  We consider a novel ultra-narrowband (UNB) low-power wide-area network
(LPWAN) architecture design for uplink transmission of a massive number of
Internet of Things (IoT) devices over multiple multiplexing bands. An IoT
device can randomly choose any of the multiplexing bands to transmit its
packet. Due to hardware constraints, a base station (BS) is able to listen to
only one multiplexing band. Our main objective is to maximize the packet
decoding probability (PDP) by optimizing the placement of the BSs and frequency
assignment of BSs to multiplexing bands. We develop two online approaches that
adapt to the environment based on the statistics of (un)successful packets at
the BSs. The first approach is based on a predefined model of the environment,
while the second approach is measurement-based model-free approach, which is
applicable to any environment. The benefit of the model-based approach is a
lower training complexity, at the risk of a poor fit in a model-incompatible
environment. The simulation results show that our proposed approaches to band
assignment and BS placement offer significant improvement in PDP over baseline
random approaches and perform closely to the theoretical upper bound.
