Contract-Based Interference Coordination in Heterogeneous Cloud Radio Access Networks

Peng, Mugen; Xie, Xuemei; Hu, Qiang; Zhang, Jie; Poor, H. Vincent

doi:10.1109/jsac.2015.2416985

Cited by 79 publications

(38 citation statements)

References 31 publications

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“…Considering the C-RAN and MBSs hold an equal position, it is preferable to implement distributed interference coordination. To this end, [129] proposed a contract based interference coordination approach for downlink scenario containing a C-RAN and an MBS. The BBU pool is taken as a principle and the MBS is taken as an agent.…”

Section: ) Game Model Based Optimizationmentioning

confidence: 99%

“…The performance of clustering strategy is always better than no clustering strategy, and lower cluster formation cost leads to better system performance [129] Contract theory Contract theory is applied to interference coordination between the C-RAN and the MBS, and optimal contract design is handled under perfect CSIs as well as imperfect CSIs…”

Section: B Dynamic Crra With Queue-awarenessmentioning

confidence: 99%

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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

Peng

Sun

et al. 2016

IEEE Commun. Surv. Tutorials

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361

205

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Abstract-As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.

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Section: ) Game Model Based Optimizationmentioning

confidence: 99%

Section: B Dynamic Crra With Queue-awarenessmentioning

confidence: 99%

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

Peng

Sun

et al. 2016

IEEE Commun. Surv. Tutorials

Self Cite

361

205

View full text Add to dashboard Cite

show abstract

“…From the key technology perspective of H-CRANs, a couple of works have been done to mainly focus on performance analysis and optimization [2]- [5]. An energy efficiency performance optimization for H-CRANs is analyzed in [3], where a joint optimization solution with resource block assignment and power allocation subject to RRH/HPN association is presented.…”

Section: A Related Workmentioning

confidence: 99%

“…It becomes necessary for the fifth generation (5G) networks to develop revolutionary ways to address this projected capacity demands and improve the quality of user experience. Various innovative network capacityincreasing strategies have been investigated in recent literatures, among which include heterogeneous cloud radio access network (H-CRAN) [2]- [5] and device-to-device (D2D) communications [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Coverage and Rate Analysis in Heterogeneous Cloud Radio Access Networks With Device-to-Device Communication

2016

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The implementation of heterogeneous cloud radio access network (H-CRAN) architecture is faced with practical challenges, such as the capacity and time-delay limitations of the fronthaul links. This paper considers the use of device-to-device (D2D) communication to offload the remote radio heads (RRHs) located in the coverage region of high-power nodes (HPNs). We propose an H-CRAN with non-uniformly deployed D2D communication, in which D2D links are only utilized outside a specified distance from any HPN. Based on the analytical framework provided in this paper, the coverage and the average ergodic rate of a typical user equipment (UE) are characterized. Through defining the exclusion area appropriately, the proposed non-uniform D2D deployment can achieve performance improvement compared with uniform D2D deployment. In addition, to account for the capacity constraint of fronthaul, we characterize the average traffic delivery latency experience by a typical UE when served by RRHs as a quality-of-service metric. Our results show that for a lower fronthaul capacity regime, the proposed non-uniform D2D deployment achieves lower average traffic delivery latency compared with both the uniform D2D deployment and the pure H-CRAN scenarios.INDEX TERMS Heterogenous cloud radio access networks (H-CRANs), device-to-device (D2D), Poisson point process (PPP), coverage probability.

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“…According to contract theory, an incentive mechanism can be realized, which will encourage every employee to consciously choose the contract designed to its service capability. It has been applied into the resource allocation problems for device-to-device communications [31], heterogeneous Long-term Evlution-Advanced (LTE-A) networks [32] and heterogeneous cloud-based radio access networks [33]. The classic contract theory is based on the definition of different employee types, which is just considered as an abstract index without any specific definition in the aforementioned studies.…”

Section: Introductionmentioning

confidence: 99%

Contract Design for Traffic Offloading and Resource Allocation in Heterogeneous Ultra-Dense Networks

Gelenbe

Jiang

et al. 2017

IEEE J. Select. Areas Commun.

128

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Abstract-In heterogeneous ultra-dense networks (HetUDNs), the software-defined wireless network (SDWN) separates resource management from the geo-distributed resources belonging to different service providers. Through a centralized SDWN controller, the operation of the entire network can be managed in a global manner. In this work, we focus on mobile traffic offloading and resource allocation in the SDWN-based HetUDNs, which is constituted of heterogeneous macro base stations (MBSs) and small-cell base stations (SBSs). We explore a scenario where the capacity of the SBSs is accessible, but their expected performance for offloading cannot be known to the SDWN controller, which is the information asymmetric case. To address this asymmetry, a bundle of incentive traffic offloading contracts is designed, to encourage each SBS to select the contract specifically designed to achieve its own maximum net utility. Meanwhile, the MBS utility and social welfare can also be optimized, in comparison to the situation without information asymmetry. To aggregate the characteristics of large numbers of SBSs in HetUDNs, we develop an analytical model that characterizes the SBS type based on different contracts which can be designed with individual rationality and incentive compatibility. This leads to an analytical expression in closed-form for SBS type which we validate mathematically and by simulations, and observe that the monotonicity and incentive compatibility of contracts are ensured. The effectiveness and efficiency of the contract-based traffic offloading mechanism that we design, and the overall system performance are also validated with simulation results.

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Contract-Based Interference Coordination in Heterogeneous Cloud Radio Access Networks

Cited by 79 publications

References 31 publications

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

Coverage and Rate Analysis in Heterogeneous Cloud Radio Access Networks With Device-to-Device Communication

Contract Design for Traffic Offloading and Resource Allocation in Heterogeneous Ultra-Dense Networks

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