Mobile wireless communication demand has increased rapidly in recent years, and advanced mobile devices such as smartphones have been widely deployed. Those devices are generally capable of connecting various wireless networks such as cellular networks and wireless LANs. Due to the software and hardware constraint of mobile devices, it is necessary to develop efficient and lightweight algorithms to select wireless networks. Previous studies have shown that multi-armed bandit algorithms can efficiently select wireless channel in cognitive radio. In this paper, we propose an efficient wireless network selection technique by using a multi-armed bandit algorithm called tug-of-war (TOW). We implement the proposed algorithm to a wireless device and show the effectiveness of the proposed method by experimental demonstrations.

<abstract><p>Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.</p></abstract>

Abstract:The wide spread of mobile communication devices has increased the opportunities to use wireless communication technologies, irrespective of one's geographical location. However communication quality deteriorates due to factors such as competition for scarce radio resources and interference among nearby devices. Cognitive radio technologies have been developed recently to conquer such difficulties. In this paper, we propose a wireless network optimization method using learning algorithms based on a control model known as cognitive cycle. We implement the proposed optimization method in wireless LANs and evaluate the throughput performance. The experimental results show the effectiveness of the proposed approach in a real environment.

Soft tissue metastases of prostate cancer to other sites are extremely rare, and, to our best knowledge, there have been no reports of metastasis to soft tissue of the hand. A 63-year-old man was diagnosed with prostatic cancer. During treatment, bone and soft tissue metastases to the right hand, appearing in the first web space, were observed. The tumor was resected, along with both the first and second metacarpal bones. The thumb was reconstructed by pollicization of the remaining index finger, enabling the patient to use the pollicized thumb for activities of daily living. This is the first case report of prostate cancer metastasizing to the soft tissue in hand. After wide resection, pollicization was able to reconstruct a functional hand and thumb.

In recent years, the demand for new applications using various Internet of Things (IoT) devices has led to an increase in the number of devices connected to wireless networks. However, owing to the limitation of available frequency resources for IoT devices, the degradation of the communication quality caused by channel congestion is a practical problem in developing IoT technology. Many IoT devices have hardware and software limitations that prevent centralized channel allocation, and congestion is even more severe in massive IoT networks without a central controller. Therefore, developing a distributed and sophisticated channel selection algorithm is necessary. In previous studies, the channel selection of each IoT device was modeled as a multi-armed bandit (MAB) problem, and a wireless channel selection method based on the MAB algorithm, which is a simple reinforcement learning, was proposed. In particular, it has been shown that the MAB algorithm of tug-of-war (TOW) dynamics can efficiently select channels with much lower computational complexity and power compared with other reinforcement learning-based channelselection methods. This paper proposes a distributed channel selection method based on TOW dynamics in fully decentralized networks. We evaluate the effectiveness of the proposed method and other distributed channel-selection methods on the communication success rate in massive IoT networks by experiments and simulations. The results show that the proposed method improves the communication success rate more than other distributed channel selection methods even in a dense and dynamic network environment.