# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.4 QoS-aware RRH Clustering  
In [58], the authors investigated the load-balancing problem in a C-RAN. The objective was to reduce the number of blocked calls and maximize the QoS. A key performance indicator (KPI) was used to represent the QoS. It was defined as the number of blocked calls. The scenario was developed such that 19 RRHs were distributed randomly and connected to a BBU pool consisting of two BBUs with three sectors each. The particle swarm optimization (PSO) algorithm was used for BBU-RRH mapping. Herein, the best particles were represented by a vector result, where each particle characterized a feasible combination of RRHs distributed in the sectors of the BBUs. In [59], [PERSON] et al. formulated a QoS-aware joint BBU-RRH mapping and user association problem in a C-RAN. The objective was to minimize the system cost by minimizing the power consumption of the RRHs and the number of virtual BBUs (VBs). The joint optimization problem was decomposed into two subproblems to reduce computing complexity. Accordingly, the user association problem was solved first, and the optimal solution was utilized to solve the BBU-RRH mapping problem. The former problem was solved using a Lagrangian relaxation algorithm, and the latter was solved by the best fit decreasing algorithm (LAGA-BFD). The BBU-RRH mapping problem was formulated as a bin packing problem. Here, VBs were backpacks, and the RRHs were the objects to be placed in the backpacks. To solve this problem, the BFD was utilized such that one RRH will be connected to one VB, which would be the best fit for it. This implies that the VB has the minimum remaining capacity after the addition of the RRH, and a VB was added when there was no VB available to accommodate the RRH. [PERSON] et al. investigated the dynamic RRH allocation considering a self-optimized C-RAN with the objective of maximizing the network QoS by traffic load balancing and minimizing handoversin the network [60]. The problem was formulated as an integer-based optimization problem in a self-organizing network (SON) by defining multiple KPIs. Based on this, a SON controller residing inside the BBU pool can identify the network configuration and perform dynamic RRH allocation as well as load balancing in the network. The KPIs considered here were for blocked users and handovers. Here, inter-BBU, intra-BBU, and forced handovers were considered. The self-organized C-RAN (SOCRAN) algorithm utilizing two evolutionary algorithms (genetic algorithm (GA) and deterministic particle swarm optimization (DPSO)) was proposed for solving the problem. It was demonstrated by numerical analysis that both the GA and DPSO provide optimal performance for small networks and nearly optimal performance for large networks, whereas DPSO outperformed GA in different network scenarios. In [61], the self-optimized architecture for C-RAN (SOCRAN) proposed in [60] was utilized for performing semi-static cell differentiation and integration (CDI) and dynamic BBU-RRH mapping for load balancing simultaneously. CDI was investigated with the objective of utilizing the network resources effectively while maintaining the overall network QoS. Herein, a cell was split into multiple small cells and vice-versa, in response to the measured load information in one or more cells in the network. A two-stage design was proposed, where the first stage computes the optimal number of BBUs according to load demand and activates or deactivates RRHs based on the CDI concept for handling traffic load. The second stage was modeled as an integer-based linear optimization problem for effective BBU-RRH mapping, to maximize QoS while minimizing handovers for network load balancing. Furthermore, this problem was solved using a DPSO algorithm. Three KPIs were considered for BBU-RRH mapping, taking into account the load fairness index, network throughput, and handovers. The authors in [62] proposed a self-optimized algorithm for dynamic RRH clustering in C-RAN based on load prediction.