Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 19, Issue 3, Pages 1689-1703Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2019.2956519
Keywords
Wireless networks; Resource management; Computational modeling; Task analysis; Adaptive systems; Mobile edge computing; computation offloading; blockchain; deep reinforcement learning
Funding
- National Natural Science Foundation of China [61671088, 61771070]
- Beijing University of Posts and Telecommunications (BUPT) Excellent Ph.D.
- Students Foundation [CX2018201]
- Canadian Natural Sciences and Engineering Research Council [RGPIN-2019-06348]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available