Future Evolution in Wireless Network Architectures: Towards a 'Cloud of Antennas'

Webb, Matthew; Li, Zhaojun; Bucknell, Paul; Moulsley, Timothy; Vadgama, Sunil

doi:10.1109/vtcfall.2012.6399319

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…This increases interference, overloads the network and causes power wastage. In centralized CRAN architecture broadcast and synchronization information can be schedule among the RRHs belong to same geographical area [38], thereby reducing transmission of replicated information. Thus smart self-constructing network will help to reduce unnecessary signaling in the network.…”

Section: Efficient Fronthaul Solutionmentioning

confidence: 99%

Survey on Cloud Radio Access Network

Chhatani¹,

Cheeran²

2016

EAI Endorsed Transactions on Wireless Spectrum

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The existing wireless network will face the challenge of data tsunami in the near future. Densification of network will deal huge data traffic but will increase the interferences and network cost. At the same time, the existing wireless network is underutilized due to dynamic traffic. To deal with this adverse scenario, a change in the current network architecture is required. Based on virtualization, Cloud Radio Access Network (CRAN) was proposed for wireless network. In CRAN the functionality of base station will be distributed into base band unit (BBU) and remote radio heads (RRH) which will achieve benefits of centralization. This paper presents a survey on CRAN centring on optimized resource allocation, energy efficiency and throughput maximization under fronthaul capacity. The existing solution and future opportunities in CRAN are also summarized.

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Section: Efficient Fronthaul Solutionmentioning

confidence: 99%

Survey on Cloud Radio Access Network

Chhatani¹,

Cheeran²

2016

EAI Endorsed Transactions on Wireless Spectrum

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show abstract

“…Then, all virtual BSs were clustered as a radio cloud, which could be used to provide centralized baseband processing. The RRH, on the other hand, allowed the radio head and antenna to be decoupled from the BS, resulting in significant improvements of flexibility in network deployment, coordination among highly dense remote antennas and overall power saving [15]. Furthermore, the cloud computing provided many stunning functions, for example virtualization, parallelism, dynamic resource allocation, signal interference management, etc.…”

Section: Evolution Of H-cranmentioning

confidence: 99%

“…In order to address the above challenges, a novel concept of Cloud-based Radio Access Network (C-RAN) [14] evolves as a promising wireless communication system to expedite mobile big data computing, which combines mobile computing with big data. Although C-RAN emerges in recent years, it is already considered as a feasible solution to design and deploy future wireless networks [4][5] [15]. Moreover, C-RAN is also beneficial to address the challenges on wireless heterogeneous networks, like the heterogeneous network [7], which is able to make offloading decision at mobile devices and perform admission control/cell association/QoS guarantee in cloud.…”

Section: Introductionmentioning

confidence: 99%

Sh-Cran

Liu

Ngai

et al. 2015

Proceedings of the 2015 Workshop on Mobile Big Data

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The heterogeneous cloud radio access network (H-CRAN) has been emerging as a cost-effective solution supporting huge volumes of mobile traffic in the big data era. This paper investigates potential security challenges on H-CRAN and analyzes their likelihoods and difficulty levels. Typically, the security threats in H-CRAN can be categorized into three groups, i.e., security threats towards remote radio heads (RRHs), those towards the radio cloud infrastructure and towards backhaul networks. To overcome challenges made by the security threats, we propose a hierarchical security framework called Secure H-CRAN (SH-CRAN) to protect the H-CRAN system against the potential threats. Specifically, the architecture of SH-CRAN contains three logically independent secure domains (SDs), which are the SDs of radio cloud infrastructure, RRHs and backhauls. The notable merits of SH-CRAN include two aspects: (i) the proposed framework is able to provide security assurance for the evolving H-CRAN system, and (ii) the impacts of any failure are limited in one specific component of H-CRAN. The proposed SH-CRAN can be regarded as the basis of the future security mechanisms of mobile bag data computing.

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“…Another advantage is that an antenna (with an analog front-end) may be provided at a location where it may be difficult to put an entire base station. Yet another advantage is that C-RAN allow collaborative algorithms among base stations such as coordinated multipoint transmission techniques, specified by the LTE-advanced standard [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

Baltiiski

Iliev

Kehaiov

et al. 2015

Wireless Pers Commun

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Spectrum monitoring is important for efficient spectrum sharing and resource management in cloud-based radio access networks (C-RAN). In this paper we show how data obtained from long-term spectrum monitoring together with machine learning (ML) operating on big data (BD) can be used in a C-RAN scenario for spectrum management purposes. We propose an approach for spectrum occupancy forecasting which can be used to reduce the delay in making dynamic spectrum allocation decisions and improve the cognitive and management functionalities of cloud-based architectures such as C-RAN. The spectrum occupancy and usage activity in a predefined frequency band is based on the statistical processing of a large amount of collected data and the introduction of a frequency-time resources indicator as a measure of spectrum usage. Furthermore, we apply ML algorithms to predict spectrum usage and compare the predicted with actual measured data. Taking into consideration that the accuracy of the prediction depends on the volume of collected data and the time of prediction on the BD and ML approach, we propose the development of a cloud-based generic processing architecture to solve the ''accuracy versus latency'' trade-off problem. The proposed architecture is appropriate for deployment in cognitive C-RAN.

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Future Evolution in Wireless Network Architectures: Towards a 'Cloud of Antennas'

Cited by 10 publications

References 3 publications

Survey on Cloud Radio Access Network

Survey on Cloud Radio Access Network

Sh-Cran

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

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