Graph Based Training Resource Allocation Scheme for CoMP Transmission in CRAN: A Low Complexity Solution

Abstract

Coordinated Multi Point (CoMP) transmission in Cloud Radio Area Network (CRAN) strives to provide the required Quality of Service (QoS), irrespective of user's location. Specifically, CoMP enables intra-cluster Access Points (APs) to maximize user's performance utilizing channel information, which is obtained by exchanging prior-known pilot signals among the intended users and intra-cluster APs. These signals are often referred as training signals, and are shared over Training Resources (TRs). However, CoMP is pillared on user-centric network, thereby the resulting clusters are inevitably overlapped. As a result, the TR allocation schemes for these networks require considerably large computational complexity. In this paper, we introduce the notion of Time Slot Tagging (TST), in which each AP is tagged with an appropriate training slot. Further, clusters are formed on the basis of this tagged information. Specifically, TST eliminates the requirement of resource allocation for individual users which saves a significant time in each clustering cycle, and thus enhances the link utilization factor. Also, TST is designed in a manner that it assures the presence of preferred APs in each intended cluster. Further, it is shown that the proposed approach performs significantly close to the most optimistic scenario.