arXiv subjects

Chuan-Chi Lai

Publications and source records attributed to Chuan-Chi Lai.

At least 19 recordsLinked to original sources

arXiv2026

Joint Energy Efficiency and Fairness Optimization for D2D Communications in Aerial-Ground Integrated Heterogeneous Networks

This study investigates an Aerial-Ground Integrated Heterogeneous network (AGIHN) architecture that combines terrestrial macro base stations and unmanned aerial vehicles (UAVs) serving as aerial base stations to enhance uplink access for macrocell users. To address the complex uplink resource allocation challenge for multiple device-to-device (D2D) communication pairs, we propose a low-complexity Multi-Channel Rate-Fair (MCRF) algorithm. Distinct from traditional exclusive allocation methods, MCRF supports shared reuse, enabling multiple D2D pairs to simultaneously multiplex on the same resource block, thereby significantly improving spectral efficiency. To manage the severe intra-tier interference arising from this non-orthogonal sharing, a heuristic Interference Avoidance (IA) strategy is integrated to ensure the transmission quality of D2D users. The proposed framework jointly optimizes system throughput, user fairness, and energy efficiency without requiring computationally intensive offline training. Simulation results demonstrate distinct performance advantages depending on the reuse mode: Compared to traditional single-channel exclusive reuse schemes, MCRF achieves massive gains, increasing D2D energy efficiency and throughput by approximately 397% and 542%, respectively. Furthermore, relative to multi-channel benchmarks (e.g., MCRR), the proposed algorithm optimizes the efficiency-fairness trade-off, enhancing the fairness index by 7.43% while maintaining a robust fairness score exceeding 0.6 in interference-prone environments.

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arXiv2026

Curriculum-Guided Reinforcement Learning for Energy-Efficient UAV-ISAC in Post-Disaster Search-and-Rescue Operations

Uncrewed aerial vehicles (UAVs) are promising platforms for integrated sensing and communication (ISAC), but their limited onboard energy creates a strong coupling among sensing accuracy, communication quality, and propulsion cost. This paper proposes a curriculum-guided soft actor-critic (CG-SAC) framework with propulsion-aware reward shaping for energy-efficient UAV-ISAC, jointly optimizing the 3D trajectory, communication-sensing power split, and per-user power allocation. A rotary-wing propulsion model is incorporated to derive a closed-form propulsion-economic cruising speed, which is used to construct a propulsion-aware speed-shaping term within a normalized composite reward together with navigation, node-visiting, energy-efficiency, and constraint-penalty terms. A log-linear curriculum progressively tightens the communication, sensing, and proximity requirements during training. Across 2000 randomized scenarios, CG-SAC achieves an average energy efficiency of 0.72 Mbits/J, substantially outperforming the evaluated DRL baselines. Among successfully completed missions, it requires 107.6 steps on average, corresponding to a 66%--82% reduction in flight steps relative to the baselines. Crucially, the learned policy exhibits mission-aware speed adaptation by decelerating near service points and accelerating during transit, while achieving a 99.6% communication-rate satisfaction ratio at service instants. Ablation results further demonstrate the complementary roles of the reward components in balancing mission feasibility and energy efficiency.

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arXiv2026

Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting

This paper addresses multi-objective conflicts and catastrophic forgetting in uncrewed aerial vehicle (UAV) networks across dynamic spatiotemporal environments. Conventional multi-agent reinforcement learning (MARL) algorithms suffer from severe policy degradation during sequential task transitions. We propose a spatiotemporal continual federated learning (SCFL) framework driven by the group-decoupled multi-agent proximal policy optimization (G-MAPPO) algorithm. SCFL incorporates a three-stage geometric alignment mechanism: it resolves local gradient conflicts via group-decoupled policy optimization (GDPO), mitigates spatial Non-Independent and Identically Distributed (non-IID) client drift through adaptive cosine aggregation, and suppresses inter-task interference via global temporal orthogonal projection without raw experience replay. Evaluations show that SCFL achieves superior robustness over federated baselines, maintaining spatial service reliability above 0.95 and a load balancing index of approximately 0.95 during non-stationary transitions. A longitudinal self-degradation analysis further shows that SCFL preserves historical knowledge with near-zero performance variation in spatial reliability and QoS under moderate loads from 40 to 120 users, while revealing its operating boundary under extreme congestion with 140 users due to hard projection constraints. The framework provides a scalable, communication-efficient approach for autonomous aerial network orchestration.

