  Future wireless access networks need to support diversified quality of
service (QoS) metrics required by various types of Internet-of-Things (IoT)
devices, e.g., age of information (AoI) for status generating sources and ultra
low latency for safety information in vehicular networks. In this paper, a
novel inner-state driven random access (ISDA) framework is proposed based on
distributed policy learning, in particular a cross-entropy method. Conventional
random access schemes, e.g., $p$-CSMA, assume state-less terminals, and thus
assigning equal priorities to all. In ISDA, the inner-states of terminals are
described by a time-varying state vector, and the transmission probabilities of
terminals in the contention period are determined by their respective
inner-states. Neural networks are leveraged to approximate the function
mappings from inner-states to transmission probabilities, and an iterative
approach is adopted to improve these mappings in a distributed manner.
Experiment results show that ISDA can improve the QoS of heterogeneous
terminals simultaneously compared to conventional CSMA schemes.
