Hirantha Abeysekera’s research while affiliated with Nippon Telegraph and Telephone and other places

This paper proposes a priority control method utilizing channel reservation for high-priority access points (APs) in IEEE 802.11 wireless LANs. The proposed algorithms are based on a network-controlled channel allocation scheme called RATOP. Computer simulation results demonstrated the positive effect of the proposed scheme on average and minimum throughput in a large area. This paper also proposes an enhanced algorithm that releases reserved channels to improve throughput. An estimation method based on theoretical analysis provides the number of required reserved channels for a given throughput ratio of high-priority APs to low-priority APs.

This paper proposes a deep learning-based angle-of-arrival (AoA) estimation method using Wi-Fi channel state information (CSI). In MIMO-OFDM systems, data are transmitted using orthogonal subcarriers at different frequencies using multiple transmit-receive antenna pairs. However, because different subcarriers have different wavelengths, the impact of environmental noise on the CSI of the subcarriers is also different in real environments. In addition, we utilize compressed CSI data, involving the reduction of dimensionality from raw CSI data. This introduces a possibility of losing information about individual antennas in some subcarriers, potentially impacting the accuracy of AoA estimation. In this study, we propose to selectively use optimal subcarriers for estimating the AoA using a deep neural network. Our designed neural network, named AoA-Net, has an end-to-end architecture that automatically performs subcarrier selection using CSI data as input and then performs AoA estimation using information regarding the selected subcarriers. In addition, we employ multi-task learning to efficiently acquire knowledge that assists in selecting optimal subcarriers. To the best of our knowledge this is the first CSI-based study that estimates the AoA using end-to-end deep learning based on subcarrier selection. We demonstrate the effectiveness of our method using data collected in real environments, and our method exhibits state-of-the-art performance when using leave-one-environment-out cross-validation.

The IEEE 802.11be includes multi-link operation (MLO) as a crucial feature. MLO allows a multi-link device (MLD) with multiple wireless interfaces to use them simultaneously and send packets concurrently. However, the performance of the MLD may degrade due to interference or insufficient chances to access the wireless medium. In this study, we assessed the throughput and the latency of a emulated MLD configured by bundling multiple single-link access points. We also evaluated the effect of the link selection in the presence of interfering links. The results showed that selecting appropriate links according to interference enables improving the latency.

Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes.

Wireless local area network (WLAN)-based localization is key for advanced indoor Internet-of-Things and embedded sensor applications. To further improve the accuracy of indoor localization, attention has been focused on WLAN-based indoor localization using channel-state information (CSI) in addition to the existing information provided by received signal strength (RSS). For easy and low cost installation of wireless sensing, wireless sensing based on standardized protocols and commercial WLAN devices, such as IEEE 802.11ac and IEEE 802.11ax, is necessary. There are few papers demonstrating AoA estimation results by using commercial WLAN devices based on CSI. Therefore, we propose a practical method for estimating the AoA to solve four problems: 1) compressed CSI, which cannot be used for AoA estimation directly, 2) the antenna wireline, in which the phase changes depending on the length of the wireline, 3) the antenna spacing, in which the distance between antennas places a restriction on AoA estimation, and 4) antenna individuality, in which the antennas used in actual MIMO communication have different characteristics. We implemented the proposed method on IEEE 802.11ac devices and evaluated it in a lecture room and shield tent. The results indicate that the proposed method can estimate AoA with an average error of 9.1° and reduce the estimation error by 85.4 % compared with a straightforward approach.

In this paper, we propose a novel centralized control method to handle multi-radio and terminal connections in an 802.11ax wireless LAN (802.11ax) mixed environment. The proposed control method can improve the throughput by applying 802.11ax Spatial Reuse in an environment hosting different terminal standards and mixed terminal communication quality. We evaluate the proposed control method by computer simulations assuming environments with mixed terminal standards, mixed communication quality, and both.

Future wireless networks will be facing unprecedented difficulties arising from mobile traffic growth, network densification, as well as diversification of applications and services. Indeed, future user devices are expected to integrate diverse radio interfaces such as 5G, WBAN or IoT, enabling each user to be served a wide range of applications at any time. This poses significant challenges in terms of wireless resource sharing and interference management, as more and more stringent Quality of Service (QoS) constraints should be jointly satisfied in dense interfering environments. Furthermore, future networks are expected to be highly autonomous and decentralized. To meet these challenges, this work proposes distributed user-to-multiple Access Points (AP) association methods, where the objective is to maximize the long-term sum-rate subject to application QoS constraints, as well as to AP load constraints. Our distributed methods enable each user to leverage their Deep Reinforcement Learning (DRL) capabilities, in particular Deep Q-Learning (DQL), to self-optimize their APs’ selection solely based on their local network state knowledge, so as to best satisfy their diverse requirements. Numerical results show that, compared to baseline schemes, the proposed methods enable global throughput enhancements while reducing user QoS outage probabilities, even in large and dense networks.

In this paper, a stochasic geometry analysis of the inversely proportional setting (IPS) of carrier sense threshold (CST) and transmission power for densely deployed wireless local area networks (WLANs) is presented. In densely deployed WLANs, CST adjustment is a crucial technology to enhance spatial reuse, but it can starve surrounding transmitters due to an asymmetric carrier sensing relationship. In order for the carrier sensing relationship to be symmetric, the IPS of the CST and transmission power is a promising approach, i.e., each transmitter jointly adjusts its CST and transmission power in order for their product to be equal to those of others. This setting is used for spatial reuse in IEEE 802.11ax. By assuming that the set of potential transmitters follows a Poisson point process, the impact of the IPS on throughput is formulated based on stochastic geometry in two scenarios: an adjustment at a single transmitter and an identical adjustment at all transmitters. The asymptotic expression of the throughput in dense WLANs is derived and an explicit solution of the optimal CST is achieved as a function of the number of neighboring potential transmitters and signal-to-interference power ratio using approximations. This solution was confirmed through numerical results, where the explicit solution achieved throughput penalties of less than 8% relative to the numerically evaluated optimal solution.

In this paper, we examine techniques for improving the throughput of unlicensed radio systems such as wireless LANs (WLANs) to take advantage of multi-radio access to mobile broadband, which will be important in 5G evolution and beyond. In WLANs, throughput is reduced due to mixed standards and the degraded quality of certain frequency channels, and thus control techniques and an architecture that provide efficient control over WLANs are needed to solve the problem. We have proposed a technique to control the terminal connection dynamically by using the multi-radio of the AP. Furthermore, we have proposed a new control architecture called WiSMA for efficient control of WLANs. Experiments show that the proposed method can solve those problems and improve the WLAN throughput.