  Blockchain technology with its secure, transparent and decentralized nature
has been recently employed in many mobile applications. However, the mining
process in mobile blockchain requires high computational and storage capability
of mobile devices, which would hinder blockchain applications in mobile
systems. To meet this challenge, we propose a mobile edge computing (MEC) based
blockchain network where multi-mobile users (MUs) act as miners to offload
their mining tasks to a nearby MEC server via wireless channels. Specially, we
formulate task offloading and user privacy preservation as a joint optimization
problem which is modelled as a Markov decision process, where our objective is
to minimize the long-term system offloading costs and maximize the privacy
levels for all blockchain users. We first propose a reinforcement learning
(RL)-based offloading scheme which enables MUs to make optimal offloading
decisions based on blockchain transaction states and wireless channel qualities
between MUs and MEC server. To further improve the offloading performances for
larger-scale blockchain scenarios, we then develop a deep RL algorithm by using
deep Q-network which can efficiently solve large state space without any prior
knowledge of the system dynamics. Simulation results show that the proposed
RL-based offloading schemes significantly enhance user privacy, and reduce the
energy consumption as well as computation latency with minimum offloading costs
in comparison with the benchmark offloading schemes.
