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Locally Orthogonal Training Design in Cloud-RANs

We consider training-based channel estimation for cloud radio access networks (CRANs), in which a large amount of remote radio heads (RRHs) and users are randomly scattered over a certain service area. In this model, assigning orthogonal training sequences to all users, if possible, will cause a substantial overhead to the overall network. Instead, we introduce the notion of local orthogonality, in which the training sequence of a user is required to be orthogonal to the training sequences of other users in its neighborhood. We model the design of locally orthogonal training sequences as a graph coloring problem. Then, based on the theory of random geometric graph, we show that the minimum training length scales in the order of ln K, where K is the number of users covered by the CRAN. Therefore, the proposed training design yields a scalable solution to sustain the need of large-scale cooperation in CRANs.