Allocating Multi-type Resources in Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access network (H-CRAN) has been proposed as a promising architecture for 5G communication system. By incorporating cloud computing into heterogeneous networks (HetNets), the H-CRAN is supposed to provide seamless service with high energy efficiency, strong processing capability and good Quality of Service (QoS) guarantees. Developing multi-type resources allocation algorithm is critical for this typical heterogeneous resources system. In this work, we propose a resource allocation algorithm, named heterogeneous weighted dominant resource fairness (H-WDRF). With H-WDRF, the transmission power allocation problem is formulated as a coordinated beamforming problem, whose objective function is to minimize the total transmission power with individual computation resource capacity, signal to interference plus noise ratio (SINR), and maximum transmission power budget constraints. After the transformation of non-convex constraints, the optimization problem can be iteratively solved by second order cone programming (SOCP). Based on the optimum transmission power, the computation resource and bandwidth are allocated next. To consider the diverse requirements of operation tasks among these two types of resources, especially opposite requirements between the control signalling and data, the H-WDRF deploys a progressive filling algorithm to fairly allocate the resources based on weighted dominant resource share. The simulation results validate the effectiveness of H-WDRF, and the impact of underlying diverse resources requirements to the resource utilization efficiency in H-RAN.