Cross-Layer Anti-Jamming Scheme: A Hierarchical Learning Approach

This paper investigates the cross-layer optimization for anti-jamming in the network and MAC layers, in which the jammer can adjust the jamming policies to maximize the jamming effectiveness. The joint problem of routing selection, channel allocation, and power control is formulated as a Stackelberg...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.34874-34883
Main Authors: Han, Chen, Niu, Yingtao
Format: Article
Language:English
Subjects:
Algorithms
Anti-jamming
Channel allocation
Channels
cross-layer
Game theory
Games
Jamming
Machine learning
Optimization
Power control
Q-learning
Routing
routing selection
Stackelberg game
Throughput
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper investigates the cross-layer optimization for anti-jamming in the network and MAC layers, in which the jammer can adjust the jamming policies to maximize the jamming effectiveness. The joint problem of routing selection, channel allocation, and power control is formulated as a Stackelberg game. The jammer leads the game by choosing the optimal jamming power and channels. The user follows by selecting the optimal nodes and corresponding channels, and adjusts its transmitting power to meet the communication requirement. Then, based on Q-learning, a cross-layer anti-jamming learning algorithm is proposed to obtain the Stackelberg equilibrium. Finally, simulation results are presented to verify the effectiveness of the proposed algorithm.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2847045