Non-orthogonal multiple access (NOMA) emerges as a superior technology for enhancing spectral efficiency, reducing latency, and improving connectivity compared to orthogonal multiple access. In NOMA networks, successive interference cancellation (SIC) plays a crucial role in decoding user signals sequentially. The challenge lies in the joint optimization of SIC ordering and power allocation, a task complicated by the factorial nature of ordering combinations. This study introduces an innovative solution, the Attention-based SIC Ordering and Power Allocation (ASOPA) framework, targeting an uplink NOMA network with dynamic SIC ordering. ASOPA aims to maximize weighted proportional fairness by employing deep reinforcement learning, strategically decomposing the problem into two manageable subproblems: SIC ordering optimization and optimal power allocation. We use an attention-based neural network to process real-time channel gains and user weights, determining the SIC decoding order for each user. A baseline network, serving as a mimic model, aids in the reinforcement learning process. Once the SIC ordering is established, the power allocation subproblem transforms into a convex optimization problem, enabling efficient calculation of optimal transmit power for all users. Extensive simulations validate ASOPA’s efficacy, demonstrating a performance closely paralleling the exhaustive method, with over 97% confidence in normalized network utility. Compared to the current state-of-the-art implementation, i.e., Tabu search, ASOPA achieves over 97.5% network utility of Tabu search. Furthermore, ASOPA has two orders of magnitude less execution latency than Tabu search when $N=10$ and even three orders magnitude less execution latency less than Tabu search when $N=20$ . Notably, ASOPA maintains a low execution latency of approximately 50 milliseconds in a ten-user NOMA network, aligning with static SIC ordering algorithms. Furthermore, ASOPA demonstrates superior performance over baseline algorithms besides Tabu search in various NOMA network configurations, including scenarios with imperfect channel state information, multiple base stations, and multiple-antenna setups. These results underscore the robustness and effectiveness of ASOPA, demonstrating its ability to ability to achieve good performance across various NOMA network environments.
{"title":"Attention-Based SIC Ordering and Power Allocation for Non-Orthogonal Multiple Access Networks","authors":"Liang Huang;Bincheng Zhu;Runkai Nan;Kaikai Chi;Yuan Wu","doi":"10.1109/TMC.2024.3470828","DOIUrl":"https://doi.org/10.1109/TMC.2024.3470828","url":null,"abstract":"Non-orthogonal multiple access (NOMA) emerges as a superior technology for enhancing spectral efficiency, reducing latency, and improving connectivity compared to orthogonal multiple access. In NOMA networks, successive interference cancellation (SIC) plays a crucial role in decoding user signals sequentially. The challenge lies in the joint optimization of SIC ordering and power allocation, a task complicated by the factorial nature of ordering combinations. This study introduces an innovative solution, the Attention-based SIC Ordering and Power Allocation (ASOPA) framework, targeting an uplink NOMA network with dynamic SIC ordering. ASOPA aims to maximize weighted proportional fairness by employing deep reinforcement learning, strategically decomposing the problem into two manageable subproblems: SIC ordering optimization and optimal power allocation. We use an attention-based neural network to process real-time channel gains and user weights, determining the SIC decoding order for each user. A baseline network, serving as a mimic model, aids in the reinforcement learning process. Once the SIC ordering is established, the power allocation subproblem transforms into a convex optimization problem, enabling efficient calculation of optimal transmit power for all users. Extensive simulations validate ASOPA’s efficacy, demonstrating a performance closely paralleling the exhaustive method, with over 97% confidence in normalized network utility. Compared to the current state-of-the-art implementation, i.e., Tabu search, ASOPA achieves over 97.5% network utility of Tabu search. Furthermore, ASOPA has two orders of magnitude less execution latency than Tabu search when \u0000<inline-formula><tex-math>$N=10$</tex-math></inline-formula>\u0000 and even three orders magnitude less execution latency less than Tabu search when \u0000<inline-formula><tex-math>$N=20$</tex-math></inline-formula>\u0000 . Notably, ASOPA maintains a low execution latency of approximately 50 milliseconds in a ten-user NOMA network, aligning with static SIC ordering algorithms. Furthermore, ASOPA demonstrates superior performance over baseline algorithms besides Tabu search in various NOMA network configurations, including scenarios with imperfect channel state information, multiple base stations, and multiple-antenna setups. These results underscore the robustness and effectiveness of ASOPA, demonstrating its ability to ability to achieve good performance across various NOMA network environments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"939-955"},"PeriodicalIF":7.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938590","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}
