An Iterative Matching-Stackelberg Game Model for Channel-Power Allocation in D2D Underlaid Cellular Networks

Yuan, Yiling; Yang, Tao; Feng, Hui; Hu, Bo

doi:10.1109/twc.2018.2867474

Cited by 67 publications

(38 citation statements)

References 34 publications

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“…1. Taking m 1 as an example, the data queue Q 1 (t), the virtual energy deficit queue N 1 (t), and the virtual service reliability deficit queue F 1 (t) are dynamically updated at each slot based on (1) and (15). Comparing m 1 and m 2 , it is noted that the data backlog and the service reliability deficit of m 1 are larger while the energy deficit of m 2 is larger.…”

Section: A Problem Transformationmentioning

confidence: 99%

“…13: Update U k (t) and Y k (t + 1) based on (4) and (6). 14: Update Q k (t + 1), N k (t + 1), and F k (t + 1) as (1), (15). 15…”

Section: A the Seb-mgsi Algorithm For The Ideal Casementioning

confidence: 99%

“…Matching theory provides a flexible, l ow-complexity, and efficient t ool t o s olve t he c ombinatorial p roblem s uch as channel selection [15], task selection [16], and server selection [17]. However, it requires perfect knowledge of global state information (GSI) to construct the preference list, which specifies the fundamental matching criteria [18].…”

Section: Introductionmentioning

confidence: 99%

“…Based on z k,j,t , U k (t) and Y k (t+1) can be calculated based on (4) and (6). Next, the three queues, i.e., Q k (t+1), N k (t+1), and F k (t + 1), are updated as (1), (15).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

Liao

Zhou

Zhao

et al. 2020

IEEE Internet Things J.

242

104

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Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resourcelimited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this paper, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to longterm constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

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Section: A Problem Transformationmentioning

confidence: 99%

“…13: Update U k (t) and Y k (t + 1) based on (4) and (6). 14: Update Q k (t + 1), N k (t + 1), and F k (t + 1) as (1), (15). 15…”

Section: A the Seb-mgsi Algorithm For The Ideal Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Based on z k,j,t , U k (t) and Y k (t+1) can be calculated based on (4) and (6). Next, the three queues, i.e., Q k (t+1), N k (t+1), and F k (t + 1), are updated as (1), (15).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

Liao

Zhou

Zhao

et al. 2020

IEEE Internet Things J.

242

104

View full text Add to dashboard Cite

show abstract

“…where V [v ij ], and k 1 and k 2 are constants. Notice that, the channel allocation problem can not be directly decomposed into disjoint subproblems for each D2D pair, due to the quadratic term in (21). Nevertheless, the gradient is linear in B, thus, the descent part of the channel allocation algorithm can be directly decomposed and each D2D pair can perform an optimization step over its corresponding part, without loss of optimality.…”

Section: Decentralized Algorithmsmentioning

confidence: 99%

Underlay Device-to-Device Communications on Multiple Channels

Elnourani

Hamid

Romero

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-stateinformation (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentralized setting, the computational load is distributed among the D2D pairs and the base station, keeping also a low communication overhead. Moreover, we also provide a theoretical convergence analysis, including also the rate of convergence to stationary points. The proposed algorithms have been experimentally tested in a simulation environment, showing their favorable performance, as compared with the state-of-the-art alternatives.

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Multicast Protocols forD2D

Bhardwaj

Agnihotri

2020

Wiley 5G Ref

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Supporting ever‐increasing number of mobile users with data‐hungry applications, running on battery‐limited devices, is a daunting challenge for the telecommunication community. Device‐to‐device (D2D) communication, which allows physically proximate mobile users to directly communicate with each other by reusing the spectrum, without going through the base station, holds promise to help us tackle this challenge. In a cellular network, D2D communication offers opportunities for spectrum reuse and spatial diversity that may lead to enhanced coverage, higher throughput, and robust communication in the network. Further, for applications such as weather forecasting and live streaming, which may require the same chunks of data distributed to geographically proximate users, D2D multicasting may provide better utilization of network resources compared to D2D unicast or the base station‐based multicast, such as LTE eMBMS. However, extensive deployment of D2D multicast in a network may introduce various issues, such as severe co‐channel interference due to spectrum reuse, underutilization of spectrum, and rapid battery depletion of the multicasting D2D nodes due to higher transmit power to mitigate co‐channel interference and facilitating data‐relaying. Therefore, this article discusses some of the challenges in supporting D2D multicast communication in cellular networks. It then surveys various existing approaches to address these challenges, which along with some future research directions may help us develop practical D2D multicast protocols for cellular networks.

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An Iterative Matching-Stackelberg Game Model for Channel-Power Allocation in D2D Underlaid Cellular Networks

Cited by 67 publications

References 34 publications

Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

Underlay Device-to-Device Communications on Multiple Channels

Multicast Protocols forD2D

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