  In this article, we first present the vision, key performance indicators, key
enabling techniques (KETs), and services of 6G wireless networks. Then, we
highlight a series of general resource management (RM) challenges as well as
unique RM challenges corresponding to each KET. The unique RM challenges in 6G
necessitate the transformation of existing optimization-based solutions to
artificial intelligence/machine learning-empowered solutions. In the sequel, we
formulate a joint network selection and subchannel allocation problem for 6G
multi-band network that provides both further enhanced mobile broadband (FeMBB)
and extreme ultra reliable low latency communication (eURLLC) services to the
terrestrial and aerial users. Our solution highlights the efficacy of
multi-band network and demonstrates the robustness of dueling deep Q-learning
in obtaining efficient RM solution with faster convergence rate compared to
deep-Q network and double deep Q-network algorithms.
