Pub Date : 2025-02-05DOI: 10.1109/TMC.2025.3527174
{"title":"2024 Reviewers List","authors":"","doi":"10.1109/TMC.2025.3527174","DOIUrl":"https://doi.org/10.1109/TMC.2025.3527174","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2470-2484"},"PeriodicalIF":7.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10874877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361325","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}
Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.
{"title":"Intelligent End-to-End Deterministic Scheduling Across Converged Networks","authors":"Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen","doi":"10.1109/TMC.2025.3530486","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530486","url":null,"abstract":"Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2504-2518"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563922","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}
Pub Date : 2024-12-23DOI: 10.1109/TMC.2024.3513457
Daliang Xu;Wangsong Yin;Hao Zhang;Xin Jin;Ying Zhang;Shiyun Wei;Mengwei Xu;Xuanzhe Liu
Generative tasks, such as text generation and question answering, are essential for mobile applications. Given their inherent privacy sensitivity, executing them on devices is demanded. Nowadays, the execution of these generative tasks heavily relies on the Large Language Models (LLMs). However, the scarce device memory severely hinders the scalability of these models. We present EdgeLLM, an efficient on-device LLM inference system for models whose sizes exceed the device's memory capacity. EdgeLLM is built atop speculative decoding, which delegates most tokens to a smaller, memory-resident (draft) LLM. EdgeLLM integrates three novel techniques: (1) Instead of generating a fixed width and depth token tree, EdgeLLM proposes compute-efficient branch navigation and verification to pace the progress of different branches according to their accepted probability to prevent the wasteful allocation of computing resources to the wrong branch and to verify them all at once efficiently. (2) It uses a self-adaptive fallback strategy that promptly initiates the verification process when the smaller LLM generates an incorrect token. (3) To not block the generation, EdgeLLM proposes speculatively generating tokens during large LLM verification with the compute-IO pipeline. Through extensive experiments, EdgeLLM exhibits impressive token generation speed which is up to 9.3× faster than existing engines.
{"title":"EdgeLLM: Fast On-Device LLM Inference With Speculative Decoding","authors":"Daliang Xu;Wangsong Yin;Hao Zhang;Xin Jin;Ying Zhang;Shiyun Wei;Mengwei Xu;Xuanzhe Liu","doi":"10.1109/TMC.2024.3513457","DOIUrl":"https://doi.org/10.1109/TMC.2024.3513457","url":null,"abstract":"Generative tasks, such as text generation and question answering, are essential for mobile applications. Given their inherent privacy sensitivity, executing them on devices is demanded. Nowadays, the execution of these generative tasks heavily relies on the Large Language Models (LLMs). However, the scarce device memory severely hinders the scalability of these models. We present <monospace>EdgeLLM</monospace>, an efficient on-device LLM inference system for models whose sizes exceed the device's memory capacity. <monospace>EdgeLLM</monospace> is built atop speculative decoding, which delegates most tokens to a smaller, memory-resident (draft) LLM. <monospace>EdgeLLM</monospace> integrates three novel techniques: (1) Instead of generating a fixed width and depth token tree, <monospace>EdgeLLM</monospace> proposes compute-efficient branch navigation and verification to pace the progress of different branches according to their accepted probability to prevent the wasteful allocation of computing resources to the wrong branch and to verify them all at once efficiently. (2) It uses a self-adaptive fallback strategy that promptly initiates the verification process when the smaller LLM generates an incorrect token. (3) To not block the generation, <monospace>EdgeLLM</monospace> proposes speculatively generating tokens during large LLM verification with the compute-IO pipeline. Through extensive experiments, <monospace>EdgeLLM</monospace> exhibits impressive token generation speed which is up to 9.3× faster than existing engines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3256-3273"},"PeriodicalIF":7.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564041","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}
In the Internet of Things (IoT), numerous devices and sensors are deployed to collect data sets. Although some IoT devices can process data locally, most devices may have limited power and computational capability. Since mobile edge computing (MEC) is a new paradigm to provide strong computing capability at the edge of networks close to users, these devices can offload their tasks to MEC servers. Therefore, designing an efficient computation offloading strategy to decide whether the tasks to be offloaded to MEC servers becomes crucial. In this paper, we study the computation offloading for IoT devices based on a non-cooperative game with one-shot random access, where users’ offloading decisions can be made independently to realize distributed offloading. In particular, we discuss the offloading game with and without sharing information among devices and find the Nash equilibrium (NE). Besides, we analyze the effective bandwidth as a performance metric from a device perspective, which considering the Quality of Service (QoS) of network layer while analyzing users’ offloading strategies. Simulation results show the effectiveness of proposed strategies and the impact of offloading tasks to users’ strategies in time-varying channel based on effective bandwidth.
