Abstract
A Self-Organising Cloud Radio Access Network (C-RAN) is proposed, which dynamically adapt to varying capacity demands. The Base Band Units and Remote Radio Heads are scaled semi-statically based on the concept of cell differentiation and integration (CDI) while a dynamic load balancing is formulated as an integer-based optimisation problem with constraints. A Discrete Particle Swarm Optimisation (DPSO) is developed as an Evolutionary Algorithm to solve load balancing optimisation problem. The performance of DPSO is tested based on two problem scenarios and compared to an Exhaustive Search (ES) algorithm. The DPSO deliver optimum performance for small-scale networks and near optimum performance for large-scale networks. The DPSO has less complexity and is much faster than the ES algorithm. Computational results demonstrate significant throughput improvement in a CDI-enabled C-RAN compared to a fixed C-RAN, i.e., an average throughput increase of 45.53 and 42.102 percent, and a decrease of 23.149 and 20.903 percent in the average blocked users is experienced for Proportional Fair (PF) and Round Robin (RR) schedulers, respectively. A power model is proposed to estimate the overall power consumption of C-RAN. A decrease of $\approx 16\%$ is estimated in a CDI-enabled C-RAN when compared to a fixed C-RAN, both serving the same geographical area.
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