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arXiv2026

Adaptive Probabilistic Skyline Analytics for Mobile Edge-Cloud Systems via State-Aware Deep Reinforcement Learning

The proliferation of Mobile Edge Computing (MEC) and distributed sensing necessitates efficient Probabilistic Skyline (PSKY) query analytics at the network edge. However, this process is severely constrained by the computation-communication trade-off under constrained wireless capacity and dynamic traffic conditions. Furthermore, mobility-induced workload fluctuations and spatio-temporal data shifts render static filtering policies inefficient, often triggering network congestion or compromising query fidelity. To address these systemic inefficiencies in dynamic mobile environments, this paper introduces SA-PSKY, a self-adaptive framework integrating deep reinforcement learning into a distributed query optimization architecture. We formulate the probabilistic skyline filtering as an adaptive control problem and develop a State-Aware Adaptive Weighting (SAAW) mechanism to dynamically regulate computation, communication, and fidelity. By incorporating Prioritized Experience Replay (PER) to stabilize learning around abrupt network transitions, our framework reliably navigates the Pareto-efficient operating point. Empirical evaluations confirm that SA-PSKY achieves substantial latency reduction while preserving query fidelity under constrained communication resources. Furthermore, zero-shot robustness analyses reveal superior adaptation to unseen mobility-induced uncertainty shifts, where rigid methods exhibit severe policy degradation.

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arXiv2026

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories via physical priors. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations confirm that PL-MARL maintains superior coverage and mission continuity under extreme signaling scarcity and node failure. Our results validate proactive inference as a scalable, low-latency solution for robust aerial coordination, effectively minimizing control overhead to preserve spectrum for payload services while ensuring resilience against interference.

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arXiv2026

ORCHID: Fairness-Aware Orchestration in Mission-Critical Air-Ground Integrated Networks

Unmanned Aerial Vehicles (UAVs) provide pivotal on-demand wireless coverage for mission-critical 6G Air-Ground Integrated Networks (AGINs). However, traditional Deep Reinforcement Learning (DRL) orchestration struggles with multi-agent non-stationarity and balancing Energy Efficiency (EE) with service equity. To address these challenges, we propose ORCHID (Orchestration of Resilient Coverage via Hybrid Intelligent Deployment), a stability-enhanced two-stage learning framework. First, ORCHID utilizes Ground Base Station (GBS)-aware topology partitioning to mitigate the exploration cold-start problem. Second, a Reset-and-Finetune (R&F) mechanism within the Multi-Agent Proximal Policy Optimization (MAPPO) architecture enhances learning stability by synchronizing learning-rate decay with optimizer resetting, thereby reducing gradient variance and mitigating policy degradation. Furthermore, by formulating the resource allocation problem as an Egalitarian Bargaining Game (EBG), our theoretical analysis provides new insights into the relationship between fairness and energy efficiency. Specifically, the proposed Max-Min Fairness (MMF) design provides a theoretical explanation for the emergence of a more dispersed and load-balanced UAV topology, while experimental results further demonstrate that this spatial organization improves system energy efficiency compared with conventional Proportional Fairness (PF) schemes. Moreover, ORCHID deliberately sacrifices opportunistic throughput peaks in favor of more stable long-term service performance, resulting in consistently lower performance variance while maintaining a higher minimum service level and substantially improving service fairness. Extensive experimental results demonstrate robust topology adaptation, stable policy convergence, and consistent performance gains over representative state-of-the-art baselines.

