Hiroaki Hashida 교수

Intelligent reflecting surfaces (IRSs) have emerged as a key enabler for beyond fifth-generation (B5G) communication technology and for realizing sixth-generation (6G) cellular communication. In addition, B5G and 6G networks are expected to support aerial user communications in accordance with the expanded requirements of data transmission for an aerial user. However, there are challenges in providing wireless communication for aerial users owing to the different radio wave propagation propertie

In recent years, intelligent reflecting surfaces (IRSs) for large-capacity and highly reliable wireless communication have attracted widespread attention. However, a multiuser access system with multiple IRSs poses limitations in reducing the large signaling overhead of channel estimation for numerous links between the IRSs and users. One approach to reduce the exhaustive channel estimation involves associating the IRS with a user and performing beam tracking for a certain period. However, as th

Intelligent reflecting surfaces (IRSs) enhance the robustness of wireless transmissions against shielding. However, addressing channel-estimation overheads caused by the large number of IRS elements remains a major challenge. In particular, these overheads increase proportionately with an increase in the number of IRS elements, thereby making it difficult to handle multiple IRSs. Therefore, we investigate a passive beamforming method with partial channel state information (CSI) in distributed IR

Recently, Internet-wide scanning has emerged as an important element of detecting the security vulnerabilities of the Internet of Things (IoT) devices. However, Internet-wide scanning induces network congestion when sending a huge number of port scanning packets in a short time. The wireless networks are particularly subject to congestion from scanning traffic. Therefore, the scan rate should be low to reduce network congestion. However, as the number of IoT devices connected to a WLAN increases

Channel estimation plays a crucial role in intelligent reflecting surface (IRS)-aided communication systems, in which the channel state information must be acquired to configure the optimal IRS reflection coefficient. However, using large numbers of IRS elements induce large channel estimation overheads, which cause crucial problems in systems utilizing IRSs. In this study, we investigate the optimal pilot interval design for IRS-aided communication systems to reduce the channel estimation overh

This study investigates the sharing of an intelligent reflecting surface (IRS) among multi-mobile network operators (MMNOs). IRSs are an energy-efficient option to manipulate electromagnetic waves; however, constraints on their installation can result in competition among MMNOs, increasing redundancy and energy consumption. A promising solution is to share the IRS among MMNOs; however, negotiating among MMNOs is challenging; thus, reaching a consensus on different IRS control requirements of eac

Intelligent reflecting surfaces (IRS) can eliminate dead zones by providing alternative paths for radio signal propagation. However, in scenarios with many moving obstacles, the IRS aperture can be partially blocked, which can decrease the effective area and, consequently, reduce its gain. Although enlarging the IRS aperture can mitigate the effects of these obstructions, large IRSs can introduce inefficiencies because of location and cost constraints. Therefore, in this study, we propose a nove

An intelligent reflecting surface (IRS), which comprises numerous passive elements, is considered a promising technology for smart wireless communication. However, the passive characteristics of an IRS render the explicit estimation of the channel state information to appropriately adjust its reflection coefficient challenging. This study proposes a deep reinforcement learning-based algorithm that learns the precoding vector of the base station (BS) and the IRS phase shift from the wireless envi

In this study, we investigate the use of intelligent reflecting surfaces (IRSs) in multi-operator communication systems for 6G networks, focusing on sustainable and efficient resource management. This research is motivated by two critical challenges: limited coverage provided by mmWave frequencies and high infrastructure costs associated with current technologies. IRSs can help eliminate these issues because they can reflect electromagnetic waves to enhance signal propagation, thereby reducing b

This study proposes a passive beamforming strategy among different communication systems (CSs) that share an intelligent reflecting surface (IRS). The conventional beamforming strategy based on channel state information is unfeasible in a shared IRS scenario owing to the challenges in handling physical layer information among multiple CSse Thus, we propose a machine learning-based framework for IRS sharing systems to address this problem; the programmable radio environment is treated with IRS as

In recent years, Internet-wide scanning has attracted interest as a countermeasure against Internet-of-Things (IoT) security problems that can detect security holes. Despite the usefulness of Internet-wide scanning, it suffers from the problem of network congestion that occurs when numerous port scanning packets are used. Network congestion becomes increasingly severe in networks with limited bandwidth such as wireless networks. Therefore, port-scanning packets must be sent at long intervals whe

This study investigates a scenario where multiple mobile network operators (MNOs) share an intelligent reflecting surface (IRS), a technology that efficiently manipulates electromagnetic waves. Although IRSs are energy-efficient, constraints on their installation lead to competition among MNOs, increasing redundancy and energy consumption. In a multi-MNO environment, achieving fairness and optimizing performance is crucial. To address these challenges, this study proposes a cooperative passive b

Intelligent reflecting surfaces (IRS) can eliminate dead zones by creating alternative paths for radio propagation. However, in environments with numerous moving obstacles, the aperture of the IRS may become partially obstructed, reducing the effective area and consequently, the gain of the IRS. Although increasing the aperture size of the IRS can mitigate the impact of obstacles, the possible installation area is limited owing to location and cost constraints. Therefore, this study proposes a m

Interference management is a central bottleneck in dense multi-antenna wireless networks. In this study, we present a digital precoding-free hierarchical rate-splitting multiple access (HRSMA) architecture assisted by a stacked intelligent metasurface (SIM) to achieve high spectral efficiency and user fairness with reduced hardware complexity. In the proposed system, the base station performs only scalar power allocation, whereas a multi-layer SIM acts as a wave-domain processor that spatially s