  Unmanned aerial vehicles (UAVs) are expected to be an integral part of
wireless networks. In this paper, we aim to find collision-free paths for
multiple cellular-connected UAVs, while satisfying requirements of connectivity
with ground base stations (GBSs) in the presence of a dynamic jammer. We first
formulate the problem as a sequential decision making problem in discrete
domain, with connectivity, collision avoidance, and kinematic constraints. We,
then, propose an offline temporal difference (TD) learning algorithm with
online signal-to-interference-plus-noise ratio (SINR) mapping to solve the
problem. More specifically, a value network is constructed and trained offline
by TD method to encode the interactions among the UAVs and between the UAVs and
the environment; and an online SINR mapping deep neural network (DNN) is
designed and trained by supervised learning, to encode the influence and
changes due to the jammer. Numerical results show that, without any information
on the jammer, the proposed algorithm can achieve performance levels close to
that of the ideal scenario with the perfect SINR-map. Real-time navigation for
multi-UAVs can be efficiently performed with high success rates, and collisions
are avoided.
