FluidNet: A Flexible Cloud-Based Radio Access Network for Small Cells

Sundaresan, Karthikeyan; Arslan, Mustafa Y.; Singh, Shailendra; Rangarajan, Sampath; Krishnamurthy, Srikanth V.

doi:10.1109/tnet.2015.2419979

Cited by 117 publications

(94 citation statements)

References 12 publications

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“…As shown in Fig. 4, these academical works can be classified as the aspects of flexible network configurations [24]- [26], the involvement of promising techniques like massive MIMO [27] and CR [28], the convergence of different RANs [29] [30], as well as the utilization of edge computing and storing capabilities [31].…”

Section: C-ran Architectures Proposed By the Academiamentioning

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

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: C-ran Architectures Proposed By the Academiamentioning

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

361

205

View full text Add to dashboard Cite

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“…In the literature, several mechanisms [12,13,16] basically focus on infrastructure development of CRAN architecture. Another noteworthy paper [6] addresses QoS-aware scheduling and based on the service provider profit [9] allocates resources for incoming requests.…”

Section: Related Workmentioning

confidence: 99%

“…However, our proposed system makes a tradeoff between user QoE as well as service provider profit while utilizing resources efficiently. [12] (Partially) VRAN-PAP [13] (Partially) Fluidnet [16] RRH Clustering [15] QoSM [6] NvG [9] QEPRA [Proposed] …”

Section: Related Workmentioning

confidence: 99%

Tradeoff between User Quality-Of-Experience and Service Provider Profit in 5G Cloud Radio Access Network

et al. 2017

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In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.

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“…4 Note that P (CCS) k,max depends on specific requirements and deployment in the practical network. It is further noted that …”

Section: B Optimal Number Of Bssmentioning

confidence: 99%

Optimising energy efficiency of non‐orthogonal multiple access for wireless backhaul in heterogeneous cloud radio access network

et al. 2016

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This paper studies the downlink problem of a cloud-based central station (CCS) to multiple base stations (BSs) in a heterogeneous cellular network sharing the same time and frequency resources. We adopt non-orthogonal multiple access (NOMA) and propose power allocation for the wireless downlink in the heterogeneous cloud radio access network (HCRAN). Taking into account practical channel modelling with power consumptions at BSs of different cell types (e.g. macro-cell, micro-cell, etc.) and backhauling power, we analyse the energy efficiency (EE) of the practical HCRAN utilising NOMA.Simulation results indicate that the proposed NOMA for the HCRAN outperforms the conventional orthogonal frequency division multiple access (OFDMA) scheme in terms of providing higher EE of up to four times. Interestingly, the results reveal a fact that the EE of the NOMA approach is not always an increasing function of the number of BSs but varies as a quasiconcave function. This motivates us to further introduce an optimisation problem to find the optimal number of BSs that maximises the EE of the HCRAN. It is shown that, with a low power supply at the CCS, a double number of micro BSs can be served by HCRAN providing an improved EE of up to 1.6 times compared to the macro BSs and RRHs, while they achieve the same EE performance with high-power CCS. Index TermsHeterogeneous networks; cloud radio access networks; wireless downlink; non-orthogonal multiple access; energy efficiency.

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FluidNet: A Flexible Cloud-Based Radio Access Network for Small Cells

Cited by 117 publications

References 12 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

Tradeoff between User Quality-Of-Experience and Service Provider Profit in 5G Cloud Radio Access Network

Optimising energy efficiency of non‐orthogonal multiple access for wireless backhaul in heterogeneous cloud radio access network

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