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Tiankui Zhang

Publications and source records attributed to Tiankui Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Traffic-Adaptive Per-Hop Multipath Routing in Multi-Hop UAV Networks

In uncrewed aerial vehicle (UAV)-relayed mobile edge computing (MEC) networks, computation tasks generate traffic with diverse latency requirements and data sizes. Routing decisions therefore need to adapt to both traffic characteristics and changing network conditions. Compared with single-path routing, multipath routing is better suited to such heterogeneous traffic because it provides multiple forwarding options and enables flexible traffic splitting. However, conventional multipath routing usually splits traffic over predefined end-to-end paths, making it difficult to respond quickly to link fluctuations and topology changes in UAV networks. To address this issue, we propose a traffic-adaptive per-hop multipath routing method for multi-hop UAV networks, in which each UAV dynamically distributes traffic among multiple candidate next hops. We formulate the routing problem to improve the on-time packet delivery ratio while reducing the packet loss ratio, and model it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we develop a multi-agent reinforcement learning (MARL) algorithm, termed Multi-Agent Proximal Policy Optimization with Dirichlet Modeling (MAPPO-DM). MAPPO-DM follows the centralized-training-and-decentralized-execution framework and models continuous traffic-splitting actions using a Dirichlet distribution. Simulation results show that MAPPO-DM outperforms the baseline methods and maintains robust performance under various network conditions.

cs.NI↗
arXiv2026

Distributed Trajectory Planning and Resource Allocation for Dynamic Multi-UAV Collaborative Computing

This paper investigates a multiple uncrewed aerial vehicles (UAVs)-enabled distributed mobile edge computing (MEC) framework, where the set of collaborative UAVs dynamically varies over time due to their energy states and service loads. The joint optimization of trajectory planning and resource allocation is formulated as a Stackelberg game, where UAVs and mobile terminals (MTs) are modeled as leaders and followers, respectively. UAVs aim to maximize their benefits by balancing executed workload, energy cost, and resource allocation revenue, while MTs seek to minimize their total overhead, composed of computing delay and resource costs, through offloading and resource-request decisions. A hierarchical joint optimization algorithm is developed within a multi-agent deep reinforcement learning (MADRL) framework to coordinate UAVs and MTs in a distributed manner. At the leader level, UAVs jointly determine their trajectories, task migration ratios, MT-UAV association, and unit computing resource pricing. Each UAV is modeled as an agent in a partially observable Markov decision process, and the agents are jointly trained via multi-agent proximal policy optimization (MAPPO) under the centralized-training-and-decentralized-execution paradigm. At the follower level, MTs determine their optimal task offloading ratios and requested computing resources using a two-stage iterative algorithm. Simulation results demonstrate stable convergence under dynamic UAV participation. Compared to the no-collaboration benchmark, the proposed algorithm improves UAV efficiency by 18.58% through inter-UAV task migration and reduces average MT overhead by 33.77% over the fully offloading scheme. It also outperforms other benchmarks under varying network scales and capabilities by jointly optimizing UAV operations and resource utilization.

cs.NI↗
arXiv2026

Rate-Splitting Multiple Access Enabled Probabilistic Semantic Communication in UAV Networks

This article proposes an uncrewed aerial vehicle (UAV) downlink semantic communication framework, where probabilistic knowledge graphs (PKGs) are employed to model user equipment (UE) semantics and decompose semantic information into shared and private components. Leveraging the capability of rate-splitting multiple access (RSMA) in addressing such semantic structures, a PKG-assisted RSMA transmission scheme is developed to efficiently deliver multi-user semantic information under severe energy constraints and fast-varying UAV channels. To characterize the strongly coupled energy costs of communication, computation, and flight, a weighted energy minimization problem is formulated to jointly optimize the UAV trajectory, power allocation, beamforming design, and semantic compression ratio. The resulting non-convex problem is efficiently solved using an iterative semantic-aware weighted energy optimization (SWEO) algorithm that integrates Lagrangian dual decomposition and successive convex approximation. Furthermore, a semantic accuracy metric is proposed to quantify the reliability of reconstruction by assigning importance-based weights to informative KG triples. Extensive simulation results verify that the proposed framework achieves superior energy efficiency, enhanced semantic preservation, and consistently better performance than conventional RSMA, non-orthogonal multiple access (NOMA), and space division multiple access (SDMA) schemes in benchmarks across various network parameters.

