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The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet...
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... and communication resource optimization are solved by the convex optimization algorithm, and the taskoffloading strategy is obtained by solving P6. (iii) Proposed: the optimization algorithm proposed in this paper. Figure 2 verifies the convergence of the proposed algorithm in this paper. We plot two curves for the number of ground devices 20 and 40. ...
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... plot two curves for the number of ground devices 20 and 40. From Figure 2, we found that the proposed iterative algorithm can quickly converge to a stable solution, and this verifies the convergence properties of our proposed algorithm. So, the algorithm we proposed is an effective algorithm with rapid convergence. ...
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Mobile edge computing (MEC) is considered as an effective technology to enhance the storage and computation capability of smart power sensors (SPSs) in smart grid networks. The MEC server is composed of multiple virtual machines (VMs) with powerful computation capability, and each VM can process multiple tasks independently, which cannot be ignored...
Citations
... Ground devices access HAP drones through the C-band. HAP drones are directly connected to LEO satellites through the Ka-band to achieve high-rate traffic backhaul [26]. ...
The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic–drone matching and LEO–drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach.
... The problem of weight energy minimization considers transmission power control, computing resource allocation, and task offloading decisions, proposing a BCD method to iteratively solve the interconnected subproblems [45] . ...
... Performance metrics Solution method [45] Minimize the weighted total energy consumption LEO, HAPs, ground nodes Weighted energy consumption of the system and maximum task tolerance delay for ground devices Proposed a low-complexity iterative algorithm using BCD to solve a MINLP problem by converting it into convex form and applying a convex optimization algorithm for efficient solution finding [5] Maximize the system capacity LEO, UAVs, users System capacity, which is influenced by smart devices' connection scheduling, power control, and UAV trajectory An efficient iterative algorithm was proposed to solve a mixed-integer nonconvex optimization problem. The algorithm employed variable substitution, successive convex optimization techniques, and the block coordinate descent algorithm [46] Maximize the network energy efficiency LEO, hot air balloons acting as relays, inter-satellite laser links ...
The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN, offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods, particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.
