Non-Terrestrial Networks (NTNs) play a critical role in ensuring uninterrupted communication services during emergencies. However, in dense urban environments, complex propagation conditions, such as obstructions from buildings, significantly degrade the link performance between satellites and ground users. To address this issue, this study proposes the integration of Drone Base Stations (DBSs) as the aerial segment and Reconfigurable Intelligent Surfaces (RISs) as the ground segment within satellite communication networks, forming a space-air-ground integrated network (SAGIN). DBSs leverage their flexibility and ability to transmit via line-of-sight (LoS), act as wireless relay stations, effectively enhancing the satellite signal transmission and improving the quality of service (QoS). Meanwhile, RIS significantly enhances the strength of reflected signals by dynamically adjusting the amplitudes and the phases, enabling stable and efficient communication networks. This study employs a deep reinforcement learning (DRL) techniques to optimize the 3D DBS position and RIS phase configurations, based on the dynamic spatial distribution of ground users. Simulation results demonstrate that this approach significantly improves network throughput and communication efficiency, providing essential theoretical foundations and technical references for achieving reliable and efficient SAGIN systems.

The marine IoT (MIoT) has experienced widespread adoption in the maritime industry; however, its communication infrastructure faces significant challenges due to a lack of terrestrial networks and the dynamic nature of oceanic environments. In response, low-Earth orbit (LEO) satellites have been suggested as an alternative solution. However, LEO satellites also face limitations, including coverage gaps due to the power constraints of MIoT devices (MIoTDs) and the curvature of the Earth, which impede continuous connectivity. To address these challenges, integrating uncrewed aerial vehicles (UAVs) as aerial base stations between LEO satellites and MIoTDs has gained attention as an effective solution to enhance communication. Managing UAV mobility and ensuring reliable communication requires autonomous 3-D trajectory optimization and dynamic resource allocation. This article presents an integrated LEO and multi-UAV-enabled MIoT network designed to maximize communication coverage, system throughput, and fairness. To achieve this, we formulate a joint UAV trajectories and resource allocation (UTCRA) optimization problem. To address the UTCRA problem, we propose a two-stage learning approach: 1) Gaussian mixture models (GMMs)-based clustering is initially applied to dynamically cluster MIoTDs under different UAVs, ensuring a balanced distribution of user loads among UAVs. 2) A federated multiagent deep deterministic policy gradient (FL-MADDPG) algorithm is employed to continuously optimize UTCRA, thereby enhancing communication efficiency. Simulation results demonstrate that FL-MADDPG with GMM significantly enhances communication coverage, average throughput, and fairness. It outperforms benchmark algorithms across all evaluated metrics, highlighting its strong potential as a method for next-generation MIoT networks.

In this paper, we consider a non-ideal successive interference cancellation receiver-based imperfect non-orthogonal multiple access (NOMA) schemes whose performance is limited by three factors: 1) power disparity & sensitivity constraints; 2) intra-cluster interference (ICRI); and 3) intercell-interference (ICI). By quantifying the residual interference with a fractional error factor (FEF), we show that NOMA cannot always perform better than orthogonal multiple access (OMA) especially under certain receiver sensitivity and FEF levels. Assuming the existence of an offline/online ICI management scheme, the proposed solution accounts for the ICI which is shown to deteriorate the NOMA performance particularly when it becomes significant compared to the ICRI. Then, a distributed cluster formation (CF) and power-bandwidth allocation (PBA) approach are proposed for downlink heterogeneous networks (HetNets) operating on the imperfect NOMA. We develop a hierarchically distributed solution methodology, where BSs independently form clusters and distributively determine the power-bandwidth allowance of each cluster. A generic CF scheme is obtained by creating a multi-partite graph via partitioning user equipment with respect to their channel gains since NOMA performance is primarily determined by the channel gain disparity of cluster members. A sequential weighted bi-partite matching method is proposed for solving the resulted weighted multi-partite matching problem. Thereafter, we present a hierarchically distributed PBA approach which consists of the primary master, secondary masters, and slave problems. For a given cluster power and bandwidth pair, optimal power allocations and Lagrange multipliers of slave problems are derived in closed-form. While power allowance of clusters is updated by the secondary masters based on dual variables of slave problems, bandwidth proportions of clusters are iteratively allocated by the primary master as per the utility achieved by the secondary masters at the previous iteration. Finally, the proposed CF and PBA approaches under the operation of imperfect NOMA are investigated and compared to the OMA scheme by extensive simulations results in DL-HetNets.

In this paper, we propose a distributed cluster formation (CF) and resource allocation (RA) framework for non-ideal non-orthogonal multiple access (NOMA) schemes in heterogeneous networks. The imperfection of the underlying NOMA scheme is due to the receiver sensitivity and interference residue from non-ideal successive interference cancellation (SIC), which is generally characterized by a fractional error factor (FEF). Our analytical findings first show that several factors have a significant impact on the achievable NOMA gain. Then, we investigate fundamental limits on NOMA cluster size as a function of FEF levels, cluster bandwidth, and quality of service (QoS) demands of user equipments (TIEs). Thereafter, a clustering algorithm is developed by taking feasible cluster size and channel gain disparity of TIEs into account. Finally, we develop a distributed α-fair RA framework where α governs the tradeoff between maximum throughput and proportional fairness objectives. Based on the derived closed-form optimal power levels, the proposed distributed solution iteratively updates bandwidths, clusters, and TIEs' transmission powers. Numerical results demonstrate that proposed solutions deliver a higher spectral and energy efficiency than traditionally adopted basic NOMA cluster size of two. We also show that an imperfect NOMA cannot always provide better performance than orthogonal multiple access under certain conditions. Finally, our numerical investigations reveal that NOMA gain is maximized under downlink/uplink decoupled (DTIDe) TIE association.