  In this paper, we are interested in symbiotic radio networks, in which an
Internet-of-Things (IoT) network parasitizes in a primary network to achieve
spectrum-, energy-, and infrastructure-efficient communications. Specifically,
the BS serves multiple cellular users using time division multiple access
(TDMA) and each IoT device is associated with one cellular user for information
transmission. We focus on the user association problem, whose objective is to
link each IoT device to an appropriate cellular user by maximizing the sum rate
of all IoT devices. However, the difficulty in obtaining the full real-time
channel information makes it difficult to design an optimal policy for this
problem. To overcome this issue, we propose two deep reinforcement learning
(DRL) algorithms, both use the historical information to infer the current
information in order to make appropriate decisions. One algorithm, centralized
DRL, makes decisions for all IoT devices at one time with global information.
The other algorithm, distributed DRL, makes a decision only for one IoT device
at one time using local information. Finally, simulation results show that the
two DRL algorithms achieve comparable performance as the optimal user
association policy which requires perfect real-time information, and the
distributed DRL algorithm has the advantage of scalability.