{"title":"Offloading Game for Mobile Edge Computing With Random Access in IoT","authors":"Rui Han;Yue Yu;Qingzhe Zeng;Jiaxing Wang;Lin Bai;Jinho Choi;Wei Zhang","doi":"10.1109/TMC.2024.3514204","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514204","url":null,"abstract":"In the Internet of Things (IoT), numerous devices and sensors are deployed to collect data sets. Although some IoT devices can process data locally, most devices may have limited power and computational capability. Since mobile edge computing (MEC) is a new paradigm to provide strong computing capability at the edge of networks close to users, these devices can offload their tasks to MEC servers. Therefore, designing an efficient computation offloading strategy to decide whether the tasks to be offloaded to MEC servers becomes crucial. In this paper, we study the computation offloading for IoT devices based on a non-cooperative game with one-shot random access, where users’ offloading decisions can be made independently to realize distributed offloading. In particular, we discuss the offloading game with and without sharing information among devices and find the Nash equilibrium (NE). Besides, we analyze the effective bandwidth as a performance metric from a device perspective, which considering the Quality of Service (QoS) of network layer while analyzing users’ offloading strategies. Simulation results show the effectiveness of proposed strategies and the impact of offloading tasks to users’ strategies in time-varying channel based on effective bandwidth.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3316-3329"},"PeriodicalIF":7.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583271","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}
Pub Date : 2024-12-11DOI: 10.1109/TMC.2024.3514852
Chiao-Wen Lin;De-Nian Yang;Wanjiun Liao
Virtual reality (VR) imaging is 360°, which requires a large bandwidth for video transmission. To address this challenge, tile-based streaming has been proposed to deliver only the focused part of the video instead of the entire one. However, the impact of cybersickness, akin to motion sickness, on tile selection in VR has not been explored. In this paper, we investigate Multi-user Tile Streaming with Cybersickness Control (MTSCC) in an adaptive 360$^circ$ video streaming system with multicast and cybersickness alleviation. We propose a novel $m^{2}$-competitive online algorithm that utilizes Individual Sickness Indicator (ISI) and Bitrate Restriction Indicator (BRI) to evaluate user cybersickness tendency and network bandwidth efficiency. Moreover, we introduce the Video Loss Indicator (VLI) and Quality Variance Indicator (QVI) to assess video quality loss and quality difference between tiles. We also propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward (video quality) and cost (cybersickness) estimation. The algorithm learns the weighting factor of each user's cost to slow down cybersickness accumulation for users with high cybersickness tendencies. We prove that the algorithm converges to an optimal solution over time. According to simulation with real network settings, our proposed algorithms outperform baselines in terms of video quality and cybersickness accumulation.
{"title":"Mobile Tile-Based 360$^circ$∘ Video Multicast With Cybersickness Alleviation","authors":"Chiao-Wen Lin;De-Nian Yang;Wanjiun Liao","doi":"10.1109/TMC.2024.3514852","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514852","url":null,"abstract":"Virtual reality (VR) imaging is 360°, which requires a large bandwidth for video transmission. To address this challenge, tile-based streaming has been proposed to deliver only the focused part of the video instead of the entire one. However, the impact of cybersickness, akin to motion sickness, on tile selection in VR has not been explored. In this paper, we investigate Multi-user Tile Streaming with Cybersickness Control (MTSCC) in an adaptive 360<inline-formula><tex-math>$^circ$</tex-math></inline-formula> video streaming system with multicast and cybersickness alleviation. We propose a novel <inline-formula><tex-math>$m^{2}$</tex-math></inline-formula>-competitive online algorithm that utilizes Individual Sickness Indicator (ISI) and Bitrate Restriction Indicator (BRI) to evaluate user cybersickness tendency and network bandwidth efficiency. Moreover, we introduce the Video Loss Indicator (VLI) and Quality Variance Indicator (QVI) to assess video quality loss and quality difference between tiles. We also propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward (video quality) and cost (cybersickness) estimation. The algorithm learns the weighting factor of each user's cost to slow down cybersickness accumulation for users with high cybersickness tendencies. We prove that the algorithm converges to an optimal solution over time. According to simulation with real network settings, our proposed algorithms outperform baselines in terms of video quality and cybersickness accumulation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3423-3440"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583228","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}
In this paper, we present our design and analysis of a Joint Encoding and Enhancement (JEE) system for low-light video analytics in mobile edge networks. First, it is observed that, relying solely on a single pipeline for encoding and enhancement of mobile videos proves insufficient, because of the fluctuations in end-edge bandwidth and computing resources. Therefore, two distinct pipelines are introduced in the JEE system, namely, the encode-decode-enhance pipeline and the enhance-encode-decode pipeline. We then characterize the relationship of accuracy, transmission overhead, and computing overhead of these two pipelines through extensive experiments. Considering the significant demands of transmission and computing for low-light videos, we formulate an optimization problem to strike a balance between accuracy and delay, where the available end-edge bandwidth and computing resources are unknown in advance. To solve this mixed-integer nonlinear programming problem, we propose an algorithm based on online gradient descent, enabling adaptive pipeline selection and joint encoding and enhancement configuration. Theoretical analysis indicates that the proposed algorithm achieves sub-linear dynamic regret, highlighting its capability to the accuracy improvement and delay reduction in online environments. Experimental comparison against baselines demonstrates that, JEE can achieve up to a 27.32% increase in accuracy and a 26.18% reduction in delay.