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arXiv2026

SCOPE: Deterministic and Training-Free 3D UAV Deployment via Perimeter-based Heuristics

Unmanned Aerial Vehicle (UAV) mounted Base Stations (UAV-BSs) provide flexible coverage for temporary hotspot scenarios; however, efficiently optimizing 3D deployment to satisfy heterogeneous user distributions remains a significant challenge. While Deep Reinforcement Learning (DRL) approaches have shown promise, they often suffer from prohibitive training overhead and poor generalization in cold-start scenarios where the user topology is unknown a priori. To address these limitations, this paper proposes Satisfaction-driven Coverage Optimization via Perimeter Extraction (SCOPE), which is a deterministic and training-free 3D deployment framework. Unlike existing heuristics that rely on fixed-altitude assumptions, SCOPE integrates a perimeter-based peeling strategy with the Welzl Smallest Enclosing Circle (SEC) algorithm to dynamically optimize 3D positions. Theoretically, we provide a rigorous convergence proof and derive a polynomial time complexity of $O(N^2 \log N)$, ensuring predictable execution for real-time applications. Experimentally, we evaluate SCOPE in unpredictable hotspot environments against both traditional heuristics and state-of-the-art DRL baselines under a matched hardware budget. Simulation results demonstrate that SCOPE maintains a high user satisfaction rate between 82% and 88% while generating solutions within millisecond-level latency on commodity hardware. Furthermore, SCOPE demonstrates exceptional resilience by maintaining an approximate 40% functional coverage rate at a minimum altitude constraint of 60 m; in this challenging regime, baseline methods suffer a significant performance degradation, dropping to approximately 20% due to altitude-induced path loss. These findings validate SCOPE as a robust and agile solution for establishing instantaneous digital lifelines in zero-day disaster response missions.

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arXiv2026

Spatio-Temporal Semantic Inference for Resilient 6G HRLLC in the Low-Altitude Economy

The rapid expansion of the Low-Altitude Economy (LAE) necessitates highly reliable coordination among autonomous aerial agents (AAAs). Traditional reactive communication paradigms in 6G networks are increasingly susceptible to stochastic network jitter and intermittent signaling silence, especially within complex urban canyon environments. To address this connectivity gap, this paper introduces the Embodied Proactive Inference for Coordination (EPIC) framework, featuring a Spatio-Temporal Semantic Inference (STSI) operator designed to decouple the coordination loop from physical signaling fluctuations. By projecting stale peer observations into a proactive belief manifold, EPIC maintains a deterministic reaction latency regardless of the network state. Extensive simulations demonstrate that EPIC achieves an average 93.5% reduction in end-to-end reaction latency, masking physical transmission delays of 150 ms with a deterministic 10 ms execution heartbeat. Crucially, EPIC exhibits strategic immunity to escalating network jitter up to 100 ms and improves the Weighted Coverage Efficiency (WCE) by 10.5% during extreme signaling silence lasting up to 50 s. These results provide the deterministic resilience essential for 6G Hyper-Reliable and Low-Latency Communication (HRLLC).

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arXiv2026

Resilient Topology-Aware Coordination for Dynamic 3D UAV Networks under Node Failure

Ensuring continuous service coverage under unexpected hardware failures is a fundamental challenge for 3D Aerial-Ground Integrated Networks. Although Multi-Agent Reinforcement Learning facilitates autonomous coordination, traditional architectures often lack resilience to sudden topology deformations. This paper proposes the Topology-Aware Graph MAPPO (TAG-MAPPO) framework to enhance system survivability through autonomous 3D spatial reconfiguration. Our framework integrates graph-based feature aggregation with a residual ego-state fusion mechanism to capture intricate inter-agent dependencies. To achieve structural robustness, we introduce a Random Observation Shuffling mechanism that fosters strong generalization to agent population fluctuations by breaking coordinate-index dependencies. Extensive simulations across heterogeneous environments, including high-speed mobility at 15 meters per second, demonstrate that TAG-MAPPO significantly outperforms Multi-Layer Perceptron baselines. Specifically, the framework reduces redundant handoffs by up to 50 percent while maintaining superior energy efficiency. Most notably, TAG-MAPPO exhibits exceptional self-healing capabilities, restoring over 90 percent of pre-failure coverage within 15 time steps. In dense urban scenarios, the framework achieves a post-failure fairness index surpassing its original four-UAV configuration by autonomously resolving service overlaps and interference. These findings confirm that topology-aware coordination is essential for resilient 6G aerial networks.

