  Long-range (LoRa) communication technology, distinguished by its low power
consumption and long communication range, is widely used in the Internet of
Things. Nevertheless, the LoRa MAC layer adopts pure ALOHA for medium access
control, which may suffer from severe packet collisions as the network scale
expands, consequently reducing the system energy efficiency (EE). To address
this issue, it is critical to carefully allocate transmission parameters such
as the channel (CH), transmission power (TP) and spreading factor (SF) to each
end device (ED). Owing to the low duty cycle and sporadic traffic of LoRa
networks, evaluating the system EE under various parameter settings proves to
be time-consuming. Consequently, we propose an analytical model aimed at
calculating the system EE while fully considering the impact of multiple
gateways, duty cycling, quasi-orthogonal SFs and capture effects. On this
basis, we investigate a joint CH, SF and TP allocation problem, with the
objective of optimizing the system EE for uplink transmissions. Due to the
NP-hard complexity of the problem, the optimization problem is decomposed into
two subproblems: CH assignment and SF/TP assignment. First, a matching-based
algorithm is introduced to address the CH assignment subproblem. Then, an
attention-based multiagent reinforcement learning technique is employed to
address the SF/TP assignment subproblem for EDs allocated to the same CH, which
reduces the number of learning agents to achieve fast convergence. The
simulation outcomes indicate that the proposed approach converges quickly under
various parameter settings and obtains significantly better system EE than
baseline algorithms.