{"title":"Joint Encoding and Enhancement for Low-Light Video Analytics in Mobile Edge Networks","authors":"Yuanyi He;Peng Yang;Tian Qin;Jiawei Hou;Ning Zhang","doi":"10.1109/TMC.2024.3514214","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514214","url":null,"abstract":"In this paper, we present our design and analysis of a Joint Encoding and Enhancement (JEE) system for low-light video analytics in mobile edge networks. First, it is observed that, relying solely on a single pipeline for encoding and enhancement of mobile videos proves insufficient, because of the fluctuations in end-edge bandwidth and computing resources. Therefore, two distinct pipelines are introduced in the JEE system, namely, the encode-decode-enhance pipeline and the enhance-encode-decode pipeline. We then characterize the relationship of accuracy, transmission overhead, and computing overhead of these two pipelines through extensive experiments. Considering the significant demands of transmission and computing for low-light videos, we formulate an optimization problem to strike a balance between accuracy and delay, where the available end-edge bandwidth and computing resources are unknown in advance. To solve this mixed-integer nonlinear programming problem, we propose an algorithm based on online gradient descent, enabling adaptive pipeline selection and joint encoding and enhancement configuration. Theoretical analysis indicates that the proposed algorithm achieves sub-linear dynamic regret, highlighting its capability to the accuracy improvement and delay reduction in online environments. Experimental comparison against baselines demonstrates that, JEE can achieve up to a 27.32% increase in accuracy and a 26.18% reduction in delay.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3330-3345"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583182","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}
The integration of time-sensitive networking (TSN) and fifth-generation (5G) offers a promising solution for real-time and reliable data transmission in the Industrial Internet of Things (IIoT). However, current research focuses on traffic scheduling in TSN-5G networks to support low latency. New challenges arise when TSN-5G networks leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) to achieve low latency and high reliability. Simply combining TAS and FRER (SCTF) requires scheduling all time-triggered (TT) flows and their replica flows, which substantially increases the computational complexity of gate control lists (GCLs) and severely weakens scheduling capabilities. Moreover, the packet elimination function (PEF) in FRER may induce packet misordering. In this paper, we propose an efficient and fault-tolerant time-aware shaper (EF-TAS) mechanism for TSN-5G networks. EF-TAS only allocates timeslots for TT flows, while replica TT (RT) flows are delivered using a best-effort strategy. Due to the potential violation of deadlines in RT flows, we design an adaptive cyclic GCL window (ACGW)-based hybrid scheduling (AHS) algorithm to schedule TT and RT flows differentially. The AHS algorithm utilizes network calculus to ensure the timely arrival of RT flows without affecting the deterministic transmission of TT flows. In particular, we provide upper bounds on the amount of reordering to quantify the disorder caused by PEF and analyze the impact of introducing the packet ordering function (POF) on EF-TAS performance. The evaluation results show that EF-TAS not only meets the reliability and deadline requirements but also significantly reduces the total number of GCL entries and the computation time of GCLs compared to state-of-the-art methods.