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arXiv2025

Edge-assisted Parallel Uncertain Skyline Processing for Low-latency IoE Analysis

Due to the Internet of Everything (IoE), data generated in our life become larger. As a result, we need more effort to analyze the data and extract valuable information. In the cloud computing environment, all data analysis is done in the cloud, and the client only needs less computing power to handle some simple tasks. However, with the rapid increase in data volume, sending all data to the cloud via the Internet has become more expensive. The required cloud computing resources have also become larger. To solve this problem, edge computing is proposed. Edge is granted with more computation power to process data before sending it to the cloud. Therefore, the data transmitted over the Internet and the computing resources required by the cloud can be effectively reduced. In this work, we proposed an Edge-assisted Parallel Uncertain Skyline (EPUS) algorithm for emerging low-latency IoE analytic applications. We use the concept of skyline candidate set to prune data that are less likely to become the skyline data on the parallel edge computing nodes. With the candidate skyline set, each edge computing node only sends the information required to the server for updating the global skyline, which reduces the amount of data that transfer over the internet. According to the simulation results, the proposed method is better than two comparative methods, which reduces the latency of processing two-dimensional data by more than 50%. For high-dimensional data, the proposed EPUS method also outperforms the other existing methods.

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arXiv2023

Distributed Indexing Schemes for k-Dominant Skyline Analytics on Uncertain Edge-IoT Data

Skyline queries typically search a Pareto-optimal set from a given data set to solve the corresponding multiobjective optimization problem. As the number of criteria increases, the skyline presumes excessive data items, which yield a meaningless result. To address this curse of dimensionality, we proposed a k-dominant skyline in which the number of skyline members was reduced by relaxing the restriction on the number of dimensions, considering the uncertainty of data. Specifically, each data item was associated with a probability of appearance, which represented the probability of becoming a member of the k-dominant skyline. As data items appear continuously in data streams, the corresponding k-dominant skyline may vary with time. Therefore, an effective and rapid mechanism of updating the k-dominant skyline becomes crucial. Herein, we proposed two time-efficient schemes, Middle Indexing (MI) and All Indexing (AI), for k-dominant skyline in distributed edge-computing environments, where irrelevant data items can be effectively excluded from the compute to reduce the processing duration. Furthermore, the proposed schemes were validated with extensive experimental simulations. The experimental results demonstrated that the proposed MI and AI schemes reduced the computation time by approximately 13% and 56%, respectively, compared with the existing method.

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arXiv2023

Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks

In this letter, we study the deployment of Unmanned Aerial Vehicle mounted Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV deployment problem as a user satisfaction maximization problem, that is, maximizing the proportion of served ground users (GUs) that meet a given minimum data rate requirement. We propose an interference-aware deployment (IAD) algorithm for serving arbitrarily distributed outdoor GUs. The proposed algorithm can alleviate the problem of overlapping coverage between adjacent UAV-BSs to minimize inter-cell interference. Therefore, reducing co-channel interference between UAV-BSs will improve user satisfaction and ensure that most GUs can achieve the minimum data rate requirement. Simulation results show that our proposed IAD outperforms comparative methods by more than 10% in user satisfaction in high-density environments.

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arXiv2022

Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks

Unmanned aerial vehicle-aided communication (UAB-BS) is a promising solution to establish rapid wireless connectivity in sudden/temporary crowded events because of its more flexibility and mobility features than conventional ground base station (GBS). Because of these benefits, UAV-BSs can easily be deployed at high altitudes to provide more line of sight (LoS) links than GBS. Therefore, users on the ground can obtain more reliable wireless channels. In practice, the mobile nature of the ground user can create uneven user density at different times and spaces. This phenomenon leads to unbalanced user associations among UAV-BSs and may cause frequent UAV-BS overload. We propose a three-dimensional adaptive and fair deployment approach to solve this problem. The proposed approach can jointly optimize the altitude and transmission power of UAV-BS to offload the traffic from overloaded UAV-BSs. The simulation results show that the network performance improves by 37.71% in total capacity, 37.48% in total energy efficiency and 16.12% in the Jain fairness index compared to the straightforward greedy approach.

