The growing demand for information access, generated by multimedia
applications, is one of the challenges of the new generation of mobile networks. The
fifth generation (5G) aims to meet increasingly stringent user requirements, such as
latencies and low power consumption. One of the proposed architectures to supply the
demands that arise with 5G and to support this traffic is the Cloud Radio Access
Network (C-RAN), which centralizes processing power to solve the load imbalance,
allocate resources accordingly based on network demand. This architecture proposes
resource sharing while addressing processing scalability issues. Recently,
metaheuristic optimization algorithms have been widely used to solve problems of this
nature. Meta-heuristic algorithms are used because they are more powerful than
conventional methods, which are to on formal logic or mathematical programming, in
addition to the fact that the time required for execution is less than the exact algorithms’
one. In this context, the objective of this study is to develop an optimized resource
allocation model that performs load balancing between Baseband Units (BBUs) and
Remote Radio Heads (RRHs), based on the Particle Swarm Optimization (PSO)
method. For this purpose, a variation of the PSO algorithm, the Discrete Particle
Swarm Optimization (DPSO) was used, to optimize the proposed objective function.
Results indicated a point to superior performance of this objective function in
comparison to the adopted benchmarking, both in high and low traffic densities.
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