Scholarlink

<p>The demand for higher data rates and increased reliability in 5G uplink communications has prompted the exploration of innovative technologies. This paper introduces a novel approach that combines Polynomial-Cancellation-Coded-Orthogonal Frequency Division Multiplexing (PCC-OFDM) and integrates the power of Neural Network based channel estimation. The proposed system aims to optimize spectral efficiency and mitigates the challenges associated with channel estimation in dynamic wireless environments. The proposed system achieves superior error correction capabilities, ensuring robust communication links. The integration of OFDM provides efficient spectrum utilization and enables seamless communication in the presence of fading channels. Furthermore, an Artificial Neural Network- Multilayer Perceptron (ANN-MLP) based channel estimation mechanism is introduced to adaptively learn and predict channel conditions, enhancing the accuracy of estimated channel parameters. The biases and weights of the neural network are optimized by stochastic gradient descent (SGD) approach which in turn minimizes the loss function. Simulation outcomes prove the superior performance of the developed approach, showcasing its potential to revolutionize 5G uplink communications by achieving higher data rates, improved reliability and adaptability to varying channel conditions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing 5G uplink communications through efficient PCC-OFDM with neural network-based channel estimation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月2日

查看全文
  • Monica Jenifer J.,
  • Sumathi S.

摘要

The demand for higher data rates and increased reliability in 5G uplink communications has prompted the exploration of innovative technologies. This paper introduces a novel approach that combines Polynomial-Cancellation-Coded-Orthogonal Frequency Division Multiplexing (PCC-OFDM) and integrates the power of Neural Network based channel estimation. The proposed system aims to optimize spectral efficiency and mitigates the challenges associated with channel estimation in dynamic wireless environments. The proposed system achieves superior error correction capabilities, ensuring robust communication links. The integration of OFDM provides efficient spectrum utilization and enables seamless communication in the presence of fading channels. Furthermore, an Artificial Neural Network- Multilayer Perceptron (ANN-MLP) based channel estimation mechanism is introduced to adaptively learn and predict channel conditions, enhancing the accuracy of estimated channel parameters. The biases and weights of the neural network are optimized by stochastic gradient descent (SGD) approach which in turn minimizes the loss function. Simulation outcomes prove the superior performance of the developed approach, showcasing its potential to revolutionize 5G uplink communications by achieving higher data rates, improved reliability and adaptability to varying channel conditions.