Abstract
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.
Highlights
T HE UP-SURGING volume of data services and applications along with the accelerated growth in wirelessManuscript received December 12, 2016; revised March 24, 2017, June 15, 2017, and June 19, 2017; accepted June 20, 2017
The self-organisation concept is explained in phases as shown in Fig. 1, where the observation and analysis phases are utilised to detect the performance of current network deployment (BBU-remote radio heads (RRHs) configuration), and an optimal implementation is identified for performance comparison
This paper presents a system model designed as a centralised-self-optimising network (SON) architecture for cloud radio access network (C-RAN), which allows for more efficient resource utilisation through centralised control across aggregated BBU resources
Summary
T HE UP-SURGING volume of data services and applications along with the accelerated growth in wireless. Densifying the access networks using small cells is realised as a promising solution to increase capacity and coverage, especially at traffic hot-spots This leads to even bigger challenges for the MNOs such as the significant increase in Capital (CAPEX) and Operational (OPEX) expenditures, inefficient utilisation of network resources due to traffic imbalances and increased signalling overhead caused by frequent handovers among small cells. The main contribution of this paper is to present an efficient model for proper BBU-RRH mapping in C-RAN as one SON approach to achieve a self-optimising network structure and solving a load balancing problem. The self-optimising feature of SON combined with the capacity routing ability of C-RAN is explored to achieve a balanced system load with high levels of QoS. This paper presents load balancing in C-RAN and the realisation of virtual small cells (supported by low-power RRHs), rather than Micro and Pico cells deployment at each antenna position.
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