  Fair resource allocation is one of the most important topics in communication
networks. Existing solutions almost exclusively assume each user utility
function is known and concave. This paper seeks to answer the following
question: how to allocate resources when utility functions are unknown, even to
the users? This answer has become increasingly important in the next-generation
AI-aware communication networks where the user utilities are complex and their
closed-forms are hard to obtain. In this paper, we provide a new solution using
a distributed and data-driven bilevel optimization approach, where the lower
level is a distributed network utility maximization (NUM) algorithm with
concave surrogate utility functions, and the upper level is a data-driven
learning algorithm to find the best surrogate utility functions that maximize
the sum of true network utility. The proposed algorithm learns from data
samples (utility values or gradient values) to autotune the surrogate utility
functions to maximize the true network utility, so works for unknown utility
functions. For the general network, we establish the nonasymptotic convergence
rate of the proposed algorithm with nonconcave utility functions. The
simulations validate our theoretical results and demonstrate the great
effectiveness of the proposed method in a real-world network.
