A Q-Learning Framework for User QoE Enhanced Self-Organizing Spectrally Efficient Network Using a Novel Inter-Operator Proximal Spectrum Sharing

摘要

As the mobile data traffic continues to grow rapidly, spectrum availability is a major concern. Furthermore, the fixed spectrum allocation to the mobile network operators (MNOs) and lack of free spectrum result in poor user quality of experience (QoE) and inefficient spectral resource utilization. Inter-operator spectrum sharing is a solution to overcome the spectral shortage, and this is achieved either by an MNO leasing other MNO's spectrum or by sharing a common pool of MNOs' spectrum. Although both approaches bring benefits, they have further challenges. We propose a novel spectrum sharing paradigm called inter-operator proximal spectrum sharing (IOPSS), where a base station (BS) intelligently offloads users to the neighboring BSs based on spectral proximity to enhance the users' QoE and spectral resource utilization. Users requesting high service rates can be served by using carrier aggregation. We demonstrate the IOPSS's benefits using a continuous-time Markov chain-based analytical model of a BS. A generic IOPSS Q-learning framework (IOPSS-QLF) for a BS to dynamically determine its load-based spectral needs and efficiently share its spectrum resulting in a self-organizing spectrally efficient network of BSs is proposed. The effectiveness of IOPSS-QLF is demonstrated using extensive simulations.