Scalable Algorithms for Cloud Radio Access Network (C-RAN) Optimization

In the evolving scenario of 5G networks, resource allocation algorithms for the Cloud Radio Access Network (C-RAN) model have proven to be the key for managing ever increasing Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) for mobile networks while ensuring high Quality of Service (QoS). In Chapter 1 a brief overview of the main elements of the C-RAN and of the methodologies that are employed in this work is provided. In Chapter 2, an exact scalable methodology for a static traffic scenario, based on lexicographic optimization, is proposed for the solution of a multi-objective optimization problem to achieve, among other goals, the minimization of the number of active nodes in the C-RAN while supporting reliability and meeting latency constraints. The optimal solution of the most relevant objectives for networks of several tens of nodes is obtained in few tens of seconds of computational time in the worst case. For the least relevant objective a heuristic is developed, providing near optimal solutions in few seconds of computing time. In Chapter 3, an optimization framework for dynamic C-RAN reconfiguration is developed. The objective is to maintain C-RAN cost optimization, while minimizing the cost of virtual network function migration. Significant savings in terms of migrations (above 82% for primary virtual BBU functions and above 75% for backup virtual BBU functions) can be obtained with respect to a static traffic scenario, with execution time of the optimization algorithm below 20 seconds in the worst cases, making its application feasible for dynamic scenarios. In Chapter 4, an alternative Column Generation model formulation is developed, and the quality of the computed lower bounds is evaluated. Further extensions from this baseline (e.g. Column Generation based heuristics, exact Branch&Price algorithms) are left as future work. In Chapter 5, the main results achieved in this work are summarized, and several possible extensions are proposed.