In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

Integrated satellite-aerial-terrestrial relay networks (ISATRNs) have been considered as a promising architecture for next-generation networks, where high altitude platform (HAP) is pivotal in these integrated networks. In this paper, we introduce a novel model for HAP-based ISATRNs with mixed FSO/RF transmission mode, which incorporates unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) to dynamically reconfigure the propagation environment and fulfill the massive access requirements of ground users. Our aim is to maximize the system ergodic rate by joint optimizing the UAV trajectory, RIS phase shift, and active transmit beamforming matrix under the constraint of UAV energy consumption. To solve this intractable problem, a deep reinforcement learning (DRL)-based energy efficient optimization scheme by utilizing an improved long short-term memory (LSTM)-double deep Q-network (DDQN) framework is proposed. Numerical results demonstrate the superiority of our proposed algorithm over the traditional DDQN algorithm, on single-step exploration average reward values and other evaluation metrics.

To overcome the long transmission distances and limited spectrum resources issues, both the space-aerial-terrestrial relay networks (SATRNs) and hybrid-free space optical/ radio frequency (FSO/RF) mode have attracted significant attentions. Specifically, high-altitude platform (HAP) and unmanned aerial vehicle (UAV) are employed in this paper to enhance the transmission reliability and improve the resource utilization along with the reconfigurable intelligent surface (RIS) and rate splitting multiple access (RSMA) techniques. Besides, we propose a novel access-free federated deep reinforcement learning (DRL) framework, which exploits the privacy-preserving security features of federated learning (FL) and DRL, to optimize active beamforming vectors, RIS reflection coefficients, UAV trajectory, and power splitting ratio. The learning process of the algorithm is performed locally which significantly reduces the computational overhead compared to traditional algorithms. Simulation results demonstrate that the proposed federated DRL-aided framework achieves higher energy efficiency compared to the reference schemes.

Satellite-aerial-ground integrated networks (SAGINs) have emerged as a promising infrastructure for next-generation wireless networks. Considering the fading effects of obstacles over longer transmission distances, in this paper, we equip reconfigurable intelligent surfaces (RIS) on the unmanned aerial vehicle (UAV) to reflect the uplink signals from the ground vehicle transmitter (VT) to high altitude platform (HAP). The HAP then acts as a relay to forward the signals to satellites through hybrid free space optics (FSO) links. This paper focuses on optimizing the system ergodic sum rate in SAGINs. To address the highdimensional non-convex optimization problems, an asymmetric long short-term memory (LSTM)-deep deterministic policy gradient (DDPG) algorithm, called AL-DDPG, is proposed. The ALDDPG algorithm leverages hidden states in partially observable Markov states using a memory inference method to address the training convergence problem. Numerical results demonstrate the superiority of the AL-DDPG algorithm, with a 16 in the system sum rate compared to the traditional optimization algorithm. Besides, we provide insights into the impacts of various factors on system performance.

Satellite and high altitude platform are considered as the key parts of the next generation networks, and specifically for the internet-of-things networks, which are utilized to provide unobstructed connections and massive user access for terrestrial networks. In this paper, we investigate the power allocation and system performance of non-orthogonal multiple access (NOMA)-enabled integrated satellite-high altitude platform-terrestrial systems under practical limitations. Particularly, a practical system model is established by considering the channel estimation errors and imperfect successive interference cancellation at the receiver. To achieve the different quality of service requirements among multiple served users, we propose a novel NOMA-based power allocation scheme. In addition, the analytical and asymptotic expressions for the outage probability of NOMA users are obtained to verify the proposed scheme as well as the ergodic capacity. Finally, numerical results are corroborated with Monte Carlo simulations, which show the correctness of our analytical results, and the benefits of our proposed scheme. The proposed scheme indicates that the HAP relay link plays a significant role in the system performance.

Satellite-high altitude platform (HAP)-terrestrial networks have been considered as an indispensable infrastructure of next-generation networks because they can offer massive access service with high throughput and broad coverage connections. Meanwhile, non-orthogonal multiple access (NOMA) and two-way relaying techniques are considered as potential technologies to enhance the spectrum efficiency and connectivity. Thus, in this paper, the performance of two-way satellite-HAP-terrestrial networks with NOMA, which are subject to imperfect channel state information and successive interference cancellation, is investigated. Closed-form and asymptotic expressions for the outage probability, and ergodic capacity are derived to verify the impact of the system and channel parameters on the considered network. Simulation results indicate that the optimization power allocation factor is independent of the channel fading.