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Joint User Association,Resource Allocation and Trajectory Design for Multi-UAV-Aided NOMA Wireless Communication systems

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China Communications 2025年第3期22卷 217-233页

作者： Yin Sixing Qu Zhaowei Yu Peng Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper,we investigate a multi-UAV aided NOMA communication system,where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication,and each UAV serves its associated users on its own *** aim at maximizing the overall common throughput in a finite time *** a problem is a typical mixed integer nonlinear problem,which involves both continuous-variable and combinatorial *** efficiently solve this problem,we propose a two-layer algorithm,which separately tackles continuous-variable and combinatorial ***,in the inner layer given one user association scheme,subproblems of bandwidth allocation,power allocation and trajectory design are solved based on alternating *** the outer layer,a small number of candidate user association schemes are generated from an initial scheme and the best solution can be determined by comparing all the candidate *** particular,a clustering algorithm based on K-means is applied to produce all candidate user association schemes,the successive convex optimization technique is adopted in the power allocation subproblem and a logistic function approximation approach is employed in the trajectory design *** results show that the proposed NOMA scheme outperforms three baseline schemes in downlink common throughput,including one solution proposed in an existing literature.

关键词： NOMA resource allocation trajectory design UAV communications user association

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A pipelining task offloading strategy via delay-aware multi-agent reinforcement learning in Cybertwin-enabled 6G network

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Digital Communications and networks 2025年第1期11卷 92-105页

作者： Haiwen Niu Luhan Wang Keliang Du Zhaoming Lu Xiangming Wen Yu Liu Beijing Laboratory of Advanced Information Networks Beijing Univ.of Posts&TelecomBeijing 100876China Beijing Key Lab of Network System Architecture and Convergence Beijing Univ.of Posts&TelecomBeijing 100876China School of Information and Communication Engineering Beijing Univ.of Posts&TelecomBeijing100876China

Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G ***-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading ***,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy *** order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy ***,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov ***,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission ***,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real *** results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.

关键词： Cybertwin Multi-Agent Deep Reinforcement Learning(MADRL) Task offloading Pipelining Delay-aware

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Reinforcement-Learning-Based Trajectory Design and Phase-Shift Control in UAV-Mounted-RIS Communications

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and networking 2025年 3卷 163-175页

作者： Sun, Tianjiao Yin, Sixing Deng, Li Richard Yu, F. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China Shenzhen University Shenzhen Key Laboratory of Digital and Intelligent Technologies and Systems Shenzhen518060 China

Taking advantages of both unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), UAV-mounted-RIS systems are expected to enhance transmission performance in complicated wireless environments. In this paper, we focus on system design for a UAV-mounted-RIS system and investigate joint optimization for the RIS's phase shift and the UAV's trajectory. To cope with the practical issue of inaccessible information on the user terminals' (UTs) location and channel state, a reinforcement learning (RL)-based solution is proposed to find the optimal policy with finite steps of "trial-and-error". As the action space is continuous, the deep deterministic policy gradient (DDPG) algorithm is applied to train the RL model. However, the online interaction between the agent and environment may lead to instability during the training and the assumption of (first-order) Markovian state transition could be impractical in real-world problems. Therefore, the decision transformer (DT) algorithm is employed as an alternative for RL model training to adapt to more general situations of state transition. Experimental results demonstrate that the proposed RL solutions are highly efficient in model training along with acceptable performance close to the benchmark, which relies on conventional optimization algorithms with the UT's locations and channel parameters explicitly known beforehand. © 2023 CCBY.

关键词： Reinforcement learning

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A Liquid Refractive Index Sensor Based On Perforated Structure 24

A Liquid Refractive Index Sensor Based On Perforated Structu...

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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Qian, Xiqin Lan, Chuwen Gao, Zehua Beijing Laboratory of Advanced Information Networks Beijing Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing Beijing China

ISBN: (纸本)9798400710094

This article proposes a rectangular cavity resonator that can achieve liquid refractive index sensing. Place a square solid dielectric block inside the cavity, drill uniformly distributed holes in front of and on top of the dielectric block, and observe the small changes in the refractive index of the liquid by measuring the changes in resonant frequency and microwave transmission coefficient S21. After analyzing various structural parameters, the article achieved a sensitivity of 4122MHz/RIU near 26.3GHz, which is 2.37 times higher than the sensitivity of solid structure sensors. This result also provides a new approach for the design and performance improvement of future high-sensitivity refractive index sensors. © 2024 Copyright held by the owner/author(s).

