  In the rapidly growing development of the Internet of Things (IoT)
infrastructure, achieving reliable wireless communication is a challenge. IoT
devices operate in diverse environments with common signal interference and
fluctuating channel conditions. Accurate channel estimation helps adapt the
transmission strategies to current conditions, ensuring reliable communication.
Traditional methods, such as Least Squares (LS) and Minimum Mean Squared Error
(MMSE) estimation techniques, often struggle to adapt to the diverse and
complex environments typical of IoT networks. This research article delves into
the potential of Deep Learning (DL) to enhance channel estimation, focusing on
the Received Signal Strength Indicator (RSSI) metric - a critical yet
challenging aspect due to its susceptibility to noise and environmental
factors. This paper presents two Fully Connected Neural Networks (FCNNs)-based
Low Power (LP-IoT) channel estimation models, leveraging RSSI for accurate
channel estimation in LP-IoT communication. Our Model A exhibits a remarkable
99.02% reduction in Mean Squared Error (MSE), and Model B demonstrates a
notable 90.03% MSE reduction compared to the benchmarks set by current studies.
Additionally, the comparative studies of our model A with other DL-based
techniques show significant efficiency in our estimation models.