This paper explores an interesting worker recruitment challenge where the mobile crowd sensing and computing (MCSC) platform hires workers to complete tasks with varying quality requirements and budget limitations, amidst uncertainties in worker participation and local workloads. We propose an innovative hybrid worker recruitment framework that combines offline and online trading modes. The offline mode enables the platform to overbook long-term workers by pre-signing contracts, thereby managing dynamic service supply. This is modeled as a 0-1 integer linear programming (ILP) problem with probabilistic constraints on service quality and budget. To address the uncertainties that may prevent long-term workers from consistently meeting service quality standards, we also introduce an online temporary worker recruitment scheme as a contingency plan. This scheme ensures seamless service provisioning and is likewise formulated as a 0-1 ILP problem. To tackle these problems with NP-hardness, we develop three algorithms, namely, i) exhaustive searching, ii) unique index-based stochastic searching with risk-aware filter constraint, iii) geometric programming-based successive convex algorithm. These algorithms are implemented in a stagewise manner to achieve optimal or near-optimal solutions. Extensive experiments demonstrate our effectiveness in terms of service quality, time efficiency, etc.
{"title":"Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and Computing","authors":"Minghui Liwang;Zhibin Gao;Seyyedali Hosseinalipour;Zhipeng Cheng;Xianbin Wang;Zhenzhen Jiao","doi":"10.1109/TMC.2024.3470993","DOIUrl":"https://doi.org/10.1109/TMC.2024.3470993","url":null,"abstract":"This paper explores an interesting worker recruitment challenge where the mobile crowd sensing and computing (MCSC) platform hires workers to complete tasks with varying quality requirements and budget limitations, amidst uncertainties in worker participation and local workloads. We propose an innovative hybrid worker recruitment framework that combines offline and online trading modes. The offline mode enables the platform to overbook long-term workers by pre-signing contracts, thereby managing dynamic service supply. This is modeled as a 0-1 integer linear programming (ILP) problem with probabilistic constraints on service quality and budget. To address the uncertainties that may prevent long-term workers from consistently meeting service quality standards, we also introduce an online temporary worker recruitment scheme as a contingency plan. This scheme ensures seamless service provisioning and is likewise formulated as a 0-1 ILP problem. To tackle these problems with NP-hardness, we develop three algorithms, namely, \u0000<italic>i)</i>\u0000 exhaustive searching, \u0000<italic>ii)</i>\u0000 unique index-based stochastic searching with risk-aware filter constraint, \u0000<italic>iii)</i>\u0000 geometric programming-based successive convex algorithm. These algorithms are implemented in a stagewise manner to achieve optimal or near-optimal solutions. Extensive experiments demonstrate our effectiveness in terms of service quality, time efficiency, etc.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1055-1072"},"PeriodicalIF":7.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938597","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 : 2024-09-30DOI: 10.1109/TMC.2024.3470831
Zhaolong Ning;Hongjing Ji;Xiaojie Wang;Edith C. H. Ngai;Lei Guo;Jiangchuan Liu
The development of Internet of Things (IoT) technology has led to the emergence of a large number of Intelligent Sensing Devices (ISDs). Since their limited physical sizes constrain the battery capacity, wireless powered IoT networks assisted by Unmanned Aerial Vehicles (UAVs) for energy transfer and data acquisition have attracted great interest. In this paper, we formulate an optimization problem to maximize system energy efficiency while satisfying the constraints of UAV mobility and safety, ISD quality of service and task completion time. The formulated problem is constructed as a Constrained Markov Decision Process (CMDP) model, and a Multi-agent Constrained Deep Reinforcement Learning (MCDRL) algorithm is proposed to learn the optimal UAV movement policy. In addition, an ISD-UAV connection assignment algorithm is designed to manage the connection in the UAV sensing range. Finally, performance evaluations and analysis based on real-world data demonstrate the superiority of our solution.