关键词： Cavity resonators

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Dynamic Trajectory Design for Multi-UAV Assisted Long-Term Data Collection Tasks

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Zijing Guo, Yijun Hao, Jianjun Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

In this paper, we focus on a long-term data collection task executed by multiple cooperative UAVs, and innovatively consider the scenario that UAVs may dynamically exit and rejoin the network to maintain continuous data collection. By considering both transmission rate and energy consumption, we maximize a multi-objective function through optimizing UAV trajectories, device access scheduling, and recharge scheduling. To address the challenges posed by fluctuating UAV numbers, we model the optimization problem as a decentralized partially observed Markov decision process (Dec-POMDP) and propose a Multi-Agent Dynamic QMIX (MADQMIX) algorithm that can adapt to changes in the number of agents. Additionally, to mitigate the dimensionality issue of output actions, we decompose joint actions into sub-agents for decision-making. Numerical results demonstrate the performance gains in both transmission rate and energy consumption achieved by the proposed algorithm, which increase with the scale of the UAV network. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Spatial Geometry Theory-Based Algorithm for Accurate Positioning With Single Vision Beacon

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 13139-13149页

作者： Yang, Boyu Yang, Yang Guo, Caili Zhu, Zhiyu Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China Shanxi University College of Physics and Electronic Engineering Shanxi Taiyuan030006 China

Quick response (QR) code is one of the most popular beacons for indoor positioning. Existing QR code-based algorithms typically require several QR codes for precise positioning, which may need intense QR code deployment or have limited coverage. This paper proposes a visual positioning algorithm based on feature points and triangular structures on a QR code (V-FTQ), which can achieve precise position and orientation estimation using only a monocular camera and a single QR code beacon. In the considered system, a few QR codes are placed on the ceiling, and a user holds a camera in hand to capture the QR code for positioning. In particular, we first propose an efficient matching mechanism to match the feature points of the captured QR beacon to their projections in the image plane of the camera. Then, based on the geometry relations between the feature points and their projections, V-FTQ can calculate the coordinates of the feature points in the camera coordinate. Finally, the orientation and position of the user are estimated based on linear algebra and spatial geometry. Moreover, since V-FTQ may have a few ambiguous solutions, a singular solution elimination scheme is further proposed based on plane geometry and linear programming, which can effectively eliminate the incorrect solutions. Both simulation and experimental results demonstrate that V-FTQ can achieve a positioning accuracy within 10 cm with only one captured beacon, outperforming baseline algorithms in terms of precision and robustness to noise. © 2004-2012 IEEE.

关键词： Geometry

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Perception-Enhanced Multitask Multimodal Semantic Communication for UAV-Assisted Integrated Sensing and Communication system

arXiv

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arXiv 2025年

作者： Guo, Ziji Tong, Haonan Zhang, Zhilong Liu, Danpu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

Recent advances in integrated sensing and communication (ISAC) unmanned aerial vehicles (UAVs) have enabled their widespread deployment in critical applications such as emergency management. This paper investigates the challenge of efficient multitask multimodal data communication in UAV-assisted ISAC systems, in the considered system model, hyperspectral (HSI) and LiDAR data are collected by UAV-mounted sensors for both target classification and data reconstruction at the terrestrial BS. The limited channel capacity and complex environmental conditions pose significant challenges to effective air-to-ground communication. To tackle this issue, we propose a perception-enhanced multitask multimodal semantic communication (PE-MMSC) system that strategically leverages the onboard computational and sensing capabilities of UAVs. In particular, we first propose a robust multimodal feature fusion method that adaptively combines HSI and LiDAR semantics while considering channel noise and task requirements. Then the method introduces a perception-enhanced (PE) module incorporating attention mechanisms to perform coarse classification on UAV side, thereby optimizing the attention-based multimodal fusion and transmission. Experimental results demonstrate that the proposed PE-MMSC system achieves 5%–10% higher target classification accuracy compared to conventional systems without PE module, while maintaining comparable data reconstruction quality with acceptable computational overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk management

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Demo: A Hybrid Semantic RAN Protocol Stack Design for 6G system and its Implementation

Demo: A Hybrid Semantic RAN Protocol Stack Design for 6G Sys...

