The introduction of satellite communications will play a key role in next generation communications. However, satellite systems are faced with the challenge of efficiently managing the satellite spectrum resource when supporting the communication needs of massive Internet-of-Things (IoT) devices, which is expected to growth explosively in scale of deployment. To address this issue, we propose a blockchain-based satellite spectrum resource optimization scheme. The scheme is based on a market-driven spectrum trading technique to maximize the benefit of the satellite systems, hence the utilization of the spectrum resource. In specific, the proposed spectrum trading protocol focuses on the heterogeneity of LEO satellite spectrum, which covers a huge band range with varying transmission qualities, by allowing a price differentiation between different spectrum ranges. As a result, different terrestrial IoT systems may select their preferred spectrum range, and price, according to their own application requirements and budgets. Furthermore, data integrity is needed to ensure the proper functioning of the spectrum trading process. In the proposed scheme, we adopt a blockchain mechanism for facilitating spectrum trading with enhanced security, with all essential transaction-related data to be stored in a blockchain, which is used as a distributed and immutable data store for all records of the trading activities. Due to the limited computing power of terrestrial IoT device terminals, the blockchain is based on a Delegated Proof
Internet-of-Vehicles (IoV) plays an important part of Intelligent Transportation Systems, and is widely regarded as one of the most strategic applications in smart cities development. Next generation wireless network is especially crucial for meeting the connectivity and bandwidth demands of IoVs. Smart spectrum resource management has received much attention of the research community as it is believed to be a promising approach for solving the spectrum resource challenge of IoV and Intelligent Transportation Systems. In this article, we propose a smart spectrum optimization technique based on a deep learning method for user mobility prediction. For this purpose, based on the Exploration and Preferential Return (EPR) model which can be used to investigate the movement trend and aggregation behavior of the target, we adopt the D-Exploration and Preferential Return (D-EPR) model as a deep learning technique to train a Long-Short Term Memory (LSTM) recurrent neural network (RNN) in order to predict the future locations of IoV nodes. With predicted user’s mobility, a graph theoretic algorithm is then applied to achieve spectrum reuse and optimization. Besides, our proposed deep-learningbased user mobility prediction is able to identify the user position. This paper then compares the performances of mobility prediction by traditional method and our proposal. The outcomes of spectrum efficiency and network capacity are also provided to show the effectiveness of the proposed solution.
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