eess.SP↗
arXiv2026

Joint Sensing and Covert Communications in RIS-NOMA Systems

A reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system is investigated, where the transmitter (Alice) is a dual functional radar communication (DFRC) base station (BS) that aims to sense the location of a potential warden (Willie), while simultaneously transmitting public and covert signals to the legitimate users, Carol and Bob, respectively. Both cases of known and unknown Willie locations are considered. For the known-location case, assuming perfect channel state information (CSI) at Willie, a covert rate maximization is formulated with the joint optimization of active and passive beamforming, which is solved using successive convex approximation (SCA), penalty method, and semidefinite relaxation (SDR). For the unknown-location case, we propose to estimate Willie's location via radar sensing and develop a sensing-based imperfect CSI model. In particular, the CSI error uncertainty is bounded by the sensing accuracy, which is characterized by the Cramer-Rao bound (CRB). Subsequently, a robust communication rate maximization problem is formulated under the constraints on quality-of-service (QoS) of Carol, sensing accuracy, and covertness level. The Schur complement and S-procedure are employed to handle the non-convex constraints. Numerical results compare the system performance under the two cases, and demonstrate the significant covert performance superiority of the sensing-based imperfect CSI model and NOMA over the general norm-bounded imperfect CSI model and the orthogonal multiple access scheme. Furthermore, the dual yet contradictory effects of sensing on covert communications are revealed. It is also found that Alice primarily utilizes Carol's signal for sensing, while allocating almost all of Bob's signal for communication.

eess.SP↗
arXiv2026

Multipath Routing for Multi-Hop UAV Networks

Multi-hop uncrewed aerial vehicle (UAV) networks are promising to extend the terrestrial network coverage. Existing multi-hop UAV networks employ a single routing path by selecting the next-hop forwarding node in a hop-by-hop manner, which leads to local congestion and increases traffic delays. In this paper, a novel traffic-adaptive multipath routing method is proposed for multi-hop UAV networks, which enables each UAV to dynamically split and forward traffic flows across multiple next-hop neighbors, thus meeting latency requirements of diverse traffic flows in dynamic mobile environments. An on-time packet delivery ratio maximization problem is formulated to determine the traffic splitting ratios at each hop. This sequential decision-making problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this Dec-POMDP, a novel multi-agent deep reinforcement leaning (MADRL) algorithm, termed Independent Proximal Policy Optimization with Dirichlet Modeling (IPPO-DM), is developed. Specifically, the IPPO serves as the core optimization framework, where the Dirichlet distribution is leveraged to parameterize a continuous stochastic policy network on the probability simplex, inherently ensuring feasible traffic splitting ratios. Simulation results demonstrate that IPPO-DM outperforms benchmark schemes in terms of both delivery latency guarantee and packet loss performance.

cs.MA↗
arXiv2025

Dependency-Aware Task Offloading in Multi-UAV Assisted Collaborative Mobile Edge Computing

This paper proposes a novel multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) framework, where the computing tasks of terminal devices (TDs) can be decomposed into serial or parallel sub-tasks and offloaded to collaborative UAVs. We first model the dependencies among all sub-tasks as a directed acyclic graph (DAG) and design a two-timescale frame structure to decouple the sub-task interdependencies for sub-task scheduling. Then, a joint sub-task offloading, computational resource allocation, and UAV trajectories optimization problem is formulated, which aims to minimize the system cost, i.e., the weighted sum of the task completion delay and the system energy consumption. To solve this non-convex mixed-integer nonlinear programming (MINLP) problem, a penalty dual decomposition and successive convex approximation (PDD-SCA) algorithm is developed. Particularly, the original MINLP problem is equivalently transferred into a continuous form relying on PDD theory. By decoupling the resulting problem into three nested subproblems, the SCA method is further combined to recast the non-convex components and obtain desirable solutions. Numerical results demonstrate that: 1) Compared to the benchmark algorithms, the proposed scheme can significantly reduce the system cost, and thus realize an improved trade-off between task latency and energy consumption; 2) The proposed algorithm can achieve an efficient workload balancing for distributed computation across multiple UAVs.

