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Autonomous parameter optimization of a heterogeneous wireless network aggregation system using machine learning algorithms

By increase of various radio access network (RAN) services, available spectrum resources for mobile communications get decrease, and efficient use of the radio resource is becoming a very important issue. In order to optimize the radio resource usage and maxmize the throughput and quality of service (QoS), the link aggregation technologies to utilize multiple different available RANs have been studied. However, in such heterogeneous wireless networks, it is difficult to improve the throughput by their aggregation because of the differences among the QoSs of the different RANs. In this paper, we propose an autonomous parameter optimization scheme using a machine learning algorithm, which maximize the throughput of the heterogeneous RAN aggregation system. We evaluate the performance of the proposed scheme implemented on a cognitive wireless network system called Cognitive Wireless Cloud (CWC) system, connected to real wireless network services, such as HSDPA, WiMAX and W-CDMA. Our experimental results of the proposed system show that the aggregation throughput can be improved with increase of the training samples, which are collected autonomously.