Intelligent Radio Resource Scheduling for LTE-Advanced using Wavelet Neural Network

This paper presents a novel technique for the efficient resource scheduling for Long Term Evaluation Advanced downlink transmission using wavelet neural network. The dynamism and the uncertainty in the resource scheduling due to the large scale of the network has been taken care through wavelet neural network. The proposed neural network based approach is trained to provide the best scheduling rule at every transmission time interval. Due to the superior estimation capability and better dynamic characteristics than conventional neural network, wavelet neural network offers a better radio resource scheduling. The objective of the proposed scheme is to enhance the system throughput, spectral efficiency and the system capacity. The simulation analysis is performed to verify the effectiveness of the theoretical development.

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Actor-Critic Wavelet Neural Network based Scheduler Technique for LTE-Advanced

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7673.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 4856-4863

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Large Scale ◽

Simulation Analysis ◽

Resource Scheduling ◽

Wavelet Neural Network ◽

Time Interval ◽

Sigmoid Function ◽

Network Resource ◽

Actor Network

This work presents an efficient and intelligent resource scheduling strategy for the Long Term EvolutionAdvanced (LTE-A) downlink transmission using Reinforcement learning and wavelet neural network. Resource scheduling in LTE-A suffers the problem of uncertainty and accuracy for large scale network. Also the performance of scheduling in conventional methods solely depends upon the scheduling algorithm which was fixed for the entire transmission session. This issue has been addressed and resolved in this paper through Actor-Critic architecture based reinforcement learning to provide the best suited scheduling method out of the rule set for every transmission time interval (TTI) of communication. The actor network will take the decision on scheduling and the critic network will evaluate this decision and update the actor network adaptively through the optimal tuning laws so as to get the desired performance in scheduling. Wavelet neural network(WNN) is derived here by using wavelet function as activation function in place of sigmoid function in conventional neural network to attain better learning capabilities, faster convergence and efficient decision making in scheduling. The actor and critic networks are created through these WNNs and are trained with the LTE parameters dataset. The efficacy of the presented work is evaluated through simulation analysis.

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Machine Learning in Radio Resource Scheduling

Advances in Wireless Technologies and Telecommunication - Next-Generation Wireless Networks Meet Advanced Machine Learning Applications ◽

10.4018/978-1-5225-7458-3.ch002 ◽

2019 ◽

pp. 24-56

Author(s):

Ioan-Sorin Comşa ◽

Sijing Zhang ◽

Mehmet Emin Aydin ◽

Pierre Kuonen ◽

Ramona Trestian ◽

...

Keyword(s):

Optimization Problem ◽

Radio Resource Management ◽

Time Windows ◽

Resource Scheduling ◽

System Capacity ◽

Radio Resource ◽

Data Packets ◽

Capacity Maximization ◽

Radio Resource Scheduling

In access networks, the radio resource management is designed to deal with the system capacity maximization while the quality of service (QoS) requirements need be satisfied for different types of applications. In particular, the radio resource scheduling aims to allocate users' data packets in frequency domain at each predefined transmission time intervals (TTIs), time windows used to trigger the user requests and to respond them accordingly. At each TTI, the scheduling procedure is conducted based on a scheduling rule that aims to focus only on particular scheduling objective such as fairness, delay, packet loss, or throughput requirements. The purpose of this chapter is to formulate and solve an aggregate optimization problem that selects at each TTI the most convenient scheduling rule in order to maximize the satisfaction of all scheduling objectives concomitantly TTI-by-TTI. The use of reinforcement learning is proposed to solve such complex multi-objective optimization problem and to ease the decision making on which scheduling rule should be applied at each TTI.

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