Background:
Spectrum scarcity, spectrum efficiency, power constraints, and jamming
attacks are core challenges that face wireless networks. While cognitive radio networks (CRNs)
enable the sharing of licensed bands when they are unoccupied, the spectrum should be used efficiently
by the SU to ensure a high data rate transmission. In addition, the mobility of the secondary
users (SUs) makes power consumption a matter of concern in wireless networks. Because of the
open environment, the jamming attack can easily deteriorate the performance and disrupt the connections.
background:
Various anti-jamming schemes have been proposed to mitigate the attacker's impact on Cognitive Radio Networks (CRNs), some of the proposed schemes aim to increase channel capacity or improve spectrum-efficient gain. However, few of them have considered the secondary user's (SU’s) power consumption.
Objectives:
We aim to enhance the performance of CRN and establish more reliable connections
for the SU in the presence of smart jammer by ensuring efficient spectrum utilization and extending
the network lifetime.
Methods:
To achieve our objectives, we propose an anti-jamming approach that adopts frequency
hopping. Our approach assumes that SUs observe spectrum availability and channel gain. Then, SU
learns the jammer behaviour and goes for the appropriate policy in terms of the number of data and
control channels that optimize jointly spectrum efficiency and power consumption. Within, the interaction
between the SU and the jammer is modelled as a zero-sum stochastic game, and we employ
reinforcement learning to address this game.
Results:
SUs learn the optimal policy that maximizes the spectrum efficiency and minimizes the
power consumption in the presence of a smart jammer. Simulation results show that the low channel
gain leads the SU to select a high number of data channels. However, when the channel gain is
high, the SU increases the number of control channels to guarantee a more reliable connection.
Taking into account the spectrum efficiency, SUs save their energy by decreasing the number of
used channels. The proposed strategy achieves better performance in comparison with myopic
learning and the random strategy.
Conclusion:
Under a jamming attack, considering the gain of utilized channels, SUs select the appropriate
number of control and data channels to ensure a reliable, efficient, and long-term connection.