FQ-SAT: A fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization

In the 5G and beyond era, multipath transport protocols, including MPQUIC, are necessary in various use cases. In MPQUIC, one of the most critical issues is efficiently scheduling the upcoming transmission packets on several paths considering path dynamicity. To this end, this paper introduces FQ-SAT - a novel Fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization, including download time, in heterogeneous wireless networks. Different from previous works, FQ-SAT combines Q-learning and Fuzzy logic in an MPQUIC scheduler to determine optimal transmission on heterogeneous paths. FQ-SAT leverages the self-learning ability of reinforcement learning (i.e., in a Q-learning model) to deal with heterogeneity. Moreover, FQ-SAT facilitates Fuzzy logic to dynamically adjust the proposed Q-learning model’s hyper-parameters along with the networks’ rapid changes. We evaluate FQ-SAT extensively in various scenarios in both simulated and actual networks. The results show that FQ-SAT reduces the single-file download time by 3.2%–13.5% in simulation and by 4.1%–13.8% in actual network, reduces the download time of all resources up to 20.4% in web browsing evaluation, and reaches percentage of on-time segments up to 97.5% in video streaming, compared to state-of-the-art MPQUIC schedulers.