Device-to-device-based heterogeneous radio access network architecture for mobile cloud computing

Jo, Minho; Maksymyuk, Taras; Strykhalyuk, Bohdan

doi:10.1109/mwc.2015.7143326

Cited by 116 publications

(77 citation statements)

References 13 publications

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“…In [10], based on channel sensing results of users, a game-theoretic distributed power control mechanism is proposed. Besides, there are other studies about spectrum sensing [17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Zhai

Wang²

2017

Wireless Communications and Mobile Computing

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Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

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Section: Related Workmentioning

confidence: 99%

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Zhai

Wang²

2017

Wireless Communications and Mobile Computing

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“…The cloud-based architecture allows to improve the spectral and energy efficiency of the mobile network. In C-RAN, the conventional cellular base station is decoupled into two entities: baseband processing unit (BBU) and remote radio head (RRH) [13]. The responsibilities of BBU are to provide necessary signal processing, functionalities of physical, MAC, and network layer, i.e.…”

Section: Analysis Of Cell Traffic and Backhaul Capacity Demandmentioning

confidence: 99%

Designing the new Backhaul for 5G Heterogeneous Network Based on Converged Optical Infrastructure

Maksymyuk¹,

Krasko²,

Kyryk³

et al. 2017

AEI

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In this paper, a new flexible backhaul design is proposed based on the time-frequency resource grid. The advantage of proposed approach is in the fine granularity bandwidth allocation. Proposed approach allows tunneling of Cloud-RAN modulated radio signals between baseband processing unit and remote radio head within the same resource blocks. In addition, new handover machanism has been introduced to reduce the handover overhead in the backhaul network. The main idea of the proposed handover mechanism is in multicast data transmission to both involved eNodeBs by joint assignment of the resource elements for multiple cells during handover that allows to decrease the amount of backhaul traffic over X2 interface by 20%.

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“…It should be noted that when mobile users are far from the target server, direct communication may not ensure the communication consistency and user QoS may suffer. Motivated by the recent work [22] for mobile computing where master device is chosen to offload the computation tasks of mobile users via D2D communication in HetNet, in this paper, we provide incentive for idle SUEs within small cells to act as relays by offloading computation tasks of MUEs to small cell base stations (SCBs), i.e. the edge servers.…”

Section: Introductionmentioning

confidence: 99%

TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in HetNet

Chen

Zhang

et al. 2019

IEEE Trans. Serv. Comput.

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I. ABSTRACTIn heterogeneous cellular network, task scheduling for computation offloading is one of the biggest challenges. Most works focus on alleviating heavy burden of macro base stations by moving the computation tasks on macro-cell user equipment (MUE) to remote cloud or small-cell base stations. But the selfishness of network users is seldom considered. Motivated by the cloud edge computing, this paper provides incentive for task transfer from macro cell users to small cell base stations. The proposed incentive scheme utilizes small cell user equipment to provide relay service. The problem of computation offloading is modelled as a two-stage auction, in which the remote MUEs with common social character can form a group and then buy the computation resource of small-cell base stations with the relay of small cell user equipment. A two-stage auction scheme named TARCO is contributed to maximize utilities for both sellers and buyers in the network. The truthful, individual rationality and budget balance of the TARCO are also proved in this paper. In addition, two algorithms are proposed to further refine TARCO on the social welfare of the network. Extensive simulation results demonstrate that, TARCO is better than random algorithm by about 104.90% in terms of average utility of MUEs, while the performance of TARCO is further improved up to 28.75% and 17.06% by the proposed two algorithms, respectively.

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Device-to-device-based heterogeneous radio access network architecture for mobile cloud computing

Cited by 116 publications

References 13 publications

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Designing the new Backhaul for 5G Heterogeneous Network Based on Converged Optical Infrastructure

TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in HetNet

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