LEO Satellite-Enabled Networks: A Privacy-Preserving Framework for Spectrum Pricing and Power Control Optimization

Low Earth orbit (LEO) satellite systems are receiving increasing attention as they provide extensive global coverage. Secure and efficient management of limited spectrum bands and power resources are crucial for controlling operational costs and ensuring reliable communication in LEO satellite systems. However, spectrum pricing and power control optimization are challenging tasks. First, dynamic pricing is needed for leasing idle satellite spectrum to terrestrial users, as it must consider user mobility and real-time demand changes. Additionally, there is a trust concern that when utilizing the leased spectrum, terrestrial users may maliciously exceed limited transmit power to improve the quality of service (QoS). Moreover, users' privacy should be protected because the data collected by satellites often contain sensitive information such as location, budget, and QoS needs. In this paper, we propose a hybrid spectrum pricing and power control framework for LEO satellite-enabled networks to mitigate the above concerns by combining blockchain technology and Federated Learning (FL). We first design a local deep reinforcement learning algorithm for LEO satellite systems to learn a revenue-maximizing pricing strategy and power control scheme. Subsequently, these individual agents collaborate to establish an FL system without sharing their sensitive raw data. We also propose a reputation-based blockchain used in the global model aggregation phase to further enhance the traceability of the network and guarantee the trust. We conduct simulation tests to evaluate the efficacy of the proposed scheme, and our results show its capability to efficiently find the maximum revenue scheme for LEO satellite systems while preserving the privacy of each participating agent in an auditable mode.