A Resource Allocation Scheme for 5G C-RAN Based on Improved Adaptive Genetic Algorithm

Abstract

AbstractCloud-Radio Access Networks (C-RAN) is a novel mobile network architecture where baseband resources are pooled, which is helpful for the operators to deal with the challenges caused by the non-uniform traffic and the fast growing user demands. The main idea of C-RAN is to divide the base stations into the baseband unit (BBU) and the remote radio head (RRH), and then centralize the BBUs to form a BBU pool. The BBU pool is virtualized and shared between the RRHs, improving statistical multiplexing gains by allocating baseband and radio resources dynamically. In this paper, aiming at the problem of resource dynamic allocation and optimization of 5G C-RAN, a resource allocation strategy based on improved adaptive genetic algorithm (IAGA) is proposed. The crossover rate and mutation rate of the genetic algorithm are optimized. Simulation results show that the performance of the proposed resource allocation strategy is better than the common frequency reuse algorithm and the traditional genetic algorithm (GA).KeywordsCloud-Radio Access NetworkResource allocationImproved adaptive genetic algorithmBaseband unitRemote radio head