  Suppressing the deliberate interference for wireless networks is critical to
guarantee a reliable communication link. However, nullifying the jamming
signals can be problematic when the correlations between transmitted jamming
signals are deliberately varied over time. Specifically, recent studies reveal
that by deliberately varying the correlations among jamming signals, attackers
can effectively vary the jamming channels and thus their nullspace, even when
the physical channels remain unchanged. That makes the beam-forming matrix
derived from the nullspace of the jamming channels unable to suppress the
jamming signals. Most existing solutions only consider unchanged correlations
or heuristically adapt to the time-varying correlation problem by continuously
monitoring the residual jamming signals before updating the beam-forming
matrix. In this paper, we systematically formulate the optimization problem of
the nullspace estimation and data transmission phases. Even ignoring the
unknown strategy of the jammers and the challenging nullspace estimation
process, the resulting problem is an integer programming problem, hence
intractable to obtain its optimal solution. To tackle it and address the
unknown strategy of the jammer, we reformulate the problem using a partially
observable semi-Markov decision process (POSMDP) and then design a deep dueling
Q-learning based framework to tune the duration of the nullspace estimation and
data transmission phases. Extensive simulations demonstrate that the proposed
techniques effectively deal with jamming signals whose correlations vary over
time, and the range of correlations is unknown. Especially, our techniques do
not require continuous monitoring of the residual jamming signals (after the
nullification process) before updating the beam-forming matrix. As such, the
system is more spectral-efficient and has a lower outage probability.