{"title":"Joint Optimization of Data Acquisition and Trajectory Planning for UAV-Assisted Wireless Powered Internet of Things","authors":"Zhaolong Ning;Hongjing Ji;Xiaojie Wang;Edith C. H. Ngai;Lei Guo;Jiangchuan Liu","doi":"10.1109/TMC.2024.3470831","DOIUrl":"https://doi.org/10.1109/TMC.2024.3470831","url":null,"abstract":"The development of Internet of Things (IoT) technology has led to the emergence of a large number of Intelligent Sensing Devices (ISDs). Since their limited physical sizes constrain the battery capacity, wireless powered IoT networks assisted by Unmanned Aerial Vehicles (UAVs) for energy transfer and data acquisition have attracted great interest. In this paper, we formulate an optimization problem to maximize system energy efficiency while satisfying the constraints of UAV mobility and safety, ISD quality of service and task completion time. The formulated problem is constructed as a Constrained Markov Decision Process (CMDP) model, and a Multi-agent Constrained Deep Reinforcement Learning (MCDRL) algorithm is proposed to learn the optimal UAV movement policy. In addition, an ISD-UAV connection assignment algorithm is designed to manage the connection in the UAV sensing range. Finally, performance evaluations and analysis based on real-world data demonstrate the superiority of our solution.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1016-1030"},"PeriodicalIF":7.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938593","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 deep neural network (DNN) intelligence into embedded mobile devices is expanding rapidly, supporting a wide range of applications. DNN compression techniques, which adapt models to resource-constrained mobile environments, often force a trade-off between efficiency and accuracy. Distributed DNN inference, leveraging multiple mobile devices, emerges as a promising alternative to enhance inference efficiency without compromising accuracy. However, effectively decoupling DNN models into fine-grained components for optimal parallel acceleration presents significant challenges. Current partitioning methods, including layer-level and operator or channel-level partitioning, provide only partial solutions and struggle with the heterogeneous nature of DNN compilation frameworks, complicating direct model offloading. In response, we introduce AdaKnife, an adaptive framework for accelerated inference across heterogeneous mobile devices. AdaKnife enables on-demand mixed-granularity DNN partitioning via computational graph analysis, facilitates efficient cross-framework model transitions with operator optimization for offloading, and improves the feasibility of parallel partitioning using a greedy operator parallelism algorithm. Our empirical studies show that AdaKnife achieves a 66.5% reduction in latency compared to baselines.
