IEEE Transactions on Mobile Computing最新文献_第7页

Towards Communication-Efficient Cooperative Perception via Planning-Oriented Feature Sharing

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-13 DOI: 10.1109/TMC.2024.3496856

Qi Xie;Xiaobo Zhou;Tianyu Hong;Wenkai Hu;Wenyu Qu;Tie Qiu

{"title":"Towards Communication-Efficient Cooperative Perception via Planning-Oriented Feature Sharing","authors":"Qi Xie;Xiaobo Zhou;Tianyu Hong;Wenkai Hu;Wenyu Qu;Tie Qiu","doi":"10.1109/TMC.2024.3496856","DOIUrl":"https://doi.org/10.1109/TMC.2024.3496856","url":null,"abstract":"Autonomous driving systems are fundamentally composed of sequential modular tasks, i.e., perception, prediction, and planning. For connected autonomous vehicles (CAVs), cooperative perception offers a promising solution to surpass their perception limitations, such as occlusion, by sharing sensing data with each other through wireless communication. Existing works typically prioritize sharing data from potential object-containing areas to maximize object detection accuracy under constrained communication resources. However, such detection-oriented approaches ignore a crucial fact that more accurate detection does not equal safer planning. Sharing large amounts of sensing data for detection accuracy can lead to communication resource wastage and performance degradation of subsequent driving tasks. To address this, we introduce Plan2comm, a communication-efficient cooperative perception framework via planning-oriented feature sharing, which shares only sensing data around planned trajectories to enable safer planning rather than mere detection accuracy. Specifically, a planning-oriented communication mechanism is designed to select and transmit the most valuable features from the perspective of the planning task. Moreover, an uncertainty-aware spatial-temporal feature fusion strategy is proposed to enhance high-quality information aggregation. Comprehensive experiments demonstrate that Plan2comm outperforms all other cooperative perception methods on motion prediction performance, and is more communication-efficient.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2551-2563"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-Stream Adaptive Bitrate Streaming for Short-Video Services

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-13 DOI: 10.1109/TMC.2024.3497954

Yuming Zhang;Shengtong Zhu;Yan Liu;Lingfeng Guo;Ji Li;Jack Y. B. Lee

{"title":"Inter-Stream Adaptive Bitrate Streaming for Short-Video Services","authors":"Yuming Zhang;Shengtong Zhu;Yan Liu;Lingfeng Guo;Ji Li;Jack Y. B. Lee","doi":"10.1109/TMC.2024.3497954","DOIUrl":"https://doi.org/10.1109/TMC.2024.3497954","url":null,"abstract":"Short-video services have seen explosive growth in recent years. Streaming over mobile networks is inherently challenging due to the latter's bandwidth fluctuations, motivating researchers to develop many sophisticated adaptive bitrate (ABR) algorithms to compensate. While ABR, together with prefetching, has been proposed for playlist streaming, its application to non-playlist streaming has received little attention. This work fills this gap by first exploring the efficacy of directly applying ABR to non-playlist streaming. Observing their limitations motivates the development of a new class of inter-stream bitrate adaptation (ISA) algorithms. Unlike ABR, ISA adapts bitrate on a per-video basis, which is not only simpler to implement and deploy but can even outperform ABR algorithms by up to 66.71% across a wide range of networks. Moreover, ISA and ABR are complementary such that they can be combined into Integrated Bitrate Adaptation (IBA) algorithms to raise performance gains further by up to 77.03%. In addition, this work develops a novel adaptive rebuffering duration (ARD) algorithm specifically designed for frame-based playback common in short-video services to further improve their performance under challenging network conditions. Together, ISA and ARD offer a new set of tools with progressive complexity-performance tradeoffs for enhancing the performance of short-video services.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2708-2725"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752828","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Quantum Reinforcement Learning Approach for Joint Resource Allocation and Task Offloading in Mobile Edge Computing

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-13 DOI: 10.1109/TMC.2024.3496918

