In this paper, we explore a maritime 6G-enhanced satellite-air-ground integrated network (SAGIN) that incorporates a UAV-carried reconfigurable intelligent surface (UCR) relay, and low Earth orbit (LEO) satellites equipped with mobile edge computing (MEC) facilities. The system captures dynamic maritime conditions, including ultra-reliable low-latency communication (URLLC) user mobility and UCR movements across harbor environments. The primary objective is to minimize the total system cost by jointly optimizing task offloading decisions, bandwidth allocation, local computational resource distribution, transmission power control, and caching management, while satisfying strict latency and resource constraints. To address this, we formulate a mixed-integer nonlinear programming (MINLP) problem that captures the complexity of resource optimization in the maritime 6G-enhanced SAGIN. Two quantum-enhanced deep reinforcement learning algorithms, namely quantum-enhanced deep deterministic policy gradient (QEDDPG) and quantum-enhanced proximal policy optimization (QEPPO), are proposed to solve the formulated MINLP problem. Moreover, higher-order quantum feature encoding and quantum neural networks are utilized to accelerate learning and enhance decision-making. Simulation results demonstrate that QEDDPG and QEPPO significantly outperform conventional deep reinforcement learning methods by achieving lower system costs and more efficient resource allocation. These findings shows that the potential of quantum-driven reinforcement learning for enabling scalable, efficient, and intelligent resource management in future 6G-enhanced SAGINs.

Energy-efficient space–air–ground integrated networks (SAGINs) are vital for sustainable communications. This study presents an energy-aware SAGIN framework that utilizes a uncrewed aerial vehicle (UAV)-mounted mobile edge computing (MEC) platform enhanced by digital-twin technology, UAV energy harvesting via wireless power transfer, and a nano-satellite constellation with MEC facilities. We formulate a joint optimization problem for UAV trajectory planning, task offloading, computational resource allocation, and satellite load balancing as a mixed-integer nonlinear programming (MINLP) problem that minimizes the weighted system cost while satisfying energy and latency constraints. To address this complex problem, two quantum-driven deep reinforcement learning (QD-DRL) algorithms namely quantum-driven cost-effective advantage actor–critic (QD-CE-A2C) and quantum-driven cost-effective proximal policy optimization (QD-CE-PPO) are proposed. These algorithms employ angle encoding with learnable parameters and variational quantum neural networks to enhance policy exploration and accelerate convergence. Simulation results demonstrate that the proposed QD-DRL approaches achieve superior cost efficiency and ensure effective service to all access points within the defined mission duration. Moreover, QD-DRL approaches achieved higher cumulative rewards and faster convergence compared to classical DRL baselines. Consequently, the proposed frameworks provide a scalable and intelligent paradigm for cost-efficient resource management in future 6G-enabled SAGINs.

In this paper, we explore a 6G-enabled space-air-ground integrated network (SAGIN) framework that integrates ground communication hubs (CHs), a UAV with mobile edge computing (MEC) capabilities, and a constellation of low Earth orbit (LEO) nano-satellites. We formulate a joint optimization problem for UAV trajectory, task offloading, and satellite load balancing, modeled as a mixed-integer nonlinear programming (MINLP) problem. To solve this, we propose a quantum-enhanced advantage actor-critic (QEA2C) reinforcement learning algorithm that employs quantum neural networks and two quantum state encoding methods: amplitude encoding (AE) and higher-order encoding (HOE). Simulation results show that HOE achieves superior performance in terms of convergence speed, cumulative rewards, and learning efficiency, successfully serving all CHs with a well-optimized UAV trajectory. Meanwhile, AE achieves better cost minimization with lower resource consumption, making it a more practical option when computational efficiency is a priority. Moreover, these results highlight the trade-offs between learning performance and cost efficiency in quantum-enhanced decision-making for managing 6G-enabled SAGINs.

This paper introduces a network architecture that integrates the space-air-ground integrated network with mobile edge computing (MEC) and orbital edge computing to advance sixth-generation communication systems. The proposed system employs unmanned aerial vehicles equipped with reconfigurable intelligent surfaces and satellite-based MEC to optimize resource management in complex, dynamic environments. By efficiently managing resources such as bandwidth and computational power at both base stations and low Earth orbit satellites, while making offloading decisions, the system aims to minimize utility costs while meeting stringent performance requirements. We utilize a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) algorithm to solve the formulated nonlinear programming problem, enabling dynamic and adaptive resource management. The LSTM-enhanced DDPG improves convergence speed by 44.44 % compared to conventional DDPG, significantly enhancing cost efficiency. Simulation results validate the robustness of the proposed method against state-of-the-art approaches.

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments.