Fronthaul optimization in 5G Cloud Radio Access Networks by means of genetic algorithm and georeferencing
To support the anticipated demands of density, scale, variety of use and
speed on 5G, which is the fifth generation technology standard for mobile networks, new network architectures are being proposed as optimization alternatives of current cell phone networks. In this context the Cloud Radio Access Networks (C-RAN) architecture emerges as an alternative enabling the evolution to 5G, in which radio connectivity for users outputs is provided through Remote Radio Head (RRH) and most signal processing tasks are performed on Base Band Unit (BBU) in the cloud. Indeed, by separating the BBUs from the antennas, there are important reductions in energy consumption and cost. However, that implies the creation of a low latency optical network for BBU interconnection and RRHs, which is called a fronthaul. In this context, this work seeks to optimize of the distribution of BBUs and RRHs through the fronthaul and that minimizes the costs of capital for deployment.
Given the complexity of the problem, this work proposes the use of a technique of evolutionary computation based optimization through Genetic Algorithm (GA) implementation. In this sense, scenarios are evaluated
based on georeferenced data from the deployment of the current 4G network, considering the constraints of: (i) distance, which is related to latency, (ii) number of RRHs per BBU, which is limited by the Wavelength Division Multiplexing (WDM) network used in fronthaul, and (iii) traffic protection in fronthaul. The obtained results indicate the effectiveness of the proposed approach, and it is possible to visualize them through of a georeferenced web tool.