  Improving the data rate of machine-type communication (MTC) is essential in
supporting emerging Internet of things (IoT) applications ranging from
real-time surveillance to edge machine learning. To this end, in this paper we
propose a resource allocation approach for uplink transmissions within a random
access procedure in MTC by exploiting multichannel selection diversity, coined
explore-before-talk (EBT). Each user in EBT first sends pilot signals through
multiple channels that are initially allocated by a base station (BS) for
exploration, and then the BS informs a subset of initially allocated channels
that are associated with high signal-to-noise ratios (SNRs) for data packet
transmission by the user while releasing the rest of the channels for other
users. Consequently, EBT exploits a multichannel selection diversity gain
during data packet transmission, at the cost of exploration during pilot
transmission. We optimize this exploration-exploitation trade-off, by deriving
closed-form mean data rate and resource outage probability expressions.
Numerical results corroborate that EBT achieves a higher mean data rate while
satisfying the same outage constraint, compared to a conventional MTC protocol
without exploration.
