Reducing Power Consumption For Signal Computation in Radio Access Networks : Optimization With Linear Programming and Graph Attention Networks

Abstract: There is an ever-increasing usage of mobile data with global traffic having reached 115 exabytes per month at the end of 2022 for mobile data traffic including fixed wireless access. This is projected to grow up to 453 exabytes at the end of 2028, according to Ericssons 2022 mobile data traffic outlook report. To meet the increasing demand radio access networks (RAN) used for mobile communication are continuously being improved with the current generation enabling larger virtualization of the network through the Cloud RAN (C-RAN) architecture. This facilitates the usage of commercial off-the-shelf servers (COTS) in the network replacing specialized hardware servers and making it easier to scale up or down the network capacity after traffic demand. This thesis looks at how we can efficiently identify servers needed to meet traffic demand in a network consisting of both COTS servers and specialized hardware servers while trying to reduce the energy consumption of the network. We model the problem as a network where the antennas and radio heads are connectedto the core network through a C-RAN and a specialized hardware layer. The network is then represented using a graph where the nodes represent servers in the network. Using this problem model as a base we then generate problem instances with varying topologies, server profiles, and traffic demands. To find out how the traffic should be passed through the network we test two different methods: A mixed integer linear programming (MILP) method focused on energy minimization and a graph attention network (GAT) predictor combined with the energy minimization MILP. To help evaluate the results we also create three other methods: a MILP model that tries to spread the traffic as evenly as possible, a random predictor combined with the energy minimization MILP and a greedy method. Our results show that the energy optimization MILP method can be used to create optimal solutions, but it suffer from a slow computation time compared to the other methods. The GAT model shows promising results in making predictions regarding what servers should be included in a network making it possible to reduce the problem size and solve it faster with MILP. The mean energy cost of the solutions created using the combined GAT/MILP method was 4% more than just using MILP but the time gain was substantial for problems of similar size as the GAT was trained on. With regards to computation time the combined GAT/MILP method used was 85% faster than using only MILP. For networks of almost double the size than the ones that the GAT model was trained on the solutions of the combined GAT and MILP methods had a mean energy cost increase of 7% while still showing a strong speedup, being 93% faster than when only using MILP.