Deep Learning and Power Allocation Analysis in NOMA System

Gaballa, Mohamed; Abbod, Maysam F.; Aldallal, Ammar

doi:10.1109/icufn55119.2022.9829643

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…It is supposed that the implemented channel model is stationary throughout one frame transmission of data and pilot signals and the channel parameters are varying from one frame to another. The basic architecture of the channel prediction scenario based on the developed Q -learning procedure employed in our examined network is illustrated in Figure 3 , which primarily consists of several stages [ 17 , 43 ].…”

Section: Q-learning Network Architecturementioning

confidence: 99%

“…Essentially, path loss and the distance between every user terminal and the BS need to be specified in the dataset to facilitate the random generation of the channel weights for every user device in the examined MISO-NOMA network [ 43 ]. In the beginning, pilot symbols are created, transmitted, and identified at the BS and at the receiver of every device.…”

Section: Q-learning Network Architecturementioning

confidence: 99%

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A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

Gaballa

Abbod

Aldallal

2023

Sensors

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In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.

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Section: Q-learning Network Architecturementioning

confidence: 99%

Section: Q-learning Network Architecturementioning

confidence: 99%

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

Gaballa

Abbod

Aldallal

2023

Sensors

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mentioning

confidence: 99%

“…The design of a single LSTM cell is basically shown in Figure 3 [13,22]. Each LSTM cell has three inputs and two output parameters.…”

mentioning

confidence: 99%

“…In our proposed DQN approach, the length of each training sequence is specified as L, which is the dimension of the input layer. In our scenario, we choose the input layer of the DNN to include 128 neurons, and the input states to the input layer will be shifted to the subsequent layer after updating the weight parameters [13,22]. As shown in Figure 2, we have applied one LSTM layer as the second layer in both the policy DNN and the target DNN, and the LSTM layer itself includes 300 hidden cells.…”

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confidence: 99%

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Simplified Deep Reinforcement Learning Approach for Channel Prediction in Power Domain NOMA System

Gaballa,

Abbod

2023

Sensors

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In this work, the impact of implementing Deep Reinforcement Learning (DRL) in predicting the channel parameters for user devices in a Power Domain Non-Orthogonal Multiple Access system (PD-NOMA) is investigated. In the channel prediction process, DRL based on deep Q networks (DQN) algorithm will be developed and incorporated into the NOMA system so that this developed DQN model can be employed to estimate the channel coefficients for each user device in NOMA system. The developed DQN scheme will be structured as a simplified approach to efficiently predict the channel parameters for each user in order to maximize the downlink sum rates for all users in the system. In order to approximate the channel parameters for each user device, this proposed DQN approach is first initialized using random channel statistics, and then the proposed DQN model will be dynamically updated based on the interaction with the environment. The predicted channel parameters will be utilized at the receiver side to recover the desired data. Furthermore, this work inspects how the channel estimation process based on the simplified DQN algorithm and the power allocation policy, can both be integrated for the purpose of multiuser detection in the examined NOMA system. Simulation results, based on several performance metrics, have demonstrated that the proposed simplified DQN algorithm can be a competitive algorithm for channel parameters estimation when compared to different benchmark schemes for channel estimation processes such as deep neural network (DNN) based long-short term memory (LSTM), RL based Q algorithm, and channel estimation scheme based on minimum mean square error (MMSE) procedure.

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