Control (management)

Process (computing)

Engineering

Neuroscience

Cognitive network

Cognition

Cognitive radio

Systems engineering

Operating system

Telecommunications

Computer science

Computer network

Thermodynamics

Physics

Reinforcement learning

Psychology

Task (project management)

Optics

Control channel

Artificial intelligence

Channel (broadcasting)

Wireless

Base station

Adaptation (eye)

Jamming

IEEE Transactions on Vehicular Technology

The future of intelligent transportation system (ITS) is expected to be composed of connected and autonomous vehicles (CAVs), the development of which will have great impact on people's everyday life. Unfortunately, this progress will be accompanied by all kinds of potential threats and attacks rising in CAV network. As a legacy from traditional wireless networks, jamming attack is still one of the major and serious threats to vehicle-to-vehicle (V2V) communications. In this paper, we investigate the anti-jamming V2V communication in CAV networks through power control in conjunction with channel selection. Bringing into play a brain-inspired research tool called cognitive dynamic system (CDS), the general structure of cognitive risk control (CRC) is well-tailored to analyze and address the jamming problem. Specifically, power control is carried out first using reinforcement learning, the result of which is then examined by a module called task-switch control. Based on the risk assessment, a multi-armed bandit (MAB) problem is formulated to perform the channel-selection process when necessary. Through continuous perception-action cycles (PACs), the feature of predictive adaptation is realized for the legitimate vehicle in its behavioral interactions with the jammer. Simulation results have shown that the proposed method has desirable performance in terms of several evaluation metrics.

Cognitive Risk Control for Anti-Jamming V2V Communications in Autonomous Vehicle Networks