{"title":"AdaKnife: Flexible DNN Offloading for Inference Acceleration on Heterogeneous Mobile Devices","authors":"Sicong Liu;Hao Luo;XiaoChen Li;Yao Li;Bin Guo;Zhiwen Yu;YuZhan Wang;Ke Ma;YaSan Ding;Yuan Yao","doi":"10.1109/TMC.2024.3466931","DOIUrl":"https://doi.org/10.1109/TMC.2024.3466931","url":null,"abstract":"The integration of deep neural network (DNN) intelligence into embedded mobile devices is expanding rapidly, supporting a wide range of applications. DNN compression techniques, which adapt models to resource-constrained mobile environments, often force a trade-off between efficiency and accuracy. Distributed DNN inference, leveraging multiple mobile devices, emerges as a promising alternative to enhance inference efficiency without compromising accuracy. However, effectively decoupling DNN models into fine-grained components for optimal parallel acceleration presents significant challenges. Current partitioning methods, including layer-level and operator or channel-level partitioning, provide only partial solutions and struggle with the heterogeneous nature of DNN compilation frameworks, complicating direct model offloading. In response, we introduce AdaKnife, an adaptive framework for accelerated inference across heterogeneous mobile devices. AdaKnife enables on-demand mixed-granularity DNN partitioning via computational graph analysis, facilitates efficient cross-framework model transitions with operator optimization for offloading, and improves the feasibility of parallel partitioning using a greedy operator parallelism algorithm. Our empirical studies show that AdaKnife achieves a 66.5% reduction in latency compared to baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"736-748"},"PeriodicalIF":7.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938469","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}
We study the design of scheduling policies to minimize the monitoring error of a collection of correlated sources, where only one source can be observed at any given time. We model correlated sources as a discrete-time Wiener process, where the increments are multivariate normal random variables, with a general covariance matrix that captures the correlation structure between the sources. Under a Kalman filter-based optimal estimation framework, we show that the performance of all scheduling policies oblivious to instantaneous error can be lower and upper bounded by the weighted sum of Age of Information (AoI) across the sources for appropriately chosen weights. We use this insight to design scheduling policies that are only a constant factor away from optimality, and make the rather surprising observation that AoI-based scheduling that ignores correlation is sufficient to obtain performance guarantees. We also derive scaling results showing that the optimal error scales roughly as the square of the system's dimensionality, even with correlation. Finally, we provide simulation results to verify our claims.
我们研究了调度策略的设计，以最小化一组相关源的监控误差，其中在任何给定时间只能观察到一个源。我们将相关源建模为离散时间维纳过程，其中增量是多变量正态随机变量，具有捕获源之间相关结构的一般协方差矩阵。在基于卡尔曼滤波的最优估计框架下，我们证明了所有调度策略的性能对瞬时误差无关，可以由适当选择权重的源之间的信息年龄（Age of Information, AoI）加权和来确定上下界。我们使用这种见解来设计调度策略，这些策略离最优性只有一个恒定的因素，并得出相当令人惊讶的观察结果，即忽略相关性的基于aoi的调度足以获得性能保证。我们还推导出缩放结果，表明最优误差大致为系统维数的平方，即使具有相关性。最后，我们提供了仿真结果来验证我们的说法。
{"title":"Monitoring Correlated Sources: AoI-Based Scheduling is Nearly Optimal","authors":"Rudrapatna Vallabh Ramakanth;Vishrant Tripathi;Eytan Modiano","doi":"10.1109/TMC.2024.3471391","DOIUrl":"https://doi.org/10.1109/TMC.2024.3471391","url":null,"abstract":"We study the design of scheduling policies to minimize the monitoring error of a collection of correlated sources, where only one source can be observed at any given time. We model correlated sources as a discrete-time Wiener process, where the increments are multivariate normal random variables, with a general covariance matrix that captures the correlation structure between the sources. Under a Kalman filter-based optimal estimation framework, we show that the performance of all scheduling policies oblivious to instantaneous error can be lower and upper bounded by the weighted sum of Age of Information (AoI) across the sources for appropriately chosen weights. We use this insight to design scheduling policies that are only a constant factor away from optimality, and make the rather surprising observation that AoI-based scheduling that ignores correlation is sufficient to obtain performance guarantees. We also derive scaling results showing that the optimal error scales roughly as the square of the system's dimensionality, even with correlation. Finally, we provide simulation results to verify our claims.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1043-1054"},"PeriodicalIF":7.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938596","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 : 2024-09-30DOI: 10.1109/TMC.2024.3461784
Fenglin Zhang;Zhebin Zhang;Le Kang;Anfu Zhou;Huadong Ma
Thoracoabdominal Asynchrony (TAA) is a key metric in respiration monitoring, which characterizes the non-parallel periodical motion of human's rib cage (RC) and abdomen (AB) during each breath. Long-term measurement of TAA plays a significant role in respiration health tracking. Existing TAA measurement methods including Respiratory Inductive Plethysmography (RIP) and Optoelectronic Plethysmography (OEP) all intrusive to subjects and have certain requirements on operation conditions, which limit their usage to hospital scenario. To address this gap, we propose mmTAA , the first mmWave-based, non-intrusive TAA measurement system ready for ubiquitous usage in daily-life. In mmTAA , we design a Two-stage RC-AB centroid finding module, aiming to identify the most probable location of RC-AB centroid, which can best represent RC and AB in mmWave sensing scenario. Subsequently, we design TAANet, a novel Convolutional Neural Network (CNN)-based architecture with residual modules, tailored for TAA measurement. Meanwhile, in order to address the imbalance of continuous data, we add imbalance information equalizer including feature and label equalizer during network training. We implement mmTAA on a commonly used multi-antenna mmWave radar. We prototype, deploy and evaluate mmTAA on 25 subjects and 25.7h data in total. mmTAA achieves 4.01 $^{circ }$ MAE and 1.56 $^{circ }$ average error, close to OEP method.
