CII: Novel CSI-RS Metric for Joint Precoder and RIS Reporting in Multi-User NextG Networks

While reconfigurable intelligent surfaces (RISs) are among the key enablers for next-generation (NextG) wireless networks, efficient feedback reporting for joint base station (BS) precoding and passive RIS configuration remains a major challenge due to the associated signaling overhead. By extending the standard-compliant channel state information reference signal framework, this paper introduces a novel channel information indicator (CII) that jointly represents the active BS precoding matrix and passive RIS configuration within a single feedback metric for multi-user multiple-input single-output systems. Simulation results demonstrate that the proposed unified feedback framework significantly reduces uplink signaling overhead compared with conventional disjoint reporting schemes. Furthermore, despite only a modest increase in the feedback payload, the proposed CII-based scheme outperforms conventional precoding matrix indicator approaches in terms of system performance, offering a practical and standards-compatible solution for RIS integration in NextG wireless networks.

Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach

The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strategic reasoning required to quickly adapt to dynamic Integrated Terrestrial and Non-Terrestrial Networks (ITNTNs). Conversely, Large Language Models (LLMs) excel at semantic reasoning but suffer from high inference latency, rendering them unsuitable for real-time aerodynamic control. To bridge this gap, we propose a novel Hierarchical LLM-driven control framework. A massive cloud-based LLM deployed on a High-Altitude Platform Station (HAPS) manages slow-timescale global load balancing, while lightweight edge-LLMs on individual UAVs translate local observations into tactical sub-goals. These sub-goals guide a fast-timescale physical DRL controller to execute collision-free, handover-aware trajectories. Simulation results demonstrate that our agentic architecture significantly reduces collision rates and improves aggregate system throughput compared to existing baselines.

HAPS-Complemented Terrestrial Networks

We consider a downlink multicell multiple-input multiple-output (MIMO) system in an urban region, with a focus on improving the capacity of cell-edge user equipments (UEs). These UEs typically experience lower rates than near UEs because of shadowing, path loss, and inter-cell interference (ICI). To address this issue, we integrate a high-altitude platform station (HAPS) with the terrestrial network as a relay for edge-UE transmissions. We assume that the HAPS operates in full-duplex (FD) mode and exploits its large physical size to enhance passive self-interference (SI) suppression by separating its transmit and receive antennas. In the proposed scheme, each terrestrial base station (BS) forwards edge-UE data to the FD-HAPS, which then relays the data to the intended edge UEs. To design beams at both BSs and HAPS, we formulate a sum-rate maximization problem for under total transmit-power and minimum quality-of-service (QoS) constraints. To solve the resulting non-convex problem, we develop a centralized algorithm based on successive convex approximation (SCA) and alternating optimization (AO) for fast convergence. Simulation results show that relaying information via FD-HAPS significantly improves the capacity of cell-edge UEs compared with a terrestrial-only network.

Constrained Capacity Analysis for Faster-than-Nyquist Signaling

This paper studies the constrained-capacity for precoded faster-than-Nyquist (FTN) signaling with finite-alphabet inputs. Despite the promise of accelerated transmission, the fundamental rate limit of precoded FTN signaling under practical finite-alphabet constraints remains unclear. By introducing cyclic prefix (CP) and cyclic suffix (CS), the FTN channel is decomposed into a set of parallel eigenchannels by the discrete Fourier transform (DFT) matrix, based on which the constrained capacity is derived. The results demonstrate that time acceleration can improve spectral efficiency over Nyquist signaling even when a fixed modulation order is employed. Moreover, in the low and moderate signal-to-noise ratio (SNR) regimes, a smaller constellation combined with stronger time acceleration can outperform a larger constellation with weaker acceleration. Next, the asymptotic behavior of the constrained capacity is analyzed as the acceleration factor tends to zero under both fixed transmit-SNR and fixed receive-SNR definitions. It is shown that the constrained capacity for DFT-precoded FTN is fundamentally limited by the constellation size. In addition, the constrained capacity under channel mismatch is studied and a mismatched achievable information rate (AIR) formulation is developed to show the effects of practical constraints on the performance degradation. Finally, adaptive bit loading across eigenchannels is investigated to exploit the higher-quality eigenchannels.

A Holistic Link Budget Analysis for mmWave and THz Communications in Non-Terrestrial Networks

The non-terrestrial network (NTN) architecture has gained significant interest from the academia owing to its versatility and the ability to provide worldwide service. To achieve extremely high data rates in NTNs, as intended in the sixth-generation (6G) communication systems, millimeter wave (mmWave) and terahertz (THz) frequencies can be considered, enabling substantial bandwidth and data transmission capacity, which makes them highly suitable for NTN applications. However, these high-frequency signals suffer from significant propagation challenges, including atmospheric attenuation, pointing errors, and various environmental effects. Therefore, a comprehensive link budget analysis is essential to accurately assess the feasibility of mmWave/THz-based NTN systems. Existing studies in the literature often fail to fully capture certain frequency-, altitude-, and direction-dependent effects observed in mmWave/THz transmission or possible communication scenarios within the NTN architecture. In particular, while most prior works primarily focus on free-space loss or atmospheric attenuation, this study adopts a much more comprehensive approach. In this work, a detailed link budget analysis is conducted for mmWave/THz NTNs, considering free-space loss, atmospheric absorption, weather-induced effects, ionospheric disturbances, polarization mismatches, feeder losses, antenna and circuitry constraints, fading, pointing errors, and non-white noise characteristics. The results have revealed that the multi-layer structure of the NTN architecture can help reducing the excessive loss levels to a certain level that can be tolerated by high-gain directional antennas/arrays, providing multi-gigabit links and making mmWave/THz NTNs feasible for 6G communication systems.

