QoS-Aware Dynamic RRH Allocation in a Self-Optimized Cloud Radio Access Network With RRH Proximity Constraint

An inefficient utilization of network resources in a time-varying traffic environment often leads to load imbalances, high call-blocking events and degraded quality of service (QoS). This paper optimizes the QoS of a cloud radio access network (C-RAN) by investigating load balancing solutions. The dynamic re-mapping ability of C-RAN is exploited to configure the remote radio heads (RRHs) to proper base band unit sectors in a time-varying traffic environment. RRH-sector configuration redistributes the network capacity over a given geographical area. A self-optimized cloud radio access network (SOCRAN) is considered to enhance the network QoS by traffic load balancing with minimum possible handovers in the network. QoS is formulated as an optimization problem by defining it as a weighted combination of new key performance indicators for the number of blocked users and handovers in the network subject to RRH sectorization constraint. A genetic algorithm (GA) and discrete particle swarm optimization (DPSO) are proposed as evolutionary algorithms to solve the optimization problem. Computational results based on three benchmark problems demonstrate that GA and DPSO deliver optimum performance for small networks, whereas close-optimum is delivered for large networks. The results of both GA and DPSO are compared to exhaustive search and $K$ -mean clustering algorithms. The percentage of blocked users in a medium sized network scenario is reduced from 10.523% to 0.421% and 0.409% by GA and DPSO, respectively. Also in a vast network scenario, the blocked users are reduced from 5.394% to 0.611% and 0.56% by GA and DPSO, respectively. The DPSO outperforms GA regarding execution, convergence, complexity, and achieving higher levels of QoS with fewer iterations to minimize both handovers and blocked users. Furthermore, a tradeoff between two critical parameters for the SOCRAN algorithm is presented, to achieve performance benefits based on the type of hardware utilized for C-RAN.

[1]  Wuyang Zhou,et al.  On Joint BBU/RRH Resource Allocation in Heterogeneous Cloud-RANs , 2017, IEEE Internet of Things Journal.

[2]  Ming Zhu,et al.  A Novel Multi-Service Small-Cell Cloud Radio Access Network for Mobile Backhaul and Computing Based on Radio-Over-Fiber Technologies , 2013, Journal of Lightwave Technology.

[3]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .

[4]  Chintha Tellambura,et al.  Multichannel Analysis of Cell Range Expansion and Resource Partitioning in Two-Tier Heterogeneous Cellular Networks , 2016, IEEE Transactions on Wireless Communications.

[5]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[6]  Dario Pompili,et al.  Elastic resource utilization framework for high capacity and energy efficiency in cloud RAN , 2016, IEEE Communications Magazine.

[7]  Ramona Trestian,et al.  5G RADIO ACCESS NETWORKS: CENTRALIZED RAN, CLOUD-RAN AND VIRTUALIZATION OF SMALL CELLS , 2019 .

[8]  Jun Chen,et al.  A conflict avoidance scheme between mobility load balancing and mobility robustness optimization in self-organizing networks , 2018, Wirel. Networks.

[9]  Muhammad Ali Imran,et al.  A Survey of Self Organisation in Future Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.

[10]  Colin Willcock,et al.  Self-organizing networks in 3GPP: standardization and future trends , 2014, IEEE Communications Magazine.

[11]  Jiangzhou Wang,et al.  Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN , 2017, IEEE Transactions on Wireless Communications.

[12]  Juan Felipe Botero,et al.  Resource Allocation in NFV: A Comprehensive Survey , 2016, IEEE Transactions on Network and Service Management.

[13]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[14]  Nanba Shinobu,et al.  BBU-RRH Switching Schemes for Centralized RAN , 2013 .

[15]  Limin Xiao,et al.  SDN-Enabled C-RAN? An Intelligent Radio Access Network Architecture , 2016, WorldCIST.

[16]  Anja Klein,et al.  Dynamic Self-Optimization of the Antenna Tilt for Best Trade-off Between Coverage and Capacity in Mobile Networks , 2016, Wireless Personal Communications.