{"title":"mmTAA: A Contact-Less Thoracoabdominal Asynchrony Measurement System Based on mmWave Sensing","authors":"Fenglin Zhang;Zhebin Zhang;Le Kang;Anfu Zhou;Huadong Ma","doi":"10.1109/TMC.2024.3461784","DOIUrl":"https://doi.org/10.1109/TMC.2024.3461784","url":null,"abstract":"Thoracoabdominal Asynchrony (TAA) is a key metric in respiration monitoring, which characterizes the non-parallel periodical motion of human's rib cage (RC) and abdomen (AB) during each breath. Long-term measurement of TAA plays a significant role in respiration health tracking. Existing TAA measurement methods including Respiratory Inductive Plethysmography (RIP) and Optoelectronic Plethysmography (OEP) all intrusive to subjects and have certain requirements on operation conditions, which limit their usage to hospital scenario. To address this gap, we propose \u0000<i>mmTAA</i>\u0000, the first mmWave-based, non-intrusive TAA measurement system ready for ubiquitous usage in daily-life. In \u0000<i>mmTAA</i>\u0000, we design a Two-stage RC-AB centroid finding module, aiming to identify the most probable location of RC-AB centroid, which can best represent RC and AB in mmWave sensing scenario. Subsequently, we design TAANet, a novel Convolutional Neural Network (CNN)-based architecture with residual modules, tailored for TAA measurement. Meanwhile, in order to address the imbalance of continuous data, we add imbalance information equalizer including feature and label equalizer during network training. We implement \u0000<i>mmTAA</i>\u0000 on a commonly used multi-antenna mmWave radar. We prototype, deploy and evaluate \u0000<i>mmTAA</i>\u0000 on 25 subjects and 25.7h data in total. \u0000<i>mmTAA</i>\u0000 achieves 4.01\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000 MAE and 1.56\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000 average error, close to OEP method.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"627-641"},"PeriodicalIF":7.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938389","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 : 2024-09-27DOI: 10.1109/TMC.2024.3470328
Emily Jimin Roh;Hankyul Baek;Donghyeon Kim;Joongheon Kim
Object detection applications, especially in autonomous driving, have drawn attention due to the advancements in deep learning. Additionally, with continuous improvements in classical convolutional neural networks (CNNs), there has been a notable enhancement in both the efficiency and speed of these applications, making autonomous driving more reliable and effective. However, due to the exponentially rapid growth in the complexity and scale of visual signals used in object detection, there are limitations regarding computation speeds while conducting object detection solely with classical computing. Motivated by this, this paper proposes the quantum object detection engine (QODE), which implements a quantum version of CNN, named QCNN, in object detection. Furthermore, this paper proposes a novel fast quantum convolution algorithm that processes the multi-channel of visual signals based on a small number of qubits and constructs the output channel data, thereby achieving relieved computational complexity. Our QODE, equipped with fast quantum convolution, demonstrates feasibility in object detection with multi-channel data, addressing a limitation of current QCNNs due to the scarcity of qubits in the current era of quantum computing. Moreover, this paper introduces a heterogeneous knowledge distillation training algorithm that enhances the performance of our QODE.
