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Optimization of C-RAN
Transcript of Optimization of C-RAN
Presented by: Mostafa Mouawad
Supervisor: Prof. Z. Dziong
Challenges in Mobile Internet Era
*Source: Cisco VNI Mobile, 2011
C-RAN Vs Traditional Architectures
Traditional macro base-stations
Architecture of C-RAN
According to the different function splitting between BBU and RRH, there are two kinds of C-RAN solutions:
J. Tang and W. Peng in ‘’ Cross-Layer Resource Allocation in Cloud Radio Access Network’’:
investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in both the BBU pool and the remote radio heads (RRHs), while guaranteeing the cross-layer QoS.
Their Simulation results suggest that their cross-layer approach achieves more energy savings than the recently proposed greedy selection and successive selection algorithms for optimal RRH selection.
Q. Zhang, C. Yang ‘’ Downlink base station cooperative transmission under limited-capacity backhaul’’
They proposed a soft switching strategy between the Joint Processing CoMP and Coordinated Beam forming CoMP modes.
Simulation results shows that their model outperform the single-mode transmission.
Develop an algorithm and architecture to improve the Quality of service based on routing protocol in C-RAN environment.
Optimization of resource allocation in the C-RAN cluster.
Introduce how C-RAN architecture can be used in the context of heterogeneous networks, which mix very small cells (Wi-Fi, LTE,…) and macro-cells.
This model will be based on Markov decision process (MDP) which is a useful tool for studying a wide range of optimization problems.
MDP provides a mathematical framework for modelling decision making in situations where outcomes are partly random and partly under the control of a decision maker.
Apply my suggested algorithm and verify it by simulation using (Opnet/MATLAB )
Analyses and compare MDP model with other algorithms
Potential and industrial benefits of C-RAN
Reduces the cost of base stations deployment and operation.
Increase the capacity.
Increase the flexibility in network upgrades.
Increase of throughput, and decrease of delays for the users.
Reduce amount of hardware by using software defined radio instead which reduces the energy consumption.
A new algorithm is going to be developed for C-RAN downlink to improve the performance and achieve better QOS.
C-RAN has the potential to reduce the network deployment and operation cost.
C-RAN improve system, mobility and coverage performance as well as energy efficiency.
C-RAN will provide today‟s mobile operator with a much more efficient, competitive, and profitable infrastructure.
“Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2012-2017,” Cisco, Tech. Rep., February 2013.
I. Hwang, B. Song, and S. Soliman, “A Holistic View on Hyper-Dense Heterogeneous and Small Cell Networks,” Communications Magazine, IEEE, vol. 51, no. 6, pp. –, 2013.
Y. Lin, L. Shao, Z. Zhu, Q. Wang, and R. K. Sabhikhi, “Wireless network cloud: Architecture and system requirements,” IBM Journal of Research and Development, january-february 2010.
Huawei, “Cloud RAN Introduction. The 4th CJK International Workshop Technology Evolution and Spectrum,” September 2011.
École de technologie supérieure
In the previous work, researchers only focused on the physical layer performance of spectral efficiency and energy efficiency and ignore the bursty traffic an the delay requirement for delay sensitive traffic application in C-RAN.
Therefore we propose a hybrid coordination multipoint model which splits the user data into shared streams and private stream and simultaneously transmitting them to achieve maximum degree of freedom .
Also Route of the data is going to be defined based on the packet sending mode (shared, or public)
A History of electronic devices
2010: Apple iPad
2000s: Ericsson R380; First smartphone