Bat algorithm based semi-distributed resource allocation in ultra-dense networks

This paper addresses the resource allocation (RA) for ultra-dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non-convex and NP-hard. Therefore, this paper considers and studies a semi-distributed resource block (RB) allocation scheme, in order to achieve a well-balanced trade-off between performance and complexity. In the context of semi-distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster-based RB allocation. We first improve the K-means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K-means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster-based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade-off between the performance and the implementation complexity.