LTE-LAA cell selection through operator data learning and numerosity reduction

Long Term Evolution-Licensed Assisted Access (LTE-LAA) architecture is markedly different from traditional LTE HetNets. LTE-LAA deployments also have to contend with interference from coexisting Wi-Fi transmissions in the unlicensed spectrum. Hence, there is a need for innovative cell selection solutions that cater specifically to LTE-LAA. Further, the impact of cell selection on the performance of the existing LTE-LAA deployments should also be investigated through operator data analysis. This work addresses these challenges. We gather a large sample of LTE-LAA deployment data for three cellular operators, i.e., AT&T, T-Mobile, and Verizon, which is analyzed through several supervised machine learning algorithms. We study the effect of cell selection on LTE-LAA capacity and network feature relationships. Insightful inferences are drawn on the contrasting characteristics of the Licensed and Unlicensed components of an LTE-LAA system. Further, a cell-quality metric is derived from operator data and is shown to have a strong correlation with Unlicensed coexistence network performance. To validate the proposed ideas, two state-of-the-art cell association and resource allocation solutions are implemented. Validation results show that data-driven cell-selection can reduce Unlicensed association time by as much as 34.89%, and enhance Licensed network capacity by up to 90.41%. Finally, with the vision to reduce the computational overhead of data-driven cell selection in LAA and 5G New Radio Unlicensed networks, the performance of two popular numerosity reduction techniques is evaluated.     

An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts

The aim of this paper is to present an integrated mathematical model to solve the dynamic cell formation problem considering operator assignment and inter/intra cell layouts problems with machine duplication, simultaneously. The proposed model includes three objectives which the first objective seeks to minimize inter/intra cell part movements and machine relocation, the second objective minimizes machine and operator related costs and the third objective maximizes consecutive forward flows ratio. In order to validate the proposed model, a numerical example is presented and solved by the sum weighted method. Due to NP-hardness of the model, two meta-heuristics namely multi-objective simulated annealing (MOSA) and multi-objective vibration damping optimization (MOVDO) present to solve the proposed model. Finally, two algorithms have been compared using multi-objective criteria.