Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series

Abstract

Dynamic channel selection is among the most important wireless communication elements in dynamically changing electromagnetic environments wherein a user can experience improved communication quality by choosing a better channel. Multi-armed bandit (MAB) algorithms are a promising approach by which the difficult tradeoff between exploration to search for better a channel and exploitation to experience enhanced communication quality is resolved. Ultrafast solution of MAB problems has been demonstrated by utilizing chaotically oscillating time series generated by semiconductor lasers. In this study, we experimentally demonstrate a MAB algorithm incorporating laser chaos time series in a wireless local area network (WLAN).Autonomous and adaptive dynamic channel selection is successfully demonstrated in an IEEE802.11a-based, four-channel WLAN. Although the laser chaos time series is arranged prior to the WLAN experiments, the results confirm the usefulness of ultrafast chaotic sequences for real wireless applications. In addition, we numerically examine the underlining adaptation mechanism of the significantly simplified MAB algorithm implemented in the present study compared with the previously reported chaos-based decision makers. This study provides a first step toward the application of ultrafast chaotic lasers for future high-performance wireless communication networks. IntroductionThe resources for wireless communications are physically limited because of their narrow frequency bandwidth and ever-increasing demands in society [1]. Therefore, dynamic channel selection is among the most important wireless communication elements in dynamically changing electromagnetic environments such that a user can experience improved communication quality by choosing a better channel in terms of, for instance, the communication throughput [2]. The metric could also be minimizing energy consumption, communication delay, etc., depending on the interest of a given system. Autonomous and prompt adaptation is important to the dynamically changing wireless communication environments.Lai et al. modelled the channel selection problem as a multi-armed bandit (MAB) problem [3]; an example of a MAB problem is finding the most highly profitable slot machine among many machines. To find the best machine, one must conduct exploration to search for the high reward machine. However, too much exploration may accompany significant losses whereas a too quick decision may result in missing the best choice. Hence, a difficult tradeoff exists, which is referred to as an exploration-exploitation dilemma [4]. A channel selection problem in wireless networks can be regarded as a MAB problem by associating the communication quality (such as the throughput) to the reward of a slot machine. Recently, Kuroda et al. applied a Tug-of-War algorithm [5] for MAB problems to a wireless local area network (WLAN) to demonstrate its effectiveness [6]. In a wider context, Obayuiwana et al. reviewed network selection problems using a decision-making algorithm [7]. M...

Entangled and correlated photon mixed strategy for social decision making

“…have already demonstrated optimal entangled photon states for three, four, and five players for the 2-armed bandit problem 21 . Scalability analysis is also critical from the viewpoint of practical applications such as resource management in information and communication infrastructure 10 . Also, the mixed strategy studied herein allows the players to immediately change the photon states from correlated to entangled photons when they detect the end of happy hour in Step 3 of Algorithm 1.…”

“…As individual players seek to maximize their rewards, they will choose the best machine, which leads to a decision conflict. Many relevant issues in real-life situations, ranging from congestion in information networks and traffic jams on roads to hoarding of goods are caused by many players choosing the same decision 9 , 10 , suggesting the importance of collective decision making. Such a problem that deals with multiple players and slot machines is called the competitive multi-armed bandit (CMAB) problem 16 .…”

Analysis on Effectiveness of Surrogate Data‐Based Laser Chaos Decision Maker

“…In recent years, photons and laser physics for high-speed information processing have been studied in various applications including photonic reservoir computing and neuromorphic photonic computing, among others [1][2][3][4][5][6][7]. A high-speed decision maker using ultrafast laser chaos has been demonstrated for applications in solving multiarmed bandit (MAB) problems [4,5].…”

“…Naruse et al [5] proposed a scalable laser chaos decision maker applicable to MABs with up to 64 arms. ese ultrafast decision makers can be applied to various problems, which require fast decision-making in dynamically changing uncertain environments [6,7]. Takeuchi et al applied laser chaos decision-making to channel selection problems in wireless communication systems, in which communication channels suffer from dynamically changing disturbances owing to traffic, interference, fading, etc.…”

“…Takeuchi et al applied laser chaos decision-making to channel selection problems in wireless communication systems, in which communication channels suffer from dynamically changing disturbances owing to traffic, interference, fading, etc. [6]. Furthermore, Kanno et al utilized laser chaos for adaptive model selection with photonic reservoir computing [7].…”