  With the continuous growth of machine-type devices (MTDs), it is expected
that massive machine-type communication (mMTC) will be the dominant form of
traffic 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 transmitting
large packets consistently. Conversely, in mMTC applications, a very large
number of MTDs transmit small packets sporadically. Therefore, conventional
grant-based access schemes commonly adopted for H2H service, are not suitable
for mMTC, as they incur in a large overhead associated with the channel request
procedure. We propose three grant-free distributed optimization architectures
that are able to significantly minimize the average power consumption of the
network. The problem of physical layer (PHY) and medium access control (MAC)
optimization in grant-free random access transmission is is modeled as a
partially observable stochastic game (POSG) aimed at minimizing the average
transmit power under a per-device delay constraint. The results show that the
proposed architectures are able to achieve significantly less average latency
than a baseline, while spending less power. Moreover, the proposed
architectures are more robust than the baseline, as they present less variance
in the performance for different system realizations.