Xinliang Wei;Xitong Gao;Kejiang Ye;Cheng-Zhong Xu;Yu Wang

{"title":"A Quantum Reinforcement Learning Approach for Joint Resource Allocation and Task Offloading in Mobile Edge Computing","authors":"Xinliang Wei;Xitong Gao;Kejiang Ye;Cheng-Zhong Xu;Yu Wang","doi":"10.1109/TMC.2024.3496918","DOIUrl":"https://doi.org/10.1109/TMC.2024.3496918","url":null,"abstract":"Mobile edge computing (MEC) has revolutionized the way computational tasks are offloaded and latency is reduced by leveraging edge servers close to end devices. Efficient resource allocation and task offloading are crucial for enhancing system performance in MEC environments. Traditional reinforcement learning (RL) approaches have shown promise in optimizing resource allocation and task offloading problems. However, they often face challenges such as high computational complexity and the need for extensive training data. Quantum reinforcement learning (QRL) emerges as a promising solution to overcome these limitations by leveraging quantum computing principles to enhance efficiency and scalability. In this paper, we propose a hybrid quantum-classical non-sequential model for joint resource allocation and task offloading in MEC systems. Our model combines the advantages of RL in handling environmental dynamics and quantum computing in reducing adjustable parameters and accelerating the training process. Extensive experiments demonstrate that our proposed algorithm can achieve higher training and inference performance under various parameter settings compared to traditional RL models and previous QRL models.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2580-2593"},"PeriodicalIF":7.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative Task Offloading and Resource Allocation in Small-Cell MEC: A Multi-Agent PPO-Based Scheme

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3496536

Han Li;Ke Xiong;Yuping Lu;Wei Chen;Pingyi Fan;Khaled Ben Letaief

{"title":"Collaborative Task Offloading and Resource Allocation in Small-Cell MEC: A Multi-Agent PPO-Based Scheme","authors":"Han Li;Ke Xiong;Yuping Lu;Wei Chen;Pingyi Fan;Khaled Ben Letaief","doi":"10.1109/TMC.2024.3496536","DOIUrl":"https://doi.org/10.1109/TMC.2024.3496536","url":null,"abstract":"Small-cell mobile edge computing (SE-MEC) networks amalgamate the virtues of MEC and small-cell networks, enhancing data processing capabilities of user devices (UDs). Nevertheless, time-varying wireless channels, dynamic UD requirements, and severe interference among UDs make it difficult to fully exploit the limited network resources and stably provide computing services for UDs. Therefore, efficient task offloading and resource allocation (TORA) is essential. Moreover, since multiple small cells are deployed, decentralized TORA schemes are preferred in practice. Thus, this paper aims to design distributed adaptive TORA schemes for SE-MEC networks. In pursuit of an eco-friendly design, an optimization problem is formulated to minimize the total energy consumption (TEC) of UDs subject to delay constraints. To effectively deal with network's dynamic characteristics, the reinforce learning framework is applied, where the TEC minimization problem is first modeled as a partially observable Markov decision process (POMDP), and then an efficient multi-agent proximal policy optimization (MAPPO)-based scheme is presented to solve it. In the presented scheme, each small-cell base station (SBS) serves as an agent and is capable of making TORA decisions only with its own local information. To promote collaboration among multiple agents, a global reward function is designed. A state normalization mechanism is also introduced into the presented scheme for enhancing learning performance. Simulation results show that although the proposed MAPPO-based scheme works in a distributed manner, it achieves very similar performance to the centralized one. In addition, it is demonstrated that the state normalization mechanism has a significant effect on reducing TEC.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2346-2359"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MagSpy: Revealing User Privacy Leakage via Magnetometer on Mobile Devices

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3495506

Yongjian Fu;Lanqing Yang;Hao Pan;Yi-Chao Chen;Guangtao Xue;Ju Ren

{"title":"MagSpy: Revealing User Privacy Leakage via Magnetometer on Mobile Devices","authors":"Yongjian Fu;Lanqing Yang;Hao Pan;Yi-Chao Chen;Guangtao Xue;Ju Ren","doi":"10.1109/TMC.2024.3495506","DOIUrl":"https://doi.org/10.1109/TMC.2024.3495506","url":null,"abstract":"Various characteristics of mobile applications (apps) and associated in-app services can reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to restrict access to data related to mobile app usage. This paper outlines a novel approach to extracting detailed app usage information by analyzing electromagnetic (EM) signals emitted from mobile devices during app-related tasks. The proposed system, MagSpy, recovers user privacy information from magnetometer readings that do not require access permissions. This EM leakage becomes complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. To address these challenges, MagSpy employs multiple techniques to extract and identify signals related to app usage. Specifically, the geomagnetic offset signal is canceled using accelerometer and gyroscope sensor data, and a Cascade-LSTM algorithm is used to classify apps and in-app services. MagSpy also uses CWT-based peak detection and a Random Forest classifier to detect PIN inputs. A prototype system was evaluated on over 50 popular mobile apps with 30 devices. Extensive evaluation results demonstrate the efficacy of MagSpy in identifying in-app services (96% accuracy), apps (93.5% accuracy), and extracting PIN input information (96% top-3 accuracy).","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2455-2469"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Optimization of Task Offloading and Resource Allocation of Fog Network by Considering Matching Externalities and Dynamics

