Optimal Fairness-Aware Resource Supply and Demand Management for Mobile Edge Computing

Guo, Chongtao; He, Wei; Li, Geoffrey Ye

doi:10.1109/lwc.2020.3046023

Cited by 16 publications

(13 citation statements)

References 17 publications

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“…As a departure from prior work, our approach considers the dynamic eICIC configuration problem of non-linear EH SWIPT and crosstier interference for UL and DL in dense IoT HetNets. For future, our work can be extend with NOMA Network with imperfect CSI [24], intelligent reflecting surface-aided coordinated multipoint [25] and the mobile edge computing with fairness [26] in dense IoT HetNets. The allocated resource for ABS from macrocell m on the UL…”

Section: Related Workmentioning

confidence: 99%

eICIC Configuration of Downlink and Uplink Decoupling With SWIPT in 5G Dense IoT HetNets

Zheng

Gao

Zhang

et al. 2021

IEEE Trans. Wireless Commun.

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Interference management and power transfer can provide a significant improvement over the 5th generation mobile networks (5G) dense Internet of Things (IoT) heterogeneous networks (HetNets). In this paper, we present a novel approach to simultaneously manage inferences at the downlink (DL) and uplink (UL), and to identify opportunities for power transfer and additional UL transmissions integrated with existing protocols and infrastructures for enhanced inter-cell interference coordination (eICIC) protocol in dense IoT HetNets, while considering practical non-linear energy harvesting (EH) model. The design is formulated as the joint optimization of interference aware UL/DL decoupling, airtime resource allocation and energy transfer. The key insight of our algorithm is to translate the original, intractable joint-optimization problem into a problem space where a good approximate solution can be quickly found. We evaluate our scheme through theoretical analysis and simulation. The evaluation shows that our approach improves the system utility by over 20% compared to start-of-the-art in dense IoT HetNets. Compared to alternative schemes, our approach maintains the best user fairness and rate experience and can solve the problem in a fast and scalable way.Index Terms-Downlink/Uplink decoupling (DUDe), Enhanced inter-cell interference coordination (eICIC), Simultaneous wireless information and power transfer (SWIPT), Internet of Things (IoT), heterogeneous network (HetNet).

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Section: Related Workmentioning

confidence: 99%

eICIC Configuration of Downlink and Uplink Decoupling With SWIPT in 5G Dense IoT HetNets

Zheng

Gao

Zhang

et al. 2021

IEEE Trans. Wireless Commun.

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“…High-speed data services with high quality of service (QoS) are responding to expanding demand by end-users, leading to many challenges in establishing reliable services in current 3G/4G wireless communication systems [1]. Since the spectrum has become a valuable resource for communication applications, it has also become essential to use the spectrum efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…Since the spectrum has become a valuable resource for communication applications, it has also become essential to use the spectrum efficiently. However, McHenry et al [1] report an extremely low efficiency for spectrum use on the geographic and temporal RF spectrum, and for that reason, the demand for good use of RF spectrum has increased and motivated researchers to find the best solutions for this problem. A promising approach to address the inefficient spectrum use is the cognitive radio (CR) [2][3][4][5][6][7][8][9], an attractive and novel communications technology that can be used to enhance the scarce natural resources by efficiently using the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

et al. 2021

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The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.

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“…A lot of works have been devoted to the research of computation offloading. Most of them have only focused on the process of offloading computing tasks from UE to MEC [10][11][12][13][14][15][16][17][18]. Only the optimal offloading decision is considered in [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Only the optimal offloading decision is considered in [10,11]. Researchers only focused on optimizing the communication resources [12,13] or the computing resources [14,15]. In some works, the combination of optimizing offloading decisions and resource allocation is used to minimize the latency or enhance the system performance [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing

Gan

Chen

et al. 2021

Wireless Communications and Mobile Computing

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Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.

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Optimal Fairness-Aware Resource Supply and Demand Management for Mobile Edge Computing

Cited by 16 publications

References 17 publications

eICIC Configuration of Downlink and Uplink Decoupling With SWIPT in 5G Dense IoT HetNets

eICIC Configuration of Downlink and Uplink Decoupling With SWIPT in 5G Dense IoT HetNets

Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing

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