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A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System.

Hammad Hassan1, Irfan Ahmed2, Rizwan Ahmad1

  • 1School of Electrical Engineering and Computer Science, National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan.

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|August 11, 2019
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Summary
This summary is machine-generated.

This study introduces novel resource block allocation schemes for relay-assisted cellular networks to enhance energy efficiency (EE) and user throughput. The proposed SAMM NN scheme demonstrates superior performance in multi-relay scenarios.

Keywords:
LTE-Abisection based optimal power allocationenergy efficiencymachine learningproportional rate constraintresource block allocationwater filling algorithm

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Area of Science:

  • Wireless communication networks
  • Telecommunication systems engineering
  • Network resource management

Background:

  • Energy efficiency (EE) is a key design parameter in modern cellular systems, requiring a balance between throughput and fairness.
  • Relay-assisted Long Term Evolution-Advanced (LTE-A) networks present complex challenges for resource block (RB) and power allocation.
  • Optimizing EE in these networks necessitates advanced allocation strategies considering relay deployment and user association.

Purpose of the Study:

  • To propose and evaluate novel resource block (RB) allocation schemes for relay-assisted LTE-A networks.
  • To investigate methods for optimizing energy efficiency (EE) and user throughput in single and multi-relay systems.
  • To reduce the computational complexity of RB and power allocation in multi-relay scenarios.

Main Methods:

  • Development of Maximum Throughput (MT) and SAMM (alternating MT and proportional fairness) RB allocation schemes for single relays.
  • Application of k-mean clustering to address relay deployment and user association dependencies in multi-relay systems.
  • Introduction of a two-step neural network (NN) process (SAMM NN) combining unsupervised learning for RB allocation and Bisection-based Power Allocation (BOPA) for power allocation.

Main Results:

  • For single relays, SAMM BOPA achieved the highest EE, while SAMM equal power maximized fairness.
  • In multi-relay networks, the SAMM NN scheme outperformed SAMM equal power and BOPA in terms of EE.
  • The SAMM NN also demonstrated superior throughput fairness compared to MT equal power and MT BOPA in multi-relay systems.

Conclusions:

  • The proposed SAMM NN scheme offers a computationally efficient and effective solution for optimizing EE and fairness in multi-relay LTE-A networks.
  • The study highlights the trade-offs between EE and fairness, providing insights into scheme selection based on network configuration.
  • Advanced techniques like k-mean clustering and neural networks are crucial for addressing the complexities of resource allocation in future cellular systems.