[17]  Philippe Martins,et al.  An Analytical Model for Evaluating Outage and Handover Probability of Cellular Wireless Networks , 2010, Wireless Personal Communications.

[18]  Ozan K. Tonguz,et al.  The Mathematical Theory of Dynamic Load Balancing in Cellular Networks , 2008, IEEE Transactions on Mobile Computing.

[19]  Yuh-Shyan Chen,et al.  A Dynamic BBU–RRH Mapping Scheme Using Borrow-and-Lend Approach in Cloud Radio Access Networks , 2018, IEEE Systems Journal.

[20]  Lei Li,et al.  Recent Progress on C-RAN Centralization and Cloudification , 2014, IEEE Access.

[21]  Wei Yu,et al.  Cloud radio access network: Virtualizing wireless access for dense heterogeneous systems , 2015, Journal of Communications and Networks.

[22]  Aleksandra Checko,et al.  Cloud Radio Access Network architecture. Towards 5G mobile networks , 2016 .

[23]  Yonggang Wen,et al.  Cloud radio access network (C-RAN): a primer , 2015, IEEE Network.

[24]  Yin Zhang,et al.  Green Spectrum Assignment in Secure Cloud Radio Network with Cluster Formation , 2019, IEEE Transactions on Sustainable Computing.

[25]  Karthikeyan Sundaresan,et al.  FluidNet: A Flexible Cloud-Based Radio Access Network for Small Cells , 2013, IEEE/ACM Transactions on Networking.

[26]  Wei Yu,et al.  Cloud Radio Access Networks: Principles, Technologies, and Applications , 2016 .

[27]  M. Toril,et al.  Analysis of Limitations of Mobility Load Balancing in a Live LTE System , 2015, IEEE Wireless Communications Letters.

[28]  Olav Tirkkonen,et al.  Network Optimization Methods for Self-Organization of Future Cellular Networks: Models and Algorithms , 2016 .

[29]  Raquel Barco,et al.  On the Potential of Handover Parameter Optimization for Self-Organizing Networks , 2013, IEEE Transactions on Vehicular Technology.

[30]  Michael S. Berger,et al.  Cloud RAN for Mobile Networks—A Technology Overview , 2015, IEEE Communications Surveys & Tutorials.

[31]  Xiaohu Ge,et al.  User Mobility Evaluation for 5G Small Cell Networks Based on Individual Mobility Model , 2015, IEEE Journal on Selected Areas in Communications.

[32]  Xuelong Li,et al.  Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues , 2016, IEEE Communications Surveys & Tutorials.

[33]  Zwi Altman,et al.  An open framework for programmable, self-managed radio access networks , 2015, IEEE Communications Magazine.

[34]  Olav N. Østerbø,et al.  Benefits of Self-Organizing Networks (SON) for Mobile Operators , 2012, J. Comput. Networks Commun..

[35]  Ke Wang,et al.  Dynamic resource allocation in TDD-based heterogeneous cloud radio access networks , 2016, China Communications.

[36]  Byeong Seok Ahn,et al.  Compatible weighting method with rank order centroid: Maximum entropy ordered weighted averaging approach , 2011, Eur. J. Oper. Res..

[37]  Wei Yu,et al.  Cross-Layer Design for Downlink Multihop Cloud Radio Access Networks With Network Coding , 2016, IEEE Transactions on Signal Processing.

[38]  Sampath Rangarajan,et al.  EXTREMELY DENSE WIRELESS NETWORKS , 2022 .

[39]  Jiangzhou Wang,et al.  When ICN meets C-RAN for HetNets: an SDN approach , 2015, IEEE Communications Magazine.

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

[41]  Xin-Ping Guan,et al.  Energy-aware and QoS-aware load balancing for HetNets powered by renewable energy , 2016, Comput. Networks.

[42]  Ashok Kumar Das,et al.  An Efficient Hybrid Anomaly Detection Scheme Using K-Means Clustering for Wireless Sensor Networks , 2016, Wirel. Pers. Commun..