  Conventional channel estimation (CE) for Internet of Things (IoT) systems
encounters challenges such as low spectral efficiency, high energy consumption,
and blocked propagation paths. Although superimposed pilot-based CE schemes and
the reconfigurable intelligent surface (RIS) could partially tackle these
challenges, limited researches have been done for a systematic solution. In
this paper, a superimposed pilot-based CE with the reconfigurable intelligent
surface (RIS)-assisted mode is proposed and further enhanced the performance by
networks. Specifically, at the user equipment (UE), the pilot for CE is
superimposed on the uplink user data to improve the spectral efficiency and
energy consumption for IoT systems, and two lightweight networks at the base
station (BS) alleviate the computational complexity and processing delay for
the CE and symbol detection (SD). These dedicated networks are developed in a
cooperation manner. That is, the conventional methods are employed to perform
initial feature extraction, and the developed neural networks (NNs) are
oriented to learn along with the extracted features. With the assistance of the
extracted initial feature, the number of training data for network training is
reduced. Simulation results show that, the computational complexity and
processing delay are decreased without sacrificing the accuracy of CE and SD,
and the normalized mean square error (NMSE) and bit error rate (BER)
performance at the BS are improved against the parameter variance.
