Developing novel channel estimation and hybrid precoding in millimeter-wave communication system using heuristic-based deep learning

Navabharat Reddy G and C.v Ravikumar

Energy, 2023, vol. 268, issue C

Abstract: The digital or analog precoders are not carried the optimal energy efficiency in the mm-wave massive MIMO and so, every antenna is needed with one radio frequency chain in the system. Based on this view, a cost-efficient technique is developed for hybrid precoding. From this technique, the short dimensional precoding is obtained from the high dimensional beam-formers in the digital domain for steering antenna elements. Therefore, the main intention of this paper is to develop a channel estimation and hybrid pre-coding model for the mm-wave massive MIMO communication system. The channel estimation phase is performed by the Adaptive Deep Convolutional Neural Network (A-Deep CNN), which covers both channel estimation and channel reconstruction. The introduction of a hybrid meta-heuristic algorithm with Forest-Tunicate Swarm Algorithm (F-TSA) is used for enhancing A-Deep CNN that is adaptable for efficient channel estimation. Once the channel estimation is done, the deployment of Optimized Recurrent Neural Networks (O-RNN) is used for hybrid precoding. Simulation results demonstrate that the proposed A-Deep CNN-based channel estimation scheme outperforms the existing schemes in terms of the Normalized Mean-Squared Error (NMSE) and spectral efficiency, while the O-RNN-hybrid precoder design method has better spectral efficiency performance than other methods.

Keywords: Millimeter-wave communication system; Channel estimation; Hybrid precoding; Adaptive deep convolutional neural network; Optimized recurrent neural networks; Forest-tunicate swarm algorithm (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:268:y:2023:i:c:s0360544222034879

DOI: 10.1016/j.energy.2022.126600

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