cs.CY↗
arXiv2025

Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multi-user multi-server MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy trade-off cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy trade-off cost and its adaptability for various applications.

cs.NI↗
arXiv2025

Joint Semantic Transmission and Resource Allocation for Intelligent Computation Task Offloading in MEC Systems

Mobile edge computing (MEC) enables the provision of high-reliability and low-latency applications by offering computation and storage resources in close proximity to end-users. Different from traditional computation task offloading in MEC systems, the large data volume and complex task computation of artificial intelligence involved intelligent computation task offloading have increased greatly. To address this challenge, we propose a MEC system for multiple base stations and multiple terminals, which exploits semantic transmission and early exit of inference. Based on this, we investigate a joint semantic transmission and resource allocation problem for maximizing system reward combined with analysis of semantic transmission and intelligent computation process. To solve the formulated problem, we decompose it into communication resource allocation subproblem, semantic transmission subproblem, and computation capacity allocation subproblem. Then, we use 3D matching and convex optimization method to solve subproblems based on the block coordinate descent (BCD) framework. The optimized feasible solutions are derived from an efficient BCD based joint semantic transmission and resource allocation algorithm in MEC systems. Our simulation demonstrates that: 1) The proposed algorithm significantly improves the delay performance for MEC systems compared with benchmarks; 2) The design of transmission mode and early exit of inference greatly increases system reward during offloading; and 3) Our proposed system achieves efficient utilization of resources from the perspective of system reward in the intelligent scenario.

eess.SY↗
arXiv2024

Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement

Accurate classification of laryngeal vascular as benign or malignant is crucial for early detection of laryngeal cancer. However, organizations with limited access to laryngeal vascular images face challenges due to the lack of large and homogeneous public datasets for effective learning. Distinguished from the most familiar works, which directly transfer the ImageNet pre-trained models to the target domain for fine-tuning, this work pioneers exploring two-step heterogeneous transfer learning (THTL) for laryngeal lesion classification with nine deep-learning models, utilizing the diabetic retinopathy color fundus images, semantically non-identical yet vascular images, as the intermediate domain. Attention visualization technique, Layer Class Activate Map (LayerCAM), reveals a novel finding that yet the intermediate and the target domain both reflect vascular structure to a certain extent, the prevalent radial vascular pattern in the intermediate domain prevents learning the features of twisted and tangled vessels that distinguish the malignant class in the target domain, summarizes a vital rule for laryngeal lesion classification using THTL. To address this, we introduce an enhanced fine-tuning strategy in THTL called Step-Wise Fine-Tuning (SWFT) and apply it to the ResNet models. SWFT progressively refines model performance by accumulating fine-tuning layers from back to front, guided by the visualization results of LayerCAM. Comparison with the original THTL approach shows significant improvements. For ResNet18, the accuracy and malignant recall increases by 26.1% and 79.8%, respectively, while for ResNet50, these indicators improve by 20.4% and 62.2%, respectively.

cs.CV↗
arXiv2023

Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for intelligent tasks in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum system performance. We focus on classification intelligence tasks and formulate an optimization problem that considers both the accuracy requirements of tasks and the parallel computing capabilities of MEC systems. To solve the optimization problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation, and compression offloading. We use convex optimization and successive convex approximation to derive closed-form expressions for the subcarrier allocation, offloading decisions, computing capacity, and compressed ratio. Based on our solutions, we design an efficient computing offloading and resource allocation algorithm for intelligent tasks in MEC systems. Our simulation results demonstrate that our proposed algorithm significantly improves the performance of intelligent tasks in MEC systems and achieves a flexible trade-off between system revenue and cost considering intelligent tasks compared with the benchmarks.

