  In this paper, efficient resource allocation for the uplink transmission of
wireless powered IoT networks is investigated. We adopt LoRa technology as an
example in the IoT network, but this work is still suitable for other
communication technologies. Allocating limited resources, like spectrum and
energy resources, among a massive number of users faces critical challenges. We
consider grouping wireless powered IoT users into available channels first and
then investigate power allocation for users grouped in the same channel to
improve the network throughput. Specifically, the user grouping problem is
formulated as a many to one matching game. It is achieved by considering IoT
users and channels as selfish players which belong to two disjoint sets. Both
selfish players focus on maximizing their own utilities. Then we propose an
efficient channel allocation algorithm (ECAA) with low complexity for user
grouping. Additionally, a Markov Decision Process (MDP) is used to model
unpredictable energy arrival and channel conditions uncertainty at each user,
and a power allocation algorithm is proposed to maximize the accumulative
network throughput over a finite-horizon of time slots. By doing so, we can
distribute the channel access and dynamic power allocation local to IoT users.
Numerical results demonstrate that our proposed ECAA algorithm achieves
near-optimal performance and is superior to random channel assignment, but has
much lower computational complexity. Moreover, simulations show that the
distributed power allocation policy for each user is obtained with better
performance than a centralized offline scheme.
