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With the continuous growth of machine-type devices (MTDs), it is expectedthat massive machine-type communication (mMTC) will be the dominant form oftraffic in future wireless networks. Applications based on this technology,have fundamentally different traffic characteristics from human-to-human (H2H)communication, which involves a relatively small number of devices transmittinglarge packets consistently. Conversely, in mMTC applications, a very largenumber of MTDs transmit small packets sporadically. Therefore, conventionalgrant-based access schemes commonly adopted for H2H service, are not suitablefor mMTC, as they incur in a large overhead associated with the channel requestprocedure. We propose three grant-free distributed optimization architecturesthat are able to significantly minimize the average power consumption of thenetwork. The problem of physical layer (PHY) and medium access control (MAC)optimization in grant-free random access transmission is is modeled as apartially observable stochastic game (POSG) aimed at minimizing the averagetransmit power under a per-device delay constraint. The results show that theproposed architectures are able to achieve significantly less average latencythan a baseline, while spending less power. Moreover, the proposedarchitectures are more robust than the baseline, as they present less variancein the performance for different system realizations.

Intelligent Link Adaptation for Grant-Free Access Cellular Networks: A Distributed Deep Reinforcement Learning Approach