cs.NI↗
arXiv2023

Dynamic Multi-time Scale User Admission and Resource Allocation for Semantic Extraction in MEC Systems

This paper investigates the semantic extraction task-oriented dynamic multi-time scale user admission and resourceallocation in mobile edge computing (MEC) systems. Amid prevalence artifi cial intelligence applications in various industries,the offloading of semantic extraction tasks which are mainlycomposed of convolutional neural networks of computer vision isa great challenge for communication bandwidth and computing capacity allocation in MEC systems. Considering the stochasticnature of the semantic extraction tasks, we formulate a stochastic optimization problem by modeling it as the dynamic arrival of tasks in the temporal domain. We jointly optimize the system revenue and cost which are represented as user admission in the long term and resource allocation in the short term respectively. To handle the proposed stochastic optimization problem, we decompose it into short-time-scale subproblems and a long-time-scale subproblem by using the Lyapunov optimization technique. After that, the short-time-scale optimization variables of resource allocation, including user association, bandwidth allocation, and computing capacity allocation are obtained in closed form. The user admission optimization on long-time scales is solved by a heuristic iteration method. Then, the multi-time scale user admission and resource allocation algorithm is proposed for dynamic semantic extraction task computing in MEC systems. Simulation results demonstrate that, compared with the benchmarks, the proposed algorithm improves the performance of user admission and resource allocation efficiently and achieves a flexible trade-off between system revenue and cost at multi-time scales and considering semantic extraction tasks.

cs.NI↗
arXiv2023

Computing Offloading and Semantic Compression for Intelligent Computing Tasks in MEC Systems

This paper investigates the intelligent computing task-oriented computing offloading and semantic compression in mobile edge computing (MEC) systems. With the popularity of intelligent applications in various industries, terminals increasingly need to offload intelligent computing tasks with complex demands to MEC servers for computing, which is a great challenge for bandwidth and computing capacity allocation in MEC systems. Considering the accuracy requirement of intelligent computing tasks, we formulate an optimization problem of computing offloading and semantic compression. We jointly optimize the system utility which are represented as computing accuracy and task delay respectively to acquire the optimized system utility. To solve the proposed optimization problem, we decompose it into computing capacity allocation subproblem and compression offloading subproblem and obtain solutions through convex optimization and successive convex approximation. After that, the offloading decisions, computing capacity and compressed ratio are obtained in closed forms. We design the computing offloading and semantic compression algorithm for intelligent computing tasks in MEC systems then. Simulation results represent that our algorithm converges quickly and acquires better performance and resource utilization efficiency through the trend with total number of users and computing capacity compared with benchmarks.

cs.NI↗
arXiv2023

Hybrid NOMA for STAR-RIS Enhanced Communication

In this paper, a hybrid non-orthogonal multiple access (NOMA) framework for the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced cell-edge communication is investigated. Specifically, one transmitted user and one reflected user are paired as one NOMA-pair, while multiple NOMA-pairs are served via time division multiple access (TDMA). The objective is to maximize the minimum downlink rate by jointly optimizing the user pairing, decoding order, passive beamforming, power and time allocation. A novel two-layer iterative algorithm is proposed to solve the highly coupled problem. Simulation results show that: 1) the proposed framework outperforms the conventional reflecting-only-RIS-based and the OMA-based frameworks; 2) the beamforming design and power allocation dominate the achieved performance; 3) increasing the number of passive elements and shortening the distance between BS and STAR-RIS are two effective ways to further improve the performance.