{"title":"Optimizing Fault-Tolerant Time-Aware Flow Scheduling in TSN-5G Networks","authors":"Guizhen Li;Shuo Wang;Yudong Huang;Tao Huang;Yuanhao Cui;Zehui Xiong","doi":"10.1109/TMC.2024.3510604","DOIUrl":"https://doi.org/10.1109/TMC.2024.3510604","url":null,"abstract":"The integration of time-sensitive networking (TSN) and fifth-generation (5G) offers a promising solution for real-time and reliable data transmission in the Industrial Internet of Things (IIoT). However, current research focuses on traffic scheduling in TSN-5G networks to support low latency. New challenges arise when TSN-5G networks leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) to achieve low latency and high reliability. Simply combining TAS and FRER (SCTF) requires scheduling all time-triggered (TT) flows and their replica flows, which substantially increases the computational complexity of gate control lists (GCLs) and severely weakens scheduling capabilities. Moreover, the packet elimination function (PEF) in FRER may induce packet misordering. In this paper, we propose an efficient and fault-tolerant time-aware shaper (EF-TAS) mechanism for TSN-5G networks. EF-TAS only allocates timeslots for TT flows, while replica TT (RT) flows are delivered using a best-effort strategy. Due to the potential violation of deadlines in RT flows, we design an adaptive cyclic GCL window (ACGW)-based hybrid scheduling (AHS) algorithm to schedule TT and RT flows differentially. The AHS algorithm utilizes network calculus to ensure the timely arrival of RT flows without affecting the deterministic transmission of TT flows. In particular, we provide upper bounds on the amount of reordering to quantify the disorder caused by PEF and analyze the impact of introducing the packet ordering function (POF) on EF-TAS performance. The evaluation results show that EF-TAS not only meets the reliability and deadline requirements but also significantly reduces the total number of GCL entries and the computation time of GCLs compared to state-of-the-art methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3441-3455"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583184","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}
Pub Date : 2024-12-09DOI: 10.1109/TMC.2024.3514165
Lin Bai;Jinpeng Xu;Jiaxing Wang;Rui Han;Jinho Choi
Cell-free technology is considered a pivotal advancement for next-generation mobile communications, which can effectively enhance the quality of service for user equipments (UEs) located at the cell edge. For cell-free systems, in this paper, we propose a hybrid downlink transmission method that combines non-orthogonal multiple access (NOMA) and multiuser diversity (MUD). To evaluate the communication performance of the system, we derive closed-form expressions for both instantaneous and average sum rates of UEs using the NOMA and MUD transmission methods. Furthermore, we comprehensively investigate the spectrum efficiency of the NOMA and MUD transmission methods to provide a basis for selecting the hybrid transmission strategy. On the basis of the proposed hybrid transmission strategy, we can derive an optimal hybrid transmission strategy for the scenarios with two access points (APs) and two UEs. Particularly, we extend the aforementioned strategy to the scenarios with multiple UEs, and formulate an optimization problem to maximize the system spectrum efficiency subject to the transmission strategy and power allocation. Furthermore, we propose a low-complexity user selection strategy and power allocation algorithm to solve the problem. Numerical results demonstrate that the hybrid transmission method and power allocation strategy can achieve higher system spectrum efficiency. Our results reveal the influence of key parameters on the downlink spectrum efficiency, analytically and numerically.
{"title":"Efficient Hybrid Transmission for Cell-Free Systems via NOMA and Multiuser Diversity","authors":"Lin Bai;Jinpeng Xu;Jiaxing Wang;Rui Han;Jinho Choi","doi":"10.1109/TMC.2024.3514165","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514165","url":null,"abstract":"Cell-free technology is considered a pivotal advancement for next-generation mobile communications, which can effectively enhance the quality of service for user equipments (UEs) located at the cell edge. For cell-free systems, in this paper, we propose a hybrid downlink transmission method that combines non-orthogonal multiple access (NOMA) and multiuser diversity (MUD). To evaluate the communication performance of the system, we derive closed-form expressions for both instantaneous and average sum rates of UEs using the NOMA and MUD transmission methods. Furthermore, we comprehensively investigate the spectrum efficiency of the NOMA and MUD transmission methods to provide a basis for selecting the hybrid transmission strategy. On the basis of the proposed hybrid transmission strategy, we can derive an optimal hybrid transmission strategy for the scenarios with two access points (APs) and two UEs. Particularly, we extend the aforementioned strategy to the scenarios with multiple UEs, and formulate an optimization problem to maximize the system spectrum efficiency subject to the transmission strategy and power allocation. Furthermore, we propose a low-complexity user selection strategy and power allocation algorithm to solve the problem. Numerical results demonstrate that the hybrid transmission method and power allocation strategy can achieve higher system spectrum efficiency. Our results reveal the influence of key parameters on the downlink spectrum efficiency, analytically and numerically.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3359-3371"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583183","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}
Pub Date : 2024-12-09DOI: 10.1109/TMC.2024.3514895
Xiaoyu Li;Jia Liu;Zihao Lin;Xuan Liu;Yanyan Wang;Shigeng Zhang;Baoliu Ye
A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the deployability and the scalability of a boundary. In this paper, we propose a brand new RFID-based virtual boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna AoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. Besides, we consider the presence of multipath in the real world applications, model the effect on signals in the dynamic scenarios, and demonstrate the robustness of our phase gradient-based scheme under multipath. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary, with a mean detection error of only 8.6 cm.
