Low Earth Orbit (LEO) satellite networks are expected to play a central role in future 6G Non-Terrestrial Networks (NTN) envisioned by the ITU IMT-2030 framework. Efficient video delivery over such networks remains challenging because of rapid visibility changes and strong elevation-dependent variations in link capacity. Motivated by the need for adaptive and energy-efficient operation in emerging 6G NTN architectures, this paper presents a preliminary study on elevation-aware video prefetching in LEO networks. A controlled simulation framework emulating stress-test link dynamics is used to evaluate three algorithms: popularity-based (B1), deadline-aware (B2), and a proposed Elevation-Aware Deadline Prefetching (B3) method that adapts prefetch timing to elevation-dependent link quality, along with a no-prefetch baseline (B0). Results show that the proposed elevation-aware approach achieves Quality of Experience (QoE) equivalent to the deadline-aware method while improving link-quality efficiency by 2.9%, reducing low-elevation usage by 3.0 percentage points, and performing 921 fewer operations. These preliminary findings indicate that incorporating elevation information in prefetch scheduling can improve resource utilization and efficiency without compromising user experience, offering early insights for ongoing 6G NTN research focused on adaptive and energy-conscious communication mechanisms.
Multi-Robot Systems (MRS) are becoming increasingly vital across various domains. In many practical applications, inadequate infrastructure necessitates decentralized operations, making inter-robot data sharing a critical challenge. As the scale of the system grows, both in terms of the number of robots and the operational area, achieving effective system-wide (global) data sharing becomes more difficult, which in turn affects the efficiency of task allocation in large-scale MRS. Emerging 5G network technologies provide facilities for localized data sharing, which occurs much faster compared to global ones. In this work, we study multi-robot task allocation under 5G-assisted localized inter-robot communication. Specifically, we provide a comprehensive comparison of three distinct well-known approaches. Our findings reveal that greedy selection-based strategies perform poorly when information availability is limited. Interestingly, task allocation strategies based on regret-maximization heuristics perform better under restricted information conditions than with full system visibility. Furthermore, our results show that a multi-agent reinforcement learning (MARL) strategy outperforms all other approaches, exhibiting minimal performance degradation even with significantly constrained information sharing.
In this paper, a low-cost, 1-bit reconfigurable intelligent surface (RIS) operating at 3.5 GHz is designed to enhance wireless signal coverage in sub-6 GHz frequency ranges. Each RIS unit cell has two square-shaped patches linked by an SMP 1340-040LF PIN diode, which is placed on a dual-layer FR4 epoxy board. The structure provides a phase shift of 180 ° ± 30 °, depending on the diode’s state, while maintaining a reflection loss of below 2.15 dB over a 630 MHz bandwidth. A 20 × 10 element array is created using column-wise diode biasing to support one-dimensional beam steering. Radial stubs are added to the biasing circuit to prevent interference between the DC control signals and the RF signals. The simulated far-field patterns demonstrate that directional beams can be formed at angles of ±15°, ±30°, and ±45°, confirming the array’s effectiveness in adaptable environments. This RIS design could significantly contribute to the development of affordable 5G and future communication technologies.
We propose a novel sparse matrix precoding (SMP) scheme to increase the spectral efficiency (SE) of MIMO systems while leveraging the belief propagation decoding on a sparse bipartite graph at the receiver to obtain near-optimal performance with low computational complexity. Furthermore, conventional MIMO precoding/ equalization has been targeted toward channel compensation. Our proposed SMP scheme is additionally aimed at increasing the SE while lowering the MIMO receiver complexity. Unlike the conventional approach of enhancing SE in bandwidth-constrained MIMO systems by employing higher-order modulation, our proposed method achieves higher SE through sparse matrix precoding applied to a lower-order modulation at the transmitter. This approach offers a key advantage: the resulting MIMO receiver is computationally more efficient. This is because our scheme is designed to enable a message-passing algorithm (on the log-likelihood ratios (LLRs) of the lower-order modulation) on the bipartite graph representing the sparse precoding matrix, rather than directly decoding a higher-order constellation. We provide information-theoretic analysis that shows the conditions under which the SMP-MIMO provides the same channel capacity as the conventional MIMO. Several simulation results showing MIMO channel capacity and bit-error-rate (BER) provide proof of the proposed concept.
Structural Health Monitoring (SHM) ensures the safety and longevity of civil infrastructure. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have transformed SHM by enabling automated, scalable solutions for damage detection and assessment. This paper presents a synthesis of AI-driven SHM methods—from vision-based surface damage detection and vibration analysis to physics-informed models and digital twins. We explore the integration of CNNs, RNNs, GANs, and transformers with non-destructive testing (NDT), UAV-based monitoring, and 3D reconstruction. Emphasis is placed on real-time defect localization, sensor fault mitigation, and data recovery in operational environments. Despite progress, challenges like data scarcity, environmental variability, and generalizability remain. We propose a roadmap using hybrid AI-physics models and autonomous SHM systems to enhance infrastructure resilience.