{"title":"Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications","authors":"Emily Jimin Roh;Hankyul Baek;Donghyeon Kim;Joongheon Kim","doi":"10.1109/TMC.2024.3470328","DOIUrl":"https://doi.org/10.1109/TMC.2024.3470328","url":null,"abstract":"Object detection applications, especially in autonomous driving, have drawn attention due to the advancements in deep learning. Additionally, with continuous improvements in classical convolutional neural networks (CNNs), there has been a notable enhancement in both the efficiency and speed of these applications, making autonomous driving more reliable and effective. However, due to the exponentially rapid growth in the complexity and scale of visual signals used in object detection, there are limitations regarding computation speeds while conducting object detection solely with classical computing. Motivated by this, this paper proposes the quantum object detection engine (QODE), which implements a quantum version of CNN, named QCNN, in object detection. Furthermore, this paper proposes a novel fast quantum convolution algorithm that processes the multi-channel of visual signals based on a small number of qubits and constructs the output channel data, thereby achieving relieved computational complexity. Our QODE, equipped with fast quantum convolution, demonstrates feasibility in object detection with multi-channel data, addressing a limitation of current QCNNs due to the scarcity of qubits in the current era of quantum computing. Moreover, this paper introduces a heterogeneous knowledge distillation training algorithm that enhances the performance of our QODE.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1031-1042"},"PeriodicalIF":7.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938595","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 : 2024-09-27DOI: 10.1109/TMC.2024.3470330
Zeqi Lai;Weisen Liu;Qian Wu;Hewu Li;Jingxi Xu;Yibo Wang;Yuanjie Li;Jun Liu
User-perceived latency is important for the quality of experience (QoE) of wide-area real-time communications (RTC). With the rapid development of low Earth orbit (LEO) mega-constellations, this paper explores a futuristic yet important problem facing the RTC community: can we exploit emerging mega-constellations to facilitate low-latency RTC globally? We carry out our quest in three steps. First, through a measurement study associated with a large number of geo-distributed RTC users, we quantitatively expose that the meandering routes in the client-to-cloud and inter-cloud-site segment of existing cloud-based RTC architecture are critical culprits for the high latency issue suffered by wide-area RTC sessions. Second, we propose SpaceRTC , a satellite-cloud cooperative framework that dynamically selects relay servers upon satellites and cloud sites to build an overlay network which enables diverse close-to-optimal paths. SpaceRTC judiciously allocates RTC flows of different sessions upon the network to facilitate low-latency interactions and adaptively selects bitrates to offer high user-perceived QoE in energy-limited space circumstance. Finally, we implement a testbed based on public constellation information and real-world RTC traces. Extensive experiments demonstrate that SpaceRTC can deliver near-optimal interactive latency, with up to 53.3% average latency reduction and 103.6% average bitrate improvement as compared to other state-of-the-art cloud-based solutions.
