PaGeO: Pareto-based Genetic Optimization for LTE radio-over-fiber cellular backhauling

Radio-over-Fiber (RoF) emerges as a promising solution to achieve cost-effective radio-antennas backhauling. The limited dynamic range of analog optical transmission is known as one of the most penalizing limitations of analog RoF links. Thus, without a special care to RoF signal impairments, the wireless system capacity may be seriously degraded. In this paper, we propose an impairment-aware RoF-based cellular backhauling system called GeRoFAN aiming to preserve the capacity of the radio system while using efficiently the optical capacity. Supported by an analytical modeling of RoF physical impairments, we develop an evolutionary multi-objective heuristic called PaGeO (Pareto-based Genetic Optimization) exploiting the concept of Pareto optimization to determine the best backhauling strategy for the GeRoFAN control plane. We outline through numerical results that our impairment-aware backhauling solution computed by PaGeO, applied to LTE radio cellular system, outperforms alternative backhauling policies. We also highlight the impact of RF cellular planning on the tradeoff optimization achieved by PaGeO.