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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal

Mohamed Gaballa1, Maysam Abbod1, Ammar Aldallal2

  • 1Department of Electronic & Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary

Deep learning, specifically Long Short-Term Memory networks, enhances channel estimation in Non-Orthogonal Multiple Access (NOMA) systems. This improves signal detection accuracy and overall system performance in fading channels.

Keywords:
KKT conditionsLSTMNOMAdeep learningoptimization

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Non-Orthogonal Multiple Access (NOMA) systems rely on successive interference cancellation (SIC) for signal decoding.
  • Fading channels complicate channel estimation, impacting SIC accuracy and overall system performance.
  • Accurate channel state information is crucial for effective NOMA operation.

Purpose of the Study:

  • To investigate the impact of Deep Neural Networks (DNNs) on channel estimation in NOMA systems.
  • To propose an integrated Long Short-Term Memory (LSTM) network for predicting channel coefficients.
  • To jointly optimize channel estimation and power allocation for improved multiuser recognition in downlink PD-NOMA.

Main Methods:

  • Utilizing LSTM networks for explicit channel coefficient estimation in NOMA.
  • Training DNNs on diverse channel statistics for accurate parameter prediction.
  • Applying Lagrange multipliers and KKT conditions for power optimization to maximize sum rate under QoS constraints.

Main Results:

  • The proposed DL-based channel estimation significantly outperforms conventional NOMA approaches.
  • Demonstrated superiority in key performance metrics including bit error rate (BER) and sum rate.
  • Evaluated the performance of optimized versus fixed power schemes with DL-based channel estimation.

Conclusions:

  • Deep learning-based channel estimation offers a robust solution for NOMA systems operating in fading environments.
  • Joint optimization of DL channel estimation and power allocation enhances multiuser recognition and system efficiency.
  • The LSTM-integrated DNN approach provides a promising direction for future NOMA system advancements.