An inefficient utilisation 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 optimises 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 (BBU) sectors in a time-varying traffic environment.
RRH-sector configuration redistributes the network capacity
over a given geographical area. A Self-Optimised 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
optimisation problem by defining it as a weighted combination
of new key performance indicators (KPIs) for the number
of blocked users and handovers in the network subject to
RRH sectorisation constraint. A Genetic Algorithm (GA) and
Discrete Particle Swarm Optimisation (DPSO) are proposed
as evolutionary algorithms to solve the optimisation 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 (ES) 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 minimise both handovers and
blocked users. Furthermore, a trade-off between two critical
parameters for the SOCRAN algorithm is presented, to achieve
performance benefits based on the type of hardware utilised for
C-RAN