Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access

Name: Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
Author: Siddiqi, U. F., Sait, S. M., Uysal, Murat

İsim	Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
Yazar	Siddiqi, U. F., Sait, S. M., Uysal, Murat
Basım Tarihi:	2020
Basım Yeri	- IEEE
Konu	NOMA, Optimization, Silicon carbide, Resource management, Task analysis, Relays, Reinforcement learning, Non-orthogonal multiplexing, Double deep Q learning, Deep reinforcement learning, Non-convex optimization, Power-domain NOMA
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2169-3536
Kayıt Numarası	446ab2ca-ca20-454a-b5f2-2f4a155f5270
Lokasyon	Electrical & Electronics Engineering
Tarih	2020
Notlar	Deanship of Scientific Research, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
Örnek Metin	NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as max-min, is a popular approach to ensure fairness among the users. NOMA optimizes the transmission power (or power-coefficients) of the users to perform max-min. The problem is a constrained non-convex optimization for users greater than two. We propose to solve this problem using the Double Deep Q Learning (DDQL) technique, a popular method of reinforcement learning. The DDQL technique employs a Deep Q- Network to learn to choose optimal actions to optimize users' power-coefficients. The model of the Markov Decision Process (MDP) is critical to the success of the DDQL method, and helps the DQN to learn to take better actions. An MDP model is proposed in which the state consists of the power-coefficients values, data-rate of users, and vectors indicating which of the power-coefficients can be increased or decreased. An action simultaneously increases the power-coefficient of one user and reduces another user's power-coefficient by the same amount. The amount of change can be small or large. The action-space contains all possible ways to alter the values of any two users at a time. DQN consists of a convolutional layer and fully connected layers. We compared the proposed method with the sequential least squares programming and trust-region constrained algorithms and found that the proposed method can produce competitive results.
DOI	10.1109/ACCESS.2020.3038923
Cilt	8

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Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access

Yazar Siddiqi, U. F., Sait, S. M., Uysal, Murat

Basım Tarihi 2020

Basım Yeri - IEEE

Konu NOMA, Optimization, Silicon carbide, Resource management, Task analysis, Relays, Reinforcement learning, Non-orthogonal multiplexing, Double deep Q learning, Deep reinforcement learning, Non-convex optimization, Power-domain NOMA

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2169-3536

Kayıt Numarası 446ab2ca-ca20-454a-b5f2-2f4a155f5270

Lokasyon Electrical & Electronics Engineering

Tarih 2020

Notlar Deanship of Scientific Research, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

Örnek Metin NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as max-min, is a popular approach to ensure fairness among the users. NOMA optimizes the transmission power (or power-coefficients) of the users to perform max-min. The problem is a constrained non-convex optimization for users greater than two. We propose to solve this problem using the Double Deep Q Learning (DDQL) technique, a popular method of reinforcement learning. The DDQL technique employs a Deep Q- Network to learn to choose optimal actions to optimize users' power-coefficients. The model of the Markov Decision Process (MDP) is critical to the success of the DDQL method, and helps the DQN to learn to take better actions. An MDP model is proposed in which the state consists of the power-coefficients values, data-rate of users, and vectors indicating which of the power-coefficients can be increased or decreased. An action simultaneously increases the power-coefficient of one user and reduces another user's power-coefficient by the same amount. The amount of change can be small or large. The action-space contains all possible ways to alter the values of any two users at a time. DQN consists of a convolutional layer and fully connected layers. We compared the proposed method with the sequential least squares programming and trust-region constrained algorithms and found that the proposed method can produce competitive results.

DOI 10.1109/ACCESS.2020.3038923

Cilt 8