QoE Provisioning by Random Access in Next-Generation Wireless Networks

In the next-generation wireless networks such as 5G network or very-high-throughput WLAN, the users (humans or machines) in human-to-human, human-to-machine and machine-to-machine communications usually have heterogenous demands and behaviors. In this case, it is very difficult to uniformly evaluate user Quality-of-Experience (QoE) by one or more traditional performance metrics (e.g. throughput, delay and interference level) due to the heterogeneity. Moreover, the diversity of terminal capacities further exacerbates the difficulty in QoE provisioning since parameter adjustment may be inapplicable to all the equipments. To solve the aforementioned problems, this paper introduces a new QoE evaluation metric: satisfaction, which is a measurable, comparable and general assessment to indicate the gap between the current QoE and the desire of a user. Based on this new metric, we model the channel contention of random access networks as a non-cooperative game, where the players (contention nodes) are altruistic but also care about their individual interests. Theoretical analysis and simulation experiments indicate that a reinforcement learning algorithm can help the proposed game to achieve an efficient Nash equilibrium, which maximizes individual satisfaction with proportional fairness.