In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.

中文翻译：

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利用区块链和移动边缘计算实现未来无线网络的自适应资源分配


在本文中，我们提出了一种基于区块链的移动边缘计算（B-MEC）框架，用于未来无线网络中的自适应资源分配和计算卸载，其中区块链作为覆盖系统提供管理和控制功能。在这个框架中，如何在节点之间达成共识，同时保证MEC和区块链系统的性能是一个重大挑战。同时，资源分配、区块大小以及每个生产者连续产生区块的数量对于B-MEC的性能至关重要。因此，提出了自适应资源分配和块生成方案。为了提高上层区块链系统的吞吐量和下层MEC系统中用户的服务质量（QoS），频谱分配、区块大小以及每个生产者生产区块的数量被制定为联合优化问题，其中考虑了随时间变化的无线链路和 MEC 服务器的计算能力。由于使用传统方法很难解决这个问题，因此我们采用深度强化学习方法。仿真结果通过与其他基线方法的比较表明了该方法的有效性。