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arXiv2021

Frequent-Pattern Based Broadcast Scheduling for Conflict Avoidance in Multi-Channel Data Dissemination Systems

With the popularity of mobile devices, using the traditional client-server model to handle a large number of requests is very challenging. Wireless data broadcasting can be used to provide services to many users at the same time, so reducing the average access time has become a popular research topic. For example, some location-based services (LBS) consider using multiple channels to disseminate information to reduce access time. However, data conflicts may occur when multiple channels are used, where multiple data items associated with the request are broadcast at about the same time. In this article, we consider the channel switching time and identify the data conflict issue in an on-demand multi-channel dissemination system. We model the considered problem as a Data Broadcast with Conflict Avoidance (DBCA) problem and prove it is NP-complete. We hence propose the frequent-pattern based broadcast scheduling (FPBS), which provides a new variant of the frequent pattern tree, FP*-tree, to schedule the requested data. Using FPBS, the system can avoid data conflicts when assigning data items to time slots in the channels. In the simulation, we discussed two modes of FPBS: online and offline. The results show that, compared with the existing heuristic methods, FPBS can shorten the average access time by 30%.

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arXiv2021

Highly Efficient Indexing Scheme for k-Dominant Skyline Processing over Uncertain Data Streams

Skyline is widely used in reality to solve multi-criteria problems, such as environmental monitoring and business decision-making. When a data is not worse than another data on all criteria and is better than another data at least one criterion, the data is said to dominate another data. When a data item is not dominated by any other data item, this data is said to be a member of the skyline. However, as the number of criteria increases, the possibility that a data dominates another data decreases, resulting in too many members of the skyline set. To solve this kind of problem, the concept of the k-dominant skyline was proposed, which reduces the number of skyline members by relaxing the limit. The uncertainty of the data makes each data have a probability of appearing, so each data has the probability of becoming a member of the k-dominant skyline. When a new data item is added, the probability of other data becoming members of the k-dominant skyline may change. How to quickly update the k-dominant skyline for real-time applications is a serious problem. This paper proposes an effective method, Middle Indexing (MI), which filters out a large amount of irrelevant data in the uncertain data stream by sorting data specifically, so as to improve the efficiency of updating the k-dominant skyline. Experiments show that the proposed MI outperforms the existing method by approximately 13% in terms of computation time.

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arXiv2020

Probabilistic Skyline Query Processing over Uncertain Data Streams in Edge Computing Environments

With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. To solve the above problems, edge computing has become a promising solution. In this paper, we propose a new algorithm for processing probabilistic skyline queries over uncertain data streams in an edge computing environment. We use the concept of a second skyline set to filter data that is unlikely to be the result of the skyline. Besides, the edge server only sends the information needed to update the global analysis results on the cloud server, which will greatly reduce the amount of data transmitted over the network. The results show that our proposed method not only reduces the response time by more than 50% compared with the brute force method on two-dimensional data but also maintains the leading processing speed on high-dimensional data.

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arXiv2020

The Coverage Overlapping Problem of Serving Arbitrary Crowds in 3D Drone Cellular Networks

Providing coverage for flash crowds is an important application for drone base stations (DBSs). However, any arbitrary crowd is likely to be distributed at a high density. Under the condition for each DBS to serve the same number of ground users, multiple DBSs may be placed at the same horizontal location but different altitudes and will cause severe co-channel interference, to which we refer as the coverage overlapping problem. To solve this problem, we then proposed the data-driven 3D placement (DDP) and the enhanced DDP (eDDP) algorithms. The proposed DDP and eDDP can effectively find the appropriate number, altitude, location, and coverage of DBSs in the serving area in polynomial time to maximize the system sum rate and guarantee the minimum data rate requirement of the user equipment. The simulation results show that, compared with the balanced k-means approach, the proposed eDDP can increase the system sum rate by 200% and reduce the computation time by 50%. In particular, eDDP can effectively reduce the occurrence of the coverage overlapping problem and then outperform DDP by about 100% in terms of system sum rate.

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arXiv2019

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-positioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.

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