  A precise incident wave angle estimation in aerial communication is a key
enabler in sixth-generation wireless communication network. With this goal, a
generic 3-dimensional (3D) channel model is analyzed for air-to-air (A2A)
networks under antenna misalignment, radio frequency impairments and
polarization loss. The unique aspects of each aerial node are highlighted and
the few-shot learning as a model agnostic meta-learning (MAML) classifier is
proposed for learning-to-learn (L2L) incident wave angle estimation by
utilizing the received signal strength (RSS). Additionally, a more
computationally efficient technique, first order model agnostic meta-learning
(FOMAML) is implemented. It has been observed that the proposed approach
reaches up to 85% training accuracy and 75.4% evaluation accuracy with MAML.
Regarding this, a convergence rate and accuracy trade-off have been established
for several cases of MAML and FOMAML. For different L2L models trained with
limited data, heuristic accuracy performance is determined by an upper bound of
the probability of confidence.
