Efficient channel estimation and beamforming in the upper mid-band spectrum pose a fundamental challenge for 6G base stations (BSs). This requires practical beam management techniques that account for the propagation characteristics of this frequency range and the increasing number of BS antennas. To address the channel estimation bottleneck in Frequency Division Duplex (FDD) systems, this paper proposes an integrated framework combining channel synthesis and AI-based beam management. We first introduce partial statistical reciprocity (PSR) by extending the concept of partial reciprocity into the statistical domain, enabling a novel model-based online channel synthesis method. The proposed approach uses uplink channel parameters collected during BS operation to generate synthetic downlink channels that reflect the spatial distribution of active user equipment (UE), enabling the creation of high-quality, site-specific datasets for AI training. Furthermore, we propose a joint optimization framework for scalable codebook design and downlink channel estimation. A U-Net Transformer model is trained to estimate full beam-domain channel state information (CSI) using a single snapshot of uplink CSI and feedback from codebook-based beam sweeping. The model iteratively updates the codebook via backpropagation to align with current UE distributions and channel conditions, while channel estimation is performed through forward inference. The proposed AI-based framework achieves accurate channel reconstruction with minimal feedback, offering a scalable and practical solution for 6G spatial multiple access in the upper mid-band FDD system.

In this paper, we investigate a multi-user downlink scheduling and power allocation strategy for urban air mobility (UAM) within a 6G non-terrestrial network (NTN) framework that integrates satellite and ground networks. We consider a system model involving multiple ground stations (GSs) and a single satellite, addressing the sum rate maximization problem with link-association, power, elevation angle, and minimum quality-of-service constraints. The proposed method initially segregates satellite-serviced users to reduce interference among the remaining GS-serviced users, taking into account the locations and movements of those UAMs. Subsequently, using a graph-theoretical approach, we convert the GS link association problem into a minimum-cost maximum-flow problem. In this process, we employ an analytical method involving polynomial approximations or a numerical method using integral approximation through the sum of time-sampled parameters. We then address the non-convex power allocation problem for scheduled links through iterative algorithms. The proposed scheduling and power allocation algorithms effectively manage interference in multi-UAM and multi-GS environments, and their performance is validated through extensive simulation results. Our study provides a comprehensive framework and strategy for efficient downlink transmission in future UAM operations, paving the way for novel applications in 6G NTN.

Free-space optical (FSO) communication is a promising solution to support wireless backhaul links in emerging 6G non-terrestrial networks. At the link level, pointing errors in FSO links can significantly impact capacity, making accurate modeling of these errors essential for both assessing and enhancing communication performance. In this paper, we introduce a novel model for FSO pointing errors in autonomous aerial vehicles (UAVs) that incorporates three-dimensional (3D) jitter, including roll, pitch, and yaw angle jittering. We derive a probability density function for the pointing error angle based on the relative position and posture of the UAV to the ground station. This model is then integrated into a trajectory optimization problem designed to maximize energy efficiency while meeting constraints on speed, acceleration, and elevation angle. Our proposed optimization method significantly improves energy efficiency by adjusting the UAV’s flight trajectory to minimize exposure to directions highly affected by jitter. The simulation results emphasize the importance of using UAV-specific 3D jitter models in achieving accurate performance measurements and effective system optimization in FSO communication networks. Using our generalized model, the optimized trajectories achieve up to 11.8% higher energy efficiency compared to those derived from conventional Gaussian pointing error models.

Full-duplex (FD) technology is gaining popularity for integration into a wide range of wireless networks due to its demonstrated potential in recent studies. In contrast to half-duplex (HD) technology, the implementation of FD in networks necessitates considering internode interference (INI) from various network perspectives. When deploying FD technology in networks, several critical factors must be taken into account. These include self-interference (SI) and the requisite SI cancellation (SIC) processes, as well as the selection of multiple user equipment (UE) per time slot. In addition, INI, including cross-link interference (CLI) and intercell interference (ICI), becomes a crucial issue during concurrent uplink (UL) and downlink (DL) transmission and reception, similar to SI. Since most INIs are challenging to eliminate, a comprehensive investigation that covers radio resource control (RRC), medium access control (MAC), and the physical (PHY) layer is essential in the context of FD network design, rather than focusing on individual network layers and types. This article covers state-of-the-art studies, including protocols and documents from the third-generation partnership project (3GPP) for FD, MAC protocol, user scheduling, and CLI handling. The methods are also compared through a network-level system simulation based on 3-D ray tracing.