{"title":"SpaceRTC: Unleashing the Low-Latency Potential of Mega-Constellations for Wide-Area Real-Time Communications","authors":"Zeqi Lai;Weisen Liu;Qian Wu;Hewu Li;Jingxi Xu;Yibo Wang;Yuanjie Li;Jun Liu","doi":"10.1109/TMC.2024.3470330","DOIUrl":"https://doi.org/10.1109/TMC.2024.3470330","url":null,"abstract":"User-perceived latency is important for the quality of experience (QoE) of wide-area real-time communications (RTC). With the rapid development of low Earth orbit (LEO) mega-constellations, this paper explores a futuristic yet important problem facing the RTC community: \u0000<italic>can we exploit emerging mega-constellations to facilitate low-latency RTC globally?</i>\u0000 We carry out our quest in three steps. First, through a measurement study associated with a large number of geo-distributed RTC users, we quantitatively expose that the \u0000<italic>meandering routes</i>\u0000 in the \u0000<italic>client-to-cloud</i>\u0000 and \u0000<italic>inter-cloud-site</i>\u0000 segment of existing cloud-based RTC architecture are critical culprits for the high latency issue suffered by wide-area RTC sessions. Second, we propose \u0000<sc>SpaceRTC</small>\u0000, a satellite-cloud cooperative framework that dynamically selects \u0000<italic>relay servers</i>\u0000 upon satellites and cloud sites to build an overlay network which enables diverse close-to-optimal paths. \u0000<sc>SpaceRTC</small>\u0000 judiciously allocates RTC flows of different sessions upon the network to facilitate low-latency interactions and adaptively selects bitrates to offer high user-perceived QoE in energy-limited space circumstance. Finally, we implement a testbed based on public constellation information and real-world RTC traces. Extensive experiments demonstrate that \u0000<sc>SpaceRTC</small>\u0000 can deliver near-optimal interactive latency, with up to 53.3% average latency reduction and 103.6% average bitrate improvement as compared to other state-of-the-art cloud-based solutions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"642-661"},"PeriodicalIF":7.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938391","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}
Configuring intelligent surface (IS) or passive antenna array without any channel knowledge, namely blind beamforming, is a frontier research topic in the wireless communication field. Existing methods in the previous literature for blind beamforming include the RFocus and the CSM, the effectiveness of which has been demonstrated on hardware prototypes. However, this paper points out a subtle issue with these blind beamforming algorithms: the RFocus and the CSM may fail to work in the non-line-of-sight (NLoS) channel case. To address this issue, we suggest a grouping strategy that enables adaptive blind beamforming. Specifically, the reflective elements (REs) of the IS are divided into three groups; each group is configured randomly to obtain a dataset of random samples. We then extract the statistical feature of the wireless environment from the random samples, thereby coordinating phase shifts of the IS without channel acquisition. The RE grouping plays a critical role in guaranteeing performance gain in the NLoS case. In particular, if we place all the REs in the same group, the proposed algorithm would reduce to the RFocus and the CSM. We validate the advantage of the proposed blind beamforming algorithm in the real-world networks at 3.5 GHz aside from simulations.
{"title":"Adaptive Blind Beamforming for Intelligent Surface","authors":"Wenhai Lai;Wenyu Wang;Fan Xu;Xin Li;Shaobo Niu;Kaiming Shen","doi":"10.1109/TMC.2024.3468618","DOIUrl":"https://doi.org/10.1109/TMC.2024.3468618","url":null,"abstract":"Configuring intelligent surface (IS) or passive antenna array without any channel knowledge, namely blind beamforming, is a frontier research topic in the wireless communication field. Existing methods in the previous literature for blind beamforming include the RFocus and the CSM, the effectiveness of which has been demonstrated on hardware prototypes. However, this paper points out a subtle issue with these blind beamforming algorithms: the RFocus and the CSM may fail to work in the non-line-of-sight (NLoS) channel case. To address this issue, we suggest a grouping strategy that enables adaptive blind beamforming. Specifically, the reflective elements (REs) of the IS are divided into three groups; each group is configured randomly to obtain a dataset of random samples. We then extract the statistical feature of the wireless environment from the random samples, thereby coordinating phase shifts of the IS without channel acquisition. The RE grouping plays a critical role in guaranteeing performance gain in the NLoS case. In particular, if we place all the REs in the same group, the proposed algorithm would reduce to the RFocus and the CSM. We validate the advantage of the proposed blind beamforming algorithm in the real-world networks at 3.5 GHz aside from simulations.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"907-923"},"PeriodicalIF":7.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938373","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 : 2024-09-26DOI: 10.1109/TMC.2024.3469252
Ouwen Huan;Tao Luo;Mingzhe Chen
In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.
{"title":"Multi-Modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning","authors":"Ouwen Huan;Tao Luo;Mingzhe Chen","doi":"10.1109/TMC.2024.3469252","DOIUrl":"https://doi.org/10.1109/TMC.2024.3469252","url":null,"abstract":"In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"696-708"},"PeriodicalIF":7.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938466","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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