Cluster Content Caching: An Energy-Efficient Approach to Improve Quality of Service in Cloud Radio Access Networks

In cloud radio access networks (C-RANs), a substantial amount of data must be exchanged in both backhaul and fronthaul links, which causes high power consumption and poor quality of service (QoS) experience for real-time services. To solve this problem, a cluster content caching structure is proposed in this paper, which takes full advantages of distributed caching and centralized signal processing. In particular, redundant traffic on the backhaul can be reduced because the cluster content cache provides a part of required content objects for remote radio heads (RRHs) connected to a common edge cloud. Tractable expressions for both effective capacity and energy efficiency performance are derived, which show that the proposed structure can improve QoS guarantees with a lower cost of local storage. Furthermore, to fully explore the potential of the proposed cluster content caching structure, the joint design of resource allocation and RRH association is optimized, and two distributed algorithms are accordingly proposed. Simulation results verify the accuracy of the analytical results and show the performance gains achieved by cluster content caching in C-RANs.

[1]  Caijun Zhong,et al.  Effective Capacity of Correlated MISO Channels , 2011, 2011 IEEE International Conference on Communications (ICC).

[2]  Wei Yu,et al.  Optimized Backhaul Compression for Uplink Cloud Radio Access Network , 2013, IEEE Journal on Selected Areas in Communications.

[3]  Vincent K. N. Lau,et al.  Recent Advances in Underlay Heterogeneous Networks: Interference Control, Resource Allocation, and Self-Organization , 2015, IEEE Communications Surveys & Tutorials.

[4]  Mugen Peng,et al.  Fog-computing-based radio access networks: issues and challenges , 2015, IEEE Network.

[5]  Deli Qiao,et al.  Effective Capacity of Two-Hop Wireless Communication Systems , 2013, IEEE Transactions on Information Theory.

[6]  Sarit Kraus,et al.  Coalition Formation Among Autonomous Agents: Strategies and Complexity , 1993, MAAMAW.

[7]  Yang Li,et al.  Coordinated caching model for minimizing energy consumption in radio access network , 2014, 2014 IEEE International Conference on Communications (ICC).

[8]  H. Vincent Poor,et al.  Cluster formation in cloud-radio access networks: Performance analysis and algorithms design , 2015, 2015 IEEE International Conference on Communications (ICC).

[9]  Yuan Li,et al.  Heterogeneous cloud radio access networks: a new perspective for enhancing spectral and energy efficiencies , 2014, IEEE Wireless Communications.

[10]  Jeffrey G. Andrews,et al.  Modeling and Analysis of K-Tier Downlink Heterogeneous Cellular Networks , 2011, IEEE Journal on Selected Areas in Communications.

[11]  Muhammad Ali Imran,et al.  Expanding cellular coverage via cell-edge deployment in heterogeneous networks: spectral efficiency and backhaul power consumption perspectives , 2014, IEEE Communications Magazine.

[12]  Zhu Han,et al.  Coalitional game theory for communication networks , 2009, IEEE Signal Processing Magazine.

[13]  H. Vincent Poor,et al.  Fronthaul-constrained cloud radio access networks: insights and challenges , 2015, IEEE Wireless Communications.

[14]  H. Vincent Poor,et al.  Inter-Tier Interference Suppression in Heterogeneous Cloud Radio Access Networks , 2015, IEEE Access.

[15]  Shlomo Shamai,et al.  Distributed MIMO Receiver—Achievable Rates and Upper Bounds , 2007, IEEE Transactions on Information Theory.

[16]  Yong Li,et al.  System architecture and key technologies for 5G heterogeneous cloud radio access networks , 2015, IEEE Netw..

[17]  Xinbing Wang,et al.  On content-centric wireless delivery networks , 2014, IEEE Wireless Communications.

[18]  Mehdi Bennis,et al.  Living on the edge: The role of proactive caching in 5G wireless networks , 2014, IEEE Communications Magazine.

[19]  Shlomo Shamai,et al.  Fronthaul Compression for Cloud Radio Access Networks: Signal processing advances inspired by network information theory , 2014, IEEE Signal Processing Magazine.

[20]  Sarit Kraus,et al.  Easy and hard coalition resource game formation problems: a parameterized complexity analysis , 2009, AAMAS.

[21]  Zhu Han,et al.  Hedonic Coalition Formation Games for Secondary Base Station Cooperation in Cognitive Radio Networks , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[22]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[23]  Dapeng Wu,et al.  Effective capacity-based quality of service measures for wireless networks , 2004, First International Conference on Broadband Networks.

[24]  Muhammad Ali Imran,et al.  How much energy is needed to run a wireless network? , 2011, IEEE Wireless Communications.

[25]  Nicholas R. Jennings,et al.  A linear approximation method for the Shapley value , 2008, Artif. Intell..

[26]  Mehdi Bennis,et al.  Cache-enabled small cell networks: modeling and tradeoffs , 2014, EURASIP Journal on Wireless Communications and Networking.

[27]  A. Hospodor Hit ratio of caching disk buffers , 1992, Digest of Papers COMPCON Spring 1992.

[28]  Uichin Lee,et al.  Greening the internet with content-centric networking , 2010, e-Energy.

[29]  R. Gallager Principles of Digital Communication , 2008 .

[30]  Dapeng Wu,et al.  Effective capacity: a wireless link model for support of quality of service , 2003, IEEE Trans. Wirel. Commun..

[31]  Xiaofei Wang,et al.  Cache in the air: exploiting content caching and delivery techniques for 5G systems , 2014, IEEE Communications Magazine.

[32]  Gerhard Fettweis,et al.  Are Heterogeneous Cloud-Based Radio Access Networks Cost Effective? , 2015, IEEE Journal on Selected Areas in Communications.