cs.IT↗
arXiv2023

NOMA for STAR-RIS Assisted UAV Networks

This paper proposes a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) emergency communication network. Multiple STAR-RISs are deployed to provide additional and intelligent transmission links between trapped users and UAV-mounted base station (BS). Each user selects the nearest STAR-RIS for uploading data, and NOMA is employed for users located at the same side of the same STAR-RIS. Considering piratical requirements of post-disaster emergency communications, we formulate a throughput maximization problem subject to constraints on minimum average rate and maximum energy consumption, where the UAV trajectory, STAR-RIS passive beamforming, and time and power allocation are jointly optimized. Furthermore, we propose a Lagrange based reward constrained proximal policy optimization (LRCPPO) algorithm, which provides an adaptive method for solving the long-term optimization problem with cumulative constraints. Specifically, using Lagrange relaxation, the original problem is transformed into an unconstrained problem with a two-layer structure. The inner layer is solved by penalized reward based proximal policy optimization (PPO) algorithm. In the outer layer, Lagrange multipliers are updated by gradient descent. Numerical results show the proposed algorithm can effectively improve network performance while satisfying the constraints well. It also demonstrates the superiority of the proposed STAR-RIS assisted UAV NOMA network architecture over the benchmark schemes employing reflecting-only RISs and orthogonal multiple access.

cs.IT↗
arXiv2023

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking under Malicious Jamming: Joint Power, Path and Target Association Optimization

In this paper, the multi-target tracking (MTT) with an unmanned aerial vehicle (UAV) swarm is investigated in the presence of jammers, where UAVs in the swarm communicate with each other to exchange information of targets during tracking. The communication between UAVs suffers from severe interference, including inter-UAV interference and jamming, thus leading to a deteriorated quality of MTT. To mitigate the interference and achieve MTT, we formulate a interference minimization problem by jointly optimizing UAV's sub-swarm division, trajectory, and power, subject to the constraint of MTT, collision prevention, flying ability, and UAV energy consumption. Due to the multiple coupling of sub-swarm division, trajectory, and power, the proposed optimization problem is NP-hard. To solve this challenging problem, it is decomposed into three subproblems, i.e., target association, path plan, and power control. First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into the multiple sub-swarms and individually matching these sub-swarms to targets. Second, a jamming-sensitive and singular case tolerance (JSSCT)-artificial potential field (APF) algorithm is proposed to plan trajectory for tracking the targets. Third, we develop a jamming-aware mean field game (JA-MFG) power control scheme, where a novel cost function is established considering the total interference. Finally, to minimize the total interference, a dynamic collaboration approach is designed. Simulation results validate that the proposed dynamic collaboration approach reduces average total interference, tracking steps, and target switching times by 28%, 33%, and 48%, respectively, comparing to existing baselines.

eess.SY↗
arXiv2020

Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks

This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA). The delivery of a large volume of multimedia contents for ground users is assisted by a mobile UAV base station, which caches some popular contents for wireless backhaul link traffic offloading. In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance. To cope with the dynamic UAV locations and content requests in practical scenarios, we formulate the long-term caching placement and resource allocation optimization problem for content delivery delay minimization as a Markov decision process (MDP). The UAV acts as an agent to take actions for caching placement and resource allocation, which includes the user scheduling of content requests and the power allocation of NOMA users. In order to tackle the MDP, we propose a Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with \emph{soft ${\varepsilon}$-greedy} strategy to search for the optimal match between actions and states. Since the action-state table size of Q-learning grows with the number of states in the dynamic networks, we propose a function approximation based algorithm with combination of stochastic gradient descent and deep neural networks, which is suitable for large-scale networks. Finally, the numerical results show that the proposed algorithms provide considerable performance compared to benchmark algorithms, and obtain a trade-off between network performance and calculation complexity.

eess.SP↗
arXiv2020

Cache-enabled HetNets with Limited Backhaul: A Stochastic Geometry Model

With the rapid explosion of data volume from mobile networks, edge caching has received significant attentions as an efficient approach to boost content delivery efficiency by bringing contents near users. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul is analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. Under the hybrid caching policy, new analytical results for successful content delivery probability, average successful delivery rate and energy efficiency are derived in the general scenario, the interference-limited scenario and the mean load scenario. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. The performance gain is dramatically improved when the content popularity is less skewed, the cache capacity is sufficient and the helper density is relatively large. Furthermore, it is confirmed that there exists an optimal helper density to maximize the energy efficiency of the cache-enabled HetNets.

cs.IT↗
arXiv2020

Cache-enabling UAV Communications: Network Deployment and Resource Allocation

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.

eess.SP↗