引用

IEEE Conference on Wireless Communications and networking

作者： Luhan Wang Haiwen Niu Zhaoming Lu Xiangming Wen Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recently, Semantic Communication (SC) has been recognized as a crucial new paradigm in 6G, significantly improving information transmission efficiency. However, the diverse range of service types in 6G networks, such as high-data-volume services like AR/VR/MR and low-data-volume applications requiring high accuracy, such as industrial control and data collection, presents significant challenges to fully replacing the fundamental technologies with SC. Therefore, we design a Hybrid Semantic Communication Ratio Access network (HSC-RAN) protocol stack demo for 6G systems to achieve compatibility and smooth transition between SC and non-SC. Specifically, we take the Physical Downlink Shared Channel (PDSCH) as an example, to efficiently integrate SC with Orthogonal Frequency Division Multiplexing (OFDM). Furthermore, we introduce a novel Downlink Control Information (DCI) format that jointly supports SC and non-SC, enabling real-time video transmission via SC and text transmission through non-SC. Experimental results demonstrate that our approach allows simultaneous transmission of semantic and non-semantic information while maintaining high-quality reconstruction at the receiver.

关键词： 6G mobile communication Wireless communication Protocols OFDM Receivers Streaming media Downlink Semantic communication Real-time systems Next generation networking

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DRLRA: A Deep Reinforcement Learning architecture Based on Resource Allocation for Mining key Players in networks

DRLRA: A Deep Reinforcement Learning Architecture Based on R...

引用

IEEE Conference on Wireless Communications and networking

作者： Wang Kaili Wu Muqing Zhao Min Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

network key nodes are very important in a network, after identifying them, network optimization can be carried out for these nodes. For example, increasing bandwidth or resource allocation, enhancing fault tolerance, protecting and strengthening key nodes. Or destroy key nodes to quickly break down the network, just like spreading rumors and preventing diseases. Different network optimization measures may vary in different network scenarios, but the core is to enhance or break key nodes, thereby affecting the overall network performance and effectiveness. However, most current network key node identification is based on fixed mathematical models or formulas, limiting the possibility of deep information mining in the network. To address this issue, this paper proposes a deep learning network architecture based on resource allocation to identify key nodes in the network. This allows for more in-depth feature extraction of network semantics and structures while combining the resource allocation situation of each node. In the process of gradually disintegrating the network through deep reinforcement learning, finding the strategy with the fastest decrease in cumulative connectivity rate is considered optimal. Finally, this paper compares the performance of six classical algorithms and the optimal algorithm on six real networks. The effectiveness improvement is 8.11% without sacrificing performance, validating the effectiveness of the method.

关键词： Limiting Semantics Fault tolerant systems network architecture Deep reinforcement learning Feature extraction Mathematical models Graph neural networks Resource management Optimization

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A Neural-Based OTFS Channel Estimator

A Neural-Based OTFS Channel Estimator

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IEEE Conference on Wireless Communications and networking

作者： Zhenghe Zhu Yawen Chen Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

orthogonal time frequency space (OTFS) systems are considered as the reliable solution for addressing the challenges of high mobility in sixth generation (6G) scenarios. By modulating data across both the delay and Doppler dimensions, OTFS efficiently handles the double spread problems caused by high mobility. Accurate and efficient channel estimation is essential for ensuring reliable data reception in OTFS systems. In this paper, we propose a convolutional neural network (CNN) based channel estimator within the standard OTFS transceiver framework to estimate the transmitted channel from the received OTFS signal. Simulation results demonstrate that our neural channel estimator achieves superior channel reconstruction and outperforms existing neural-based methods. Moreover, we analyze its performance with higher-order modulation and show that it effectively supports such modulation under good channel conditions, improving spectral efficiency and throughput.

关键词： 6G mobile communication Time-frequency analysis Spectral efficiency Simulation Channel estimation Modulation Transceivers Reliability Convolutional neural networks Standards

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