Joint Grouping and Offloading in NOMA-Assisted Multi-MEC IoVT Systems

Yang, Xinyu; Lu, Hancheng; Guo, Fengqian; Wang, Yazheng; Kong, Chani; Lu, Qiaojia

doi:10.1109/globecom48099.2022.10001213

“…Channel state information (CSI) can be obtained through emerging deep learning-based channel estimation methods [26], [27]. Similar to [13], [16], [21], [28], we assume all the IoVT devices and MEC-BSs can obtain the perfect channel state information (CSI) in advance 1 . Let x n , p n and y mg denote the signal transmitted from IoVT device n, transmit power of IoVT device n and the signal received by MEC-BS m on subchannel g, respectively.…”

Section: B Transmission Modelmentioning

confidence: 99%

“…By deploying servers in wireless access networks to users, mobile edge computing (MEC) can offer IoVT devices robust computing, storage, networking, and communication capabilities [10]. A few studies have been done on resource allocation optimization in IoVT with MEC [2], [11]- [13], where IoVT devices offload visual processing tasks to edge servers to obtain better quality of service (QoS). The research topics, proposed solutions, distinctive aspects, and shortcomings of all highly relevant works are summarized in Table I.…”

Section: Introductionmentioning

confidence: 99%

Joint Transmission and Resource Optimization in NOMA-Assisted IoVT With Mobile Edge Computing

Qin,

Lu,

Chen

et al. 2024

IEEE Trans. Veh. Technol.

2

0

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“…γ mg n is a random variable modeling the small-scale fading between IoVT deveice n and MEC-BS m on subchannel g. Without loss of generality, we assume that the IoVT devices have been ordered by their channel gains in ascending. And all the IoVT devices and MEC-BSs can obtain the perfect channel state information (CSI) in advance [14], [25]. Let x n , p n and y mg denote the signal transmitted from IoVT device n, transmit power of IoVT device n and the signal received by MEC-BS m on subchannel g, respectively.…”

Section: B Transmission Modelmentioning

confidence: 99%

“…By deploying servers in wireless access networks to users, mobile edge computing (MEC) can offer IoVT devices with robust computing, storage, networking, and communication capabilities [10]. A few studies have been done on resource allocation optimization in IoVT with MEC [11]- [14], where IoVT devices offload visual processing tasks to edge servers to obtain better quality of service (QoS).…”

Section: Introductionmentioning

confidence: 99%

Joint Transmission and Resource Optimization in NOMA-assisted IoVT with Mobile Edge Computing

QIN¹

2023

Preprint

0

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<p>Internet of Video Things (IoVT) brings much higher requirements on the transmission and computing capabilities of wireless networks than traditional Internet of Things (IoT). Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been considered as two promising technologies to satisfy these requirements. However, successive interference cancellation (SIC) and grouping operations in NOMA as well as delay sensitive IoVT video tasks with different priorities make it challenging to achieve the optimal performance in NOMA-assisted IoVT with MEC. To address this issue, we formulate a joint optimization problem where both NOMA operations and MEC offloading are involved, with the goal to minimize the weighted average total delay. To tackle such intractable problem, we proposed a graph theory-based optimization framework, then decompose and transform the problem into finding \emph{negative} loops in the weighted directed graph. Specifically, we design a priority-based SIC decoding mechanism and propose convex optimization-based power allocation and computing resource allocation algorithms to calculate the adjacency matrix. Then, two negative loop searching algorithms are adopted to obtain the device association and grouping strategies. Simulation results demonstrate that compared with existing algorithms, the proposed algorithm reduces the weighted average total delay by up to 92.43% as well as improves the transmission rate of IoVT devices by up to 79.1%.</p>

show abstract

“…Channel state information (CSI) can be obtained through emerging deep learning-based channel estimation methods [26], [27]. Similar to [13], [16], [21], [28], we assume all the IoVT devices and MEC-BSs can obtain the perfect channel state information (CSI) in advance 1 . Let x n , p n and y mg denote the signal transmitted from IoVT device n, transmit power of IoVT device n and the signal received by MEC-BS m on subchannel g, respectively.…”

Section: B Transmission Modelmentioning

confidence: 99%

Joint Transmission and Resource Optimization in NOMA-assisted IoVT with Mobile Edge Computing

QIN

2023

Preprint

0

View full text Add to dashboard Cite

<p>Internet of Video Things (IoVT) brings much higher requirements on the transmission and computing capabilities of wireless networks than traditional Internet of Things (IoT). Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been considered as two promising technologies to satisfy these requirements. However, successive interference cancellation (SIC) and grouping operations in NOMA as well as delay sensitive IoVT video tasks with different priorities make it challenging to achieve the optimal performance in NOMA-assisted IoVT with MEC. To address this issue, we formulate a joint optimization problem where both NOMA operations and MEC offloading are involved, with the goal to minimize the weighted average total delay. To tackle such intractable problem, we proposed a graph theory-based optimization framework, then decompose and transform the problem into finding \emph{negative} loops in the weighted directed graph. Specifically, we design a priority-based SIC decoding mechanism and propose convex optimization-based power allocation and computing resource allocation algorithms to calculate the adjacency matrix. Then, two negative loop searching algorithms are adopted to obtain the device association and grouping strategies. Simulation results demonstrate that compared with existing algorithms, the proposed algorithm reduces the weighted average total delay by up to 92.43% as well as improves the transmission rate of IoVT devices by up to 79.1%.</p>

show abstract

Joint Grouping and Offloading in NOMA-Assisted Multi-MEC IoVT Systems

Cited by 7 publications

References 15 publications

Joint Transmission and Resource Optimization in NOMA-Assisted IoVT With Mobile Edge Computing

Joint Transmission and Resource Optimization in NOMA-Assisted IoVT With Mobile Edge Computing

Joint Transmission and Resource Optimization in NOMA-assisted IoVT with Mobile Edge Computing

Joint Transmission and Resource Optimization in NOMA-assisted IoVT with Mobile Edge Computing

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