Coordinated Data Assignment: A Novel Scheme for Big Data over Cached Cloud-RAN

A cloud radio access network (Cloud-RAN) is a network architecture that holds onto the promise of meeting the explosive growth of mobile data traffic. Cloud-RAN consists a central processor (CP) connecting to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. To reduce the backhaul traffic, BS-level caching technique is utilized in which the popular contents are pre-fetched in memories at each BS. This technique plays an important role in future wireless big data processing due to its simplicity, low cost, and natural integration with big data analytical tools. Considered the tradeoff between the backhaul and the transmission power cost, in this paper we define the network cost of the system as a normalized weighted sum. The problem of minimizing the network cost with respect to both the precoding matrix and the cache placement matrix is formulated subject to the quality of service (QoS), peak transmission power, and cache capacity constraints. The l0-norm in the objective function along with the QoS constraints renders the optimization problem non-convex. Additionally, since the entries of cache placement matrix take binary values, the optimization problem falls into a mixed integer nonlinear programming (MINLP) which is a NP-hard problem. An iterative coordinated data assignment algorithm is introduced which achieves a stationary point of the problem. Simulations are conducted to illustrate the performance of introduced algorithm. It suggests that the introduced scheme can significantly reduce the total network cost of the underlying Cloud-RAN network and demonstrate the importance of considering the designing of cache placement matrix.