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3494793

Jiahui Xu;Yingbiao Yao;Xin Xu;Wei Feng;Pei Li

{"title":"Joint Optimization of Task Offloading and Resource Allocation of Fog Network by Considering Matching Externalities and Dynamics","authors":"Jiahui Xu;Yingbiao Yao;Xin Xu;Wei Feng;Pei Li","doi":"10.1109/TMC.2024.3494793","DOIUrl":"https://doi.org/10.1109/TMC.2024.3494793","url":null,"abstract":"How to jointly optimize task offloading and resource allocation to minimize the task failure rate and task payments remains an unresolved challenge in fog networks. Focusing on this problem, this research formulates a novel task offloading and resource allocation model with two offloading modes and on-demand virtual resource units (VRUs). This model is decomposed into two sub-problems to solve: a joint task offloading and resource allocation optimization problem and a matching problem with externalities and dynamics. First, for a given terminal node (TN) and fog node (FN), this research theoretically derives the optimal offloading ratio and resource allocation strategy to minimize the payment of TNs for two offloading modes, i.e., immediate and queued offloading. Second, in the multi-TNs and multi-FNs scenario, the problem of making the task offloading decision is transformed into a many-to-one matching game by considering externalities and dynamics. Finally, a Deferred acceptance-based Loss ratio and Payment Minimized task Offloading and resource Allocation optimization (DLPMOA) algorithm is proposed to derive a stable and Pareto-optimal match. The simulation results show that the proposed DLPMOA has better performance in terms of task failure rate, task average payment, fog computing resource utilization, and fairness than the state-of-the-art methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2534-2550"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

REM: Enabling Real-Time Neural-Enhanced Video Streaming on Mobile Devices Using Macroblock-Aware Lookup Table

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3496443

Baili Chai;Di Wu;Jinyu Chen;Mengyu Yang;Zelong Wang;Miao Hu

{"title":"REM: Enabling Real-Time Neural-Enhanced Video Streaming on Mobile Devices Using Macroblock-Aware Lookup Table","authors":"Baili Chai;Di Wu;Jinyu Chen;Mengyu Yang;Zelong Wang;Miao Hu","doi":"10.1109/TMC.2024.3496443","DOIUrl":"https://doi.org/10.1109/TMC.2024.3496443","url":null,"abstract":"The demand for mobile video streaming has seen a substantial surge in recent years. However, current platforms heavily depend on network capacity to ensure the delivery of high-quality video streams. The emergence of neural-enhanced video streaming presents a promising solution to address this challenge by leveraging client-side computation, thereby reducing bandwidth consumption. Nonetheless, deploying advanced super-resolution (SR) models on mobile devices is hindered by the computational demands of existing SR models. In this paper, we propose REM, a novel neural-enhanced mobile video streaming framework. REM utilizes a customized lookup table to facilitate real-time neural-enhanced video streaming on mobile devices. Initially, we conduct a series of measurements to identify abundant macroblock redundancies across frames in a video stream. Subsequently, we introduce a dynamic macroblock selection algorithm that prioritizes important macroblocks for neural enhancement. The SR-enhanced results are stored in the lookup table and efficiently reused to meet real-time requirements and minimize resource overhead. By considering macroblock-level characteristics of the video frames, the lookup table enables efficient and fast processing. Additionally, we design a lightweight macroblock-aware SR module to expedite inference. Finally, we perform extensive experiments on various mobile devices. The results demonstrate that REM enhances overall processing throughput by up to 10.2 times and reduces power consumption by up to 58.6% compared to state-of-the-art methods. Consequently, this leads to a 38.06% improvement in the quality of experience for mobile users.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2085-2097"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-Driven Approach

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3495719

Jingbo Liu;Jiacheng Chen;Zongxi Liu;Haibo Zhou

{"title":"Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-Driven Approach","authors":"Jingbo Liu;Jiacheng Chen;Zongxi Liu;Haibo Zhou","doi":"10.1109/TMC.2024.3495719","DOIUrl":"https://doi.org/10.1109/TMC.2024.3495719","url":null,"abstract":"To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes nearest neighbor interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel; 2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations; and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2437-2454"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Delay- and Resource-Aware Satellite UPF Service Optimization

