  The performance of Non-orthogonal Multiple Access (NOMA) system dramatically
decreases in the presence of inter-cell interference. This condition gets more
challenging if a smart jammer is interacting in a network. In this paper, the
NOMA power allocation of two independent Base Stations (BSs) against a smart
jammer is, modeled as a sequential game. In this game, at first, each BS as a
leader independently chooses its power allocation strategy. Then, the smart
jammer as the follower selects its optimal strategy based on the strategies of
the BSs. The solutions of this game are, derived under different conditions.
Based on the game-theoretical analysis, three new schemes are proposed for
anti-jamming NOMA power allocation in a two-cell scenario called a) Q-Learning
based Unselfish (QLU) NOMA power allocation scheme, b) Deep Q-Learning based
Unselfish (DQLU) NOMA power allocation scheme, and c) Hot Booting Deep
Q-Learning based Unselfish (HBDQLU) NOMA power allocation scheme. In these
methods the BSs do not coordinate with each other. But our analysis
theoretically predicts that with high probability, the proposed methods will
converge to the optimal strategy from the total network point of view.
Simulation results show the convergence of the proposed schemes and also their
outperformance with respect to the Q-Learning-based Selfish (QLS) NOMA power
allocation method.