{"title":"Advancing RFID Technology for Virtual Boundary Detection","authors":"Xiaoyu Li;Jia Liu;Zihao Lin;Xuan Liu;Yanyan Wang;Shigeng Zhang;Baoliu Ye","doi":"10.1109/TMC.2024.3514895","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514895","url":null,"abstract":"A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the deployability and the scalability of a boundary. In this paper, we propose a brand new RFID-based virtual boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna AoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. Besides, we consider the presence of multipath in the real world applications, model the effect on signals in the dynamic scenarios, and demonstrate the robustness of our phase gradient-based scheme under multipath. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary, with a mean detection error of only 8.6 cm.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3407-3422"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583227","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}
Pub Date : 2024-12-03DOI: 10.1109/TMC.2024.3509915
Houxin Gong;Haishuai Wang;Peng Zhang;Sheng Zhou;Hongyang Chen;Jiajun Bu
With the rapid development of mobile network technology and wearable mobile devices, user-scenario interactions generate a large amount of user behavioral data in the form of multivariate event sequences. Due to data isolation, these multi-scenario events need to be jointly trained to achieve better prediction results. However, traditional federated learning methods face significant challenges when handling distributed event sequences. And the effectiveness of existing modeling approaches for event sequences in federated contexts has not been thoroughly explored. To this end, we propose Federated Multivariate Temporal Point Processes (FedMTPP), which enables learning from distributed event sequences within a novel federated learning framework and leverages efficient event modeling technology, MTPP, to forecast future events. Specifically, FedMTPP restores the temporal structure of the original event sequence by rearranging event embeddings and redesigns the autoregressive-based hidden representation computation in traditional MTPP, making it more suitable for federated prediction tasks. Additionally, FedMTPP incorporates advanced encryption techniques to effectively safeguard user privacy and security. Experimental results on both synthetic and real datasets demonstrate that FedMTPP substantially improves the performance of local models and achieves results comparable to state-of-the-art centralized MTPP methods.
{"title":"FedMTPP: Federated Multivariate Temporal Point Processes for Distributed Event Sequence Forecasting","authors":"Houxin Gong;Haishuai Wang;Peng Zhang;Sheng Zhou;Hongyang Chen;Jiajun Bu","doi":"10.1109/TMC.2024.3509915","DOIUrl":"https://doi.org/10.1109/TMC.2024.3509915","url":null,"abstract":"With the rapid development of mobile network technology and wearable mobile devices, user-scenario interactions generate a large amount of user behavioral data in the form of multivariate event sequences. Due to data isolation, these multi-scenario events need to be jointly trained to achieve better prediction results. However, traditional federated learning methods face significant challenges when handling distributed event sequences. And the effectiveness of existing modeling approaches for event sequences in federated contexts has not been thoroughly explored. To this end, we propose Federated Multivariate Temporal Point Processes (FedMTPP), which enables learning from distributed event sequences within a novel federated learning framework and leverages efficient event modeling technology, MTPP, to forecast future events. Specifically, FedMTPP restores the temporal structure of the original event sequence by rearranging event embeddings and redesigns the autoregressive-based hidden representation computation in traditional MTPP, making it more suitable for federated prediction tasks. Additionally, FedMTPP incorporates advanced encryption techniques to effectively safeguard user privacy and security. Experimental results on both synthetic and real datasets demonstrate that FedMTPP substantially improves the performance of local models and achieves results comparable to state-of-the-art centralized MTPP methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3302-3315"},"PeriodicalIF":7.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583270","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}
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