An efficient and reliable OFDM channel state estimator using deep learning convolutional neural networks

الملخص EN

Orthogonal frequency division multiplexing (OFDM) wireless systems rely heavily on channel state estimation (CSE) to mitigate the effects of multipath channel fading.

achieving a high data rate with OFDM technology requires efficient CSE and accurate signal detection.

in contrast to more traditional CSE methods that depend on a model-based strategy, machine learning (ml)-based CSE techniques have attracted increased interest in recent years due to their data-driven, learning-based flexibility.

in light of this, a deep learning (dl) convolutional neural network (CNN) is utilized to acquire reliable CSE over OFDM wireless system rayleigh-fading channels.

the suggested CSE utilizes offline training to gather channel information from transmit / receive pairs.

in addition, it employs pilots to provide additional guidance on channels of communication.

the proposed CNN-based estimator is compared to conventional estimation approaches and state-of-the-art dl channel estimators using ser analysis.

the simulation results show that the proposed CNN estimator provides far superior ser performance compared to the conventional ls and MMSE estimation methods.

also, the proposed CNN CSE performs similarly to the DL BiLSTM estimator with restricted training pilots (8).

furthermore, CNN CSE beats DL BiLSTM with enough training pilots (64).

the simulation findings also confirm that the suggested CNN-based CSE is efficient / reliable with (16 and 8) or without cycle prefixes (CP).

this, in turn, reduces the bandwidth required to convey the same quantity of data.

in addition, there is no background knowledge of the channel's statistics in the proposed estimator.

consequently, the proposed method shows potential for addressing CSE issues in OFDM systems with a significant spectrum resource reduction.