Dynamic Resource Allocation in C-RAN with Real-Time Traffic and Realistic Scenarios

Abstract

This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with QoS constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modelling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. Results show that even after the optimization, there are some time intervals where the allocated resources are underutilized, which opens the door to the definition of new Machine Learning algorithms able to predict the required capacity.