To enable a globally integrated non-terrestrial network (NTN), the interconnection of heterogeneous low-earth orbit (LEO) satellite constellations via geostationary (GEO) relays is paramount. A significant challenge in this architecture lies in the dynamic allocation of hybrid free-space optical (FSO) and radio frequency (RF) links for the GEO-LEO interconnection. Conventional heuristic approaches that prioritize either instantaneous traffic demand or link visibility would be inadequate, as they fail to balance these conflicting metrics, resulting in performance bottlenecks and inefficient resource utilization. To resolve this allocation challenge, we propose a novel two-stage quasi-optimal control framework that maximizes the network-wide traffic accommodation rate. Our approach employs Binary Particle Swarm Optimization (BPSO) to solve the combinatorial FSO/RF link assignment problem, followed by convex optimization for RF bandwidth allocation. Simulation results demonstrate that our method consistently outperforms conventional approaches across various network loads, significantly improving the traffic accommodation rate and overall data throughput, thereby validating its effectiveness for dynamic resource management in hybrid satellite networks.

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 separates users and mitigates interference via nonlinear wavefront reconfiguration. This design eliminates the need for digital or hybrid precoding, drastically reducing the baseband computations. A joint optimization problem is formulated to maximize the minimum user rate by jointly optimizing SIM phase shifts, power allocation, and user grouping. To efficiently solve the resulting non-convex problem, an alternating optimization algorithm is developed, combining simultaneous perturbation stochastic approximation (SPSA) for SIM configuration and power control with clustering-based grouping refinement. Simulation results demonstrate that the proposed SIM-aided HRSMA achieves substantial gains in both spectral efficiency and fairness compared to hybrid beamforming and non-precoding baselines. Specifically, SIM-aided HRSMA attains comparable or superior minimum rates with significantly fewer active antennas by exploiting the additional wave-domain degrees of freedom facilitated using multi-layer SIMs. These findings highlight the potential of SIM-aided HRSMA as a low-cost, energy-efficient, and scalable solution for beyond-6G networks.

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 beamforming strategy for maximizing the minimum achievable rate among users of different MNOs. An algorithm based on the projected gradient ascent (PGA) method is introduced to resolve the inherent complexity of this max-min fairness problem. Numerical evaluations demonstrate that the proposed IRS-sharing approach outperforms traditional single MNO-specific and sequential IRS control schemes. These findings highlight the potential benefits of IRS sharing, underscoring its practical value in enhancing network efficiency and fairness in multi-operator scenarios.

The use cases of satellite communications are rapidly expanding towards 6 G technology. Long propagation distances and dynamic changes in the network topology create an unstable environment with greater delays and disruptions than those of terrestrial networks. Delay/disruption-tolerant networks (DTN) have been developed to address these challenges. Since the nearearth satellite network is a scheduled network, contact plans can be created beforhand. Contact graph routing (CGR) is a contact-plan-based method used to determine the shortest path to deliver a bundle, a data unit of a DTN, to its destination. However, the conventional contact graph-based bundle transmission control methods do not consider the transfer order or bundle generation from multiple sources, such as observation satellites, ground, and marine users. Therefore, the bundle delivery ratio will decrease in future satellite networks, in which traffic of various sizes and acceptable delays are expected to flow in. In this study, we propose a bundle transmission control method for a multi-source DTN that considers the bundle variety. The simulation evaluation results demonstrated that the proposed method achieved a higher bundle delivery ratio than the conventional methods.

The scale of data centers has increased significantly in recent years, with their energy demands adversely impacting the environment. Consequently, distributed green data centers, equipped powered renewable energy, have gained considerable attention. However, conventional optical fibers installations for such data centers incur high costs and face locations constraints. Therefore, this study explores the use of optical satellite communications for distributed green data centers. Efficient task allocation is crucial to minimize service delays. However, task transmission time depends on satellite-ground link performance, while processing time is affected by the energy at data centers. Additionally, the non-linear relationship between the server per-formance and power consumption highlights the inefficiency, task allocation without future-aware considerations. To address this, we propose a method for task allocation and processing performance control that minimizes service delays through predictions of task generation, link performance, and power generation. In the proposed method, we formulate an optimization problem based on prediction data and find its solution by exploration. Simulations demonstrate that the proposed method significantly reduces tasks transmission and processing times.