Pushing the Limits: Unlocking the Potential of Faster-than-Nyquist Signaling

Faster-than-Nyquist (FTN) signaling is gaining attention as a smart way to pack more data into limited spectrum by intentionally breaking the traditional symbol-spacing rules. This article takes a fresh look at FTN's potential to boost capacity, examining how performance varies across different acceleration factors and signal-to-noise ratio (SNR) definitions. Beyond the theory, we explore what it takes to make FTN work in practice, such as dealing with power amplifier constraints, managing high peak-to-average power, and designing practical coding strategies. We also highlight real-world issues like spectrum sharing, short-packet communication, and receiver complexity. With applications ranging from low-latency links to integrated sensing and satellite systems, FTN offers a compelling path forward for future wireless technologies.

RSMA Enabled Hierarchical UAV Networks with Non Linear Energy Harvesting: Outage Probability Analysis and UAV Placement Optimization

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil applications. Although multi-UAV systems offer greater efficiency and cost-effectiveness than single-UAV deployments, their implementation still faces several fundamental challenges that limit their reliability, sustainability, and scalability. The limited onboard energy restricts mission duration and communication continuity. Therefore, wireless energy harvesting (EH) emerges as a promising solution to overcome this limitation. However, terrestrial energy sources experience path loss, making EH from surrounding UAVs more sustainable. Moreover, rate-splitting multiple access (RSMA) remains insufficiently explored in hierarchical UAV networks under hardware impairments (HWI) and imperfect channel state information (ICSI). This paper proposes a hierarchical ad hoc UAV network with non-linear EH and RSMA to enhance both energy and cost efficiency, where UAVs harvest energy from surrounding UAVs. For a practical scenario, we consider the effect of HWI and ICSI in our proposed system. To the best of the authors knowledge, this study is the first to investigate such a scenario in the literature. The outage probability expressions for ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-$m$ fading channels while considering practical constraints such as HWI, ICSI, and non-linear EH. Additionally, approximate outage probability expressions are derived for high transmit power regimes. Subsequently, we formulate two optimization problems to enhance reliability and performance. Our findings indicate that the proposed system outperforms all benchmarks in terms of outage probability.

Rethinking NB-IoT Downlink Synchronization for LEO-NTN: A Novel Overhead Reduction Method and Measurement-Based Evaluation

Narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is a key enabler for massive Internet of Things (IoT) in 6G, but in low Earth orbit (LEO) scenarios, large and time-varying Doppler shifts generate carrier frequency offset (CFO) beyond the correction range of standard user equipment (UE), making initial downlink synchronization a major bottleneck. This paper analyzes Doppler characteristics in realistic NB-IoT LEO scenarios, reviews Doppler mitigation strategies, and proposes a standard-compliant, low-overhead search-space optimization method for downlink acquisition. Results under realistic LEO conditions with real-time measurements show reduced acquisition overhead while maintaining synchronization reliability, supporting NB-IoT adaptation to 6G NTN deployment.

GNN-based Online Beamforming Design for HAPS-Assisted NTN

In terrestrial networks, especially in urban areas, cell-edge users often face significant capacity limitations due to high path loss, shadowing, and inter-cell interference (ICI). This paper proposes integrating a high-altitude platform station (HAPS) into terrestrial networks, where terrestrial base stations (BS) can alleviate these issues by relaying data intended for cell-edge users via HAPS, thereby leveraging line-of-sight (LoS) links. We formulate an energy-efficiency (EE) maximization problem to jointly design beamforming vectors at the BS and HAPS with the goal of improving cell-edge user performance. Since the resulting problem is non-convex, we develop an online optimization framework based on a graph neural networks (GNN), which effectively captures the network topology. Numerical results show that the proposed HAPS-assisted architecture improves network performance, particularly by increasing the 5th-percentile EE, thereby enhancing service for cell-edge users.

Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity

Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control and connectivity remains a fundamental challenge. In this paper, we study a multi-UAV system operating in an integrated terrestrial and non-terrestrial network (ITNTN) comprising terrestrial base stations and high-altitude platform stations (HAPS). We consider a three-dimensional (3D) aerial highway scenario where UAVs must adapt their motion to ensure collision avoidance, efficient traffic flow, and reliable communication under dynamic and partially observable conditions. We first model the problem as a hierarchical multi-objective partially observable Markov decision process (H-MO-POMDP), capturing the coupling between control and communication objectives. Based on this formulation, we propose a large language model (LLM)-driven hierarchical multi-rate control framework. At the global level, an LLM-based controller on the HAPS performs long-term planning for load balancing and handover decisions. At the local level, each UAV employs a hybrid controller that integrates a slow-timescale LLM for high-level spatial reasoning with a reinforcement learning agent for faster UAV-to-infrastructure (U2I) communication and motion control. We further develop a high-fidelity 3D simulation platform by integrating the gym-pybullet-drones environment with 3GPP-compliant RF/THz channel models. Numerical results demonstrate that the proposed framework significantly outperforms state-of-the-art baselines, achieving a 14% increase in transportation efficiency and a 25% improvement in telecommunication throughput. Additionally, it achieves a 23% reduction in physical collision rates, demonstrating strong handover stability and zero-shot generalization in dynamic scenarios.