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Temporal Neural Network Framework Adaptation in Reconfigurable Intelligent Surface-Assisted Wireless Communication.

Mohammad Abrar Shakil Sejan^1,2, Md Habibur Rahman^1,2, Md Abdul Aziz^1,2

¹Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This study introduces a Temporal Convolutional Network (TCN) model to improve wireless communication performance using reconfigurable intelligent surfaces (RIS). The TCN model effectively enhances signal reflection for better user connectivity and communication efficiency.

Keywords:

machine learning reconfigurable intelligent surface temporal neural network wireless communication

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Area of Science:

Wireless Communication Engineering
Machine Learning Applications
Signal Processing

Background:

Reconfigurable Intelligent Surfaces (RIS) offer potential for enhancing wireless communication by controlling signal reflection to specific user locations.
Machine Learning (ML) techniques provide efficient, data-driven solutions for complex problems without explicit programming.
Integrating ML with RIS can optimize wireless communication systems.

Purpose of the Study:

To propose and evaluate a novel Temporal Convolutional Network (TCN)-based model for RIS-enhanced wireless communication.
To assess the model's effectiveness in predicting signal characteristics and improving communication performance.
To compare the TCN model's performance against Long Short-Term Memory (LSTM) and non-ML approaches.

Main Methods:

Developed a TCN-based model comprising four TCN layers, a fully connected layer, a ReLU layer, and a classification layer.
Input data consisted of complex numbers representing signals under Quadrature Phase Shift Keying (QPSK) and Binary Phase Shift Keying (BPSK) modulation.
Simulations involved 2x2 and 4x4 Multiple-Input Multiple-Output (MIMO) configurations with one base station and two single-antenna users, evaluating three optimizers.

Main Results:

The proposed TCN model demonstrated effectiveness in mapping input data to specified labels for QPSK and BPSK modulation.
Performance was evaluated using Bit Error Rate (BER) and Symbol Error Rate (SER) metrics.
Simulation results indicated superior performance of the TCN model compared to LSTM and non-ML benchmarks.

Conclusions:

The TCN-based model is effective for RIS-enhanced wireless communication, offering improved signal classification and performance.
Data-driven ML approaches, specifically TCNs, provide a powerful tool for optimizing complex wireless communication systems.
The proposed model shows significant potential for future advancements in wireless communication technologies.