In the context of a network with multiple access points (APs) utilizing coordinated orthogonal frequency division multiple access (C-OFDMA), a channel unfairness among APs arises when the APs participating in the coordination decrease the available channels accessible to non-coordinated nodes. This study introduces a solution in the form of C-OFDMA, in which coordinated sides prioritize channels that may not be used by the non-coordinated sides for resource allocation to STAs that are overlapped within the range of the non-coordinated sides. Consequently, conflicts between non-coordinated and coordinated APs are minimized and non-coordinated nodes cannot have their number of available channels decreased through carrier sense. This enhances channel fairness among all APs, thus facilitates the effective implementation of C-OFDMA. Moreover it maintains system throughput in C-OFDMA without RU reuse. Based on simulations, in systems where non-coordinated and coordinated nodes are in close proximity, the proposed method enhances these throughput and fairness compared with conventional C-OFDMA approaches. This allows C-OFDMA to be efficiently utilized even in dense wireless local area networks, which are increasing in popularity.

In recent wireless local area networks (WLANs), high-density access points (APs) have been considered for high-speed communications. However, networks with a high-density of APs can cause interference between basic service sets (BSSs). To address this issue, the Institution of Electrical and Electronic Engineers (IEEE) 802.11bn Task Group is considering to introduce multi-AP coordination (MAPC), in which multiple APs work together. Each coordination function has different performance in terms of instantaneous throughput and transmission overhead, hence coordination function selection may improve performance. In this work, we evaluate the performance of coordination function selection. We propose models for estimating the instantaneous throughput and transmission overhead of each coordination function. Simulations show that coordination function selection can improve performance compared with the case in which all APs use a single coordination function. Furthermore, we show the optimal coordination function pattern is considered to be related to the distance between APs and stations in the BSSs. Coordination function selection contributes to the improvement of MAPC performance in various environments.

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 an application programming interface that can be called from each CS. The effectiveness of the proposed machine learning-based passive beamforming design was validated through experiments. The number of transmission sites and the diversity of transmitting site positions influence the effectiveness of the IRS sharing scenario.

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 environment to address this problem. We develop a beam-pattern-based learning framework for this algorithm that indirectly maps the wireless environment to the phase shift to manage the large state-action space caused by the multiple elements of the BS and IRS. Based on the simulation results, the proposed algorithm can learn from the environment and establish a transmission strategy that improves the user's transmission rate. Furthermore, the results validate that the proposed algorithm based on the beam pattern learning framework is more efficient and scalable to the number of IRS elements compared to the method that directly builds a mapping to the phase shift.

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 the IRS–user association is fixed during the tracking period, the dynamic variations in their link status caused by user mobility degrade the system performance if the association is decided without prior planning. Therefore, this paper proposes an IRS–user association strategy considering user mobility for IRS-aided multibeam transmission systems. Contrary to prior works, our association strategy aims to optimize the long-term performance of systems in terms of capacity and reliability. The proposed strategy minimized performance degradation even under drastic fluctuations of link conditions, thereby reducing channel estimation overhead because both the IRS and user can be associated for long periods with low performance degradation.

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, a higher scan rate is required due to the excessive time consumption of low rate scanning. In this article, we propose a method for setting an optimal scan rate which is as high as possible without imposing congestion on a WLAN. To address this problem, we construct a model for quantifying the impact of scanning traffic on IoT data communication in a target network and demonstrate how scanning packet delay elicits the congestion of the target network. The validity of our model is evaluated and is corroborated by numerical analysis. In addition, numerical analysis results demonstrate the effectiveness of the proposed scan rate optimizing method in terms of low network congestion and a high scanning rate.