  The conventional design of wireless communication systems typically relies on
established mathematical models that capture the characteristics of different
communication modules. Unfortunately, such design cannot be easily and directly
applied to future wireless networks, which will be characterized by large-scale
ultra-dense networks whose design complexity scales exponentially with the
network size. Furthermore, such networks will vary dynamically in a significant
way, which makes it intractable to develop comprehensive analytical models.
Recently, deep learning-based approaches have emerged as potential alternatives
for designing complex and dynamic wireless systems. However, existing
learning-based methods have limited capabilities to scale with the problem size
and to generalize with varying network settings. In this paper, we propose a
scalable and generalizable neural calibration framework for future wireless
system design, where a neural network is adopted to calibrate the input of
conventional model-based algorithms. Specifically, the backbone of a
traditional time-efficient algorithm is integrated with deep neural networks to
achieve a high computational efficiency, while enjoying enhanced performance.
The permutation equivariance property, carried out by the topological structure
of wireless systems, is furthermore utilized to develop a generalizable neural
network architecture. The proposed neural calibration framework is applied to
solve challenging resource management problems in massive multiple-input
multiple-output (MIMO) systems. Simulation results will show that the proposed
neural calibration approach enjoys significantly improved scalability and
generalization compared with the existing learning-based methods.
