Matching Theory Assisted Resource Allocation in Millimeter Wave Ultra Dense Small Cell Networks

Zhao, Zhongling; Shi, Jia; Li, Zan; Yang, Long; Zhao, Yue; Liang, Wei

doi:10.1109/icc.2019.8761759

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…As for constraints, ( 13) and ( 14) constrain that each CP-UE needs to complete the offloading task within one TS, and each TS needs to be allocated one CP-UE. Further, (15) means each CP-UE i should complete the offloading task within its tolerable delay D i . In (16), the instantaneous rate of CM-UEs must be larger than R 0 .…”

Section: A Original Problem Formulationmentioning

confidence: 99%

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Multiobjective Resource Allocation for mmWave MEC Offloading Under Competition of Communication and Computing Tasks

Zhao

Shi

et al. 2022

IEEE Internet Things J.

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Towards 6G networks, such as virtual reality (VR) applications, Industry 4.0 and automated driving, demand mobile edge computing (MEC) techniques to offload computing tasks to nearby servers, which however causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, so that enabling low latency and high throughput. For this sake, this paper investigates the resource management for the offload transmission of mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computingoriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation and power allocation is formulated as a multi-objective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using ϵ-constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that, the gap between Pareto front generated by three-stage iterative resource allocation algorithm and the real Pareto front less than 0.16%. Futher, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.

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Section: A Original Problem Formulationmentioning

confidence: 99%

“…As a promising technology, mmWave has recently attracted large research interest because of the huge resources of available bandwidth, which can exponentially increase the transmission ability of MEC offloading [12][13][14][15], which is now become an important research trend for the B5G and 6G era.…”

Section: Introductionmentioning

confidence: 99%