision making per time slot is possible via deep reinforcement learning (DRL). To decouple the continuous optimization variables, we introduce a twin- twin-delayed deep deterministic policy gradient (TTD3) to maximize the expected cumulative reward, which is linked to SEE enhancement. Simulation results confirm that the proposed method achieves greater secrecy energy savings than the traditional twin-deep deterministic policy gradient DRL (TDDRL)-based method.

With the deployment of heterogeneous networks, mobile users are expecting ubiquitous connectivity when using applications. For bandwidth-intensive applications such as Internet Protocol Television (IPTV), multimedia contents are typically transmitted using a multicast delivery method due to its bandwidth efficiency. However, not all networks support multicasting. Multicasting alone could lead to service disruption when the users move from a multicast-capable network to a non-multicast network. In this paper, we propose a handover scheme called application layer seamless switching (ALSS) to provide smooth real-time multimedia delivery across unicast and multicast networks. ALSS adopts a soft handover to achieve seamless playback during the handover period. A real-time streaming testbed is implemented to investigate the overall handover performance, especially the overlapping period where both network interfaces are receiving audio and video packets. Both the quality of service (QoS) and objective-mapped quality of experience (QoE) metrics are measured. Experimental results show that the overlapping period takes a minimum of 56 and 4 ms for multicast-to-unicast (M2U) and unicast-to-multicast (U2M) handover, respectively. The measured peak signal-to-noise ratio (PSNR) confirms that the frame-by-frame quality of the streamed video during the handover is at least 33 dB, which is categorized as good based on ITU-T recommendations. The estimated mean opinion score (MOS) in terms of video playback smoothness is also at a satisfactory level.

Multimedia broadcast multicast service (MBMS) with inherently low requirement for network resources has been proposed as a candidate solution for using such resources in a more efficient manner. On the other hand, the Next Generation Mobile Network (NGMN) combines multiple radio access technologies (RATs) to optimize overall network performance. Handover performance is becoming a vital indicator of the quality experience of mobile user equipment (UE). In contrast to the conventional vertical handover issue, the problem we are facing is how to seamlessly transmit broadcast/multicast sessions among heterogeneous networks. In this paper, we propose a new network entity, media independent broadcast multicast service center (MIBM-SC), to provide seamless handover for broadcast/multicast sessions over heterogeneous networks, by extensions and enhancements of MBMS and media independent information service (MIIS) architectures. Additionally, a network selection scheme and a cell transmission mode selection scheme are proposed for selecting the best target network and best transmission mode. Both schemes are based on a load-aware network capacity estimation algorithm. Simulation results show that the proposed approach has the capability to provide MBMS over heterogeneous networks, with improved handover performance in terms of packet loss rate, throughput, handover delay, cell load, bandwidth usage, and the peak signal-to-noise ratio (PSNR).

This paper investigates the impact of multiple BER requirements on resource allocation for orthogonal frequency division multiple access (OFDMA) downlink systems. Max-total capacity (MTC) and proportional fairness (PF) problems are considered. With the goal of maximizing either the instantaneous or long-term network capacity, we propose BER-driven resource allocation (BRA) schemes which exploit both channel state information (CSI) and target BER of each traffic flow. Simulations results show that the average spectral efficiency of the BRA-like schemes is superior to that of the conventional schemes which only consider one BER constraint and thus the chosen BER constraint has to be the strictest one out of multiple BER requirements. The performance gap between these schemes increases when the average signal-to-ratio (SNR) increases. Besides that, the average spectral efficiency increases when the target BER ratio of one user to the other users increases. In other words, our proposed methods enhance wireless resource utilization.

With the emergence of multimode terminals and the deployment of multiple access networks, users are expecting ubiquitous and seamless connectivity across heterogeneous networks when using IP-based applications. For bandwidth intensive IP-based application such as Internet Protocol Television (IPTV), the multimedia contents are usually transmitted via multicast delivery for reason of efficiency. However, not all networks support multicast. Hence, multicast video delivery could lead to service disruption when users move from a network with multicast support to another network that is not. In this paper, we propose a system called Application Layer Switching System (ALSS) that focuses on switching video transmission between multicast and unicast delivery methods. More specifically, we study the overlapping period needed by the mobile terminal to achieve smooth video playback for different videos during connection handover.