Data Driven AI Assisted Green Network Design and Management
Abstract: The energy consumption of mobile networks is increasing due to an increase in traffic demands and the number of connected users to the network. To assure the sustainability of mobile networks, energy efficiency must be a key design pillar of the next generations of mobile networks. In this thesis, we deal with improving the energy efficiency of 5G and beyond networks from two perspectives, i.e., minimizing the energy consumption of the network, and energy-efficient network architecture design. In the first part of this thesis, we focus on energy-saving methods at the base stations (BSs) which are the most energy-consuming component of mobile networks. We obtain a data set from a mobile network operator which contains network load information. It is a challenge to use mobile network traffic data to train ML algorithms for sleep mode management decisions due to the coarse time granularity of data. We propose a method to regenerate mobile network traffic data taking into account the burstiness of arrivals. We propose ML-based algorithms to decide when and how deep to put BSs into sleep. The current literature on using ML in network management lacks guaranteeing any quality of service. To handle this issue, we combine analytical model-based approaches with ML where the former is used for risk analyses in the network. We define a novel metric to quantify the risk of decision-making. We design a digital twin that can mimic the behavior of a real BS with advanced sleep modes to continuously assess the risk and monitor the performance of ML algorithms. Simulation results show that using proposed methods considerable energy saving is obtained compared to the baselines at cost of a negligible number of delayed users. In the second part of the thesis, we study and model end-to-end energy consumption and delay of a cloud-native network architecture based on virtualized cloud RAN forming foundations of open RAN. Today large telco players achieved a consensus on an open RAN architecture based on hybrid C-RAN which is studied in this thesis. Migrating from conventional distributed RAN architectures to network architectures based on hybrid C-RAN is challenging in terms of energy consumption and costs. We model the migration cost, in terms of both OPEX and CAPEX, with economic viability analyses of a virtualized cloud-native architecture considering the future traffic forecasts. It is not clear under what conditions C-RAN-based architectures are more cost-efficient than D-RAN considering the infrastructure cost of fronthaul and fiber links. We formulate an integer linear programming (ILP) optimization problem to optimally design the fronthaul minimizing the migration costs. We solve the problem optimally using commercial solvers and propose AI-based heuristic algorithm to deal with the scalability issue of the problem for large problem sizes. Dealing with the trade-off between network energy consumption and delay is a challenging issue in network design and management. In a multi-layer hybrid C-RAN architecture, we formulate an ILP problem to optimize the delay by storing the popular contents in the edge closer to the users and to minimize the network energy consumption. Moreover, we investigate the trade-off between the overall energy consumption and occupied bandwidth in the network. We demonstrate that intelligent content placement reduces not only delay but also saves energy by finding a compromise between performance metrics. With a similar objective of minimizing network energy consumption, we propose a method for end-to-end network slicing, where logical networks are tailored and customized for a specific service. As per literature, end-to-end network slicing is optimized for the first time considering energy consumption. Most network slicing studies consider only radio access network resources. Intuitively, energy consumption goes down if more bandwidth resources are allocated to users when the RAN segment of the network is considered. However, with the end-to-end energy consumption model, presented in this thesis, it is demonstrated that increasing bandwidth allocation also increases processing energy consumption in the cloud and the fronthaul segment of the network. To deal with this issue, we formulate a non-convex optimization problem to allocate end-to-end resources to minimize the energy consumption of the network while guaranteeing the slices’ QoS. We transform the problem into a second-order cone programming problem and solve the problem optimally. We show that end-to-end network slicing can decrease the total energy consumption of the network compared to radio access network slicing.
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