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Future wireless access networks need to support diversified quality ofservice (QoS) metrics required by various types of Internet-of-Things (IoT)devices, e.g., age of information (AoI) for status generating sources and ultralow latency for safety information in vehicular networks. In this paper, anovel inner-state driven random access (ISDA) framework is proposed based ondistributed policy learning, in particular a cross-entropy method. Conventionalrandom access schemes, e.g., $p$-CSMA, assume state-less terminals, and thusassigning equal priorities to all. In ISDA, the inner-states of terminals aredescribed by a time-varying state vector, and the transmission probabilities ofterminals in the contention period are determined by their respectiveinner-states. Neural networks are leveraged to approximate the functionmappings from inner-states to transmission probabilities, and an iterativeapproach is adopted to improve these mappings in a distributed manner.Experiment results show that ISDA can improve the QoS of heterogeneousterminals simultaneously compared to conventional CSMA schemes.

Distributed Policy Learning Based Random Access for Diversified QoS Requirements