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-08 DOI: 10.1109/TMC.2024.3494043

Chao Wang;Xiao Ma;Ruolin Xing;Sisi Li;Ao Zhou;Shangguang Wang

{"title":"Delay- and Resource-Aware Satellite UPF Service Optimization","authors":"Chao Wang;Xiao Ma;Ruolin Xing;Sisi Li;Ao Zhou;Shangguang Wang","doi":"10.1109/TMC.2024.3494043","DOIUrl":"https://doi.org/10.1109/TMC.2024.3494043","url":null,"abstract":"Executing 5G core network functions on satellites has become crucial to enhance satellite network management and service capabilities. The User Plane Function (UPF) is responsible for efficient data traffic forwarding and is envisioned as a key and pioneering core network function that will be deployed on satellites. However, managing and providing services with satellite UPFs face dual challenges. Limited satellite resources constrain the user scale that a satellite UPF can service, resulting in an unguaranteed service delay. Moreover, the extremely rapid mobility of satellites renders it difficult for satellite UPFs to provide seamless services. To address the above challenges, this paper presents the first-of-its-kind service optimization scheme for satellite UPFs in terms of switch control, state migration, and traffic routing. To provide guaranteed service delay, we provide a theoretical analysis based on the M/G/1 queue model, demonstrating the service delay-resource consumption trade-off. A satellite UPF switch control scheme is integrated into the service optimization process, which can decrease satellite UPF service delay while saving satellite resources by adjusting the switch control parameters. To provide seamless services, we propose a satellite UPF-oriented state-aware service migration and traffic routing (UPF service optimization) algorithm. A policy network-based reinforcement learning approach is employed to dynamically perceive the satellite network’s state as well as the satellite UPF switch state. Building upon the optimization of service delay through satellite UPF switch control, the processes of state-aware state migration and traffic routing are further employed to reduce delay, ensuring seamless service effectively. Experiments reveal that the proposed algorithm outperforms other benchmark algorithms under different metrics. The service delay is reduced by an average of 23.2% compared with other algorithms.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2564-2579"},"PeriodicalIF":7.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixed Numerology-Based Intelligent Resource Management in a Sliced 6G Space–Terrestrial Integrated Radio Access Network 分片 6G 天地一体化无线接入网中基于混合数字技术的智能资源管理

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-08 DOI: 10.1109/TMC.2024.3494842

Ning Hui;Qian Sun;Jie Zeng;Lin Tian;Yuanyuan Wang;Yiqing Zhou

{"title":"Mixed Numerology-Based Intelligent Resource Management in a Sliced 6G Space–Terrestrial Integrated Radio Access Network","authors":"Ning Hui;Qian Sun;Jie Zeng;Lin Tian;Yuanyuan Wang;Yiqing Zhou","doi":"10.1109/TMC.2024.3494842","DOIUrl":"https://doi.org/10.1109/TMC.2024.3494842","url":null,"abstract":"Although resource sharing and mixed numerology among slices are promising for improving wireless resource utilization, these techniques can compromise isolation performance and cause serious inter numerology interference (INI). Therefore, this paper studies wireless resource management in a mixed numerology-based sliced 6G space–terrestrial integrated radio access network (STI-RAN) with the aim of reducing INI and guaranteeing isolation performance while decreasing interference from Doppler frequency shifts caused by the high-speed movement of low-orbit satellites. First, an isolation performance indicator is defined to evaluate different isolation performances, and a universal spectral distance model is formulated to rewrite the INI power model. Next, the dynamic wireless resource management problem is formulated in a discrete form, yielding a scheme called Flex-<inline-formula><tex-math>$mu$</tex-math></inline-formula>, which is designed to reduce the INI and Doppler frequency shifts, guarantee isolation performance, and enhance the SINR. Finally, an intelligent multi-characteristic matrix coding-based social group optimization (MultiMatrix-SGO) algorithm is designed to solve the proposed NP-hard discrete optimization problem. Compared with existing schemes, the system utility is efficiently increased by up to 58.32%, the SINR can converge to 38.44 dB, and the isolation performance is guaranteed while the INI and Doppler frequency shifts are reduced.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1338-1356"},"PeriodicalIF":7.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0