Multiuser Scheduling Algorithm for 5G IoT Systems Based on Reinforcement Learning

Li, Zibo; Su, Pan; Yu-xi, Qin

doi:10.1109/tvt.2022.3223652

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…According to [ 51 ], we can approximate the computational complexity of the Q-learning algorithm as

per iteration, where S is the number of states, A is the number of actions, and H is the number of steps per episode. According to the state space and action space defined in our simulation environment, the amount of work per iteration can be approximated as

[ 51 , 52 ], where

represents a number of antennas in BS,

represent a number of user devices in the cell, and

represents the size of channel coefficients. On the other hand, the computational complexity for the benchmark scheme implemented in [ 15 ], is described as follows: the sizes of the input layer, the first hidden layer, the second hidden layer, and the output layer for each network implemented in [ 15 ] is denoted as

, and

respectively.…”

Section: Simulation Results and Discussionmentioning

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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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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“…According to [ 51 ], we can approximate the computational complexity of the Q-learning algorithm as

[ 51 , 52 ], where

represents a number of antennas in BS,

represent a number of user devices in the cell, and

, and

respectively.…”

Section: Simulation Results and Discussionmentioning

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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“…Elsayed et al [39] utilized an energy-efficient Q-Learning algorithm based on the UCB strategy to dynamically activate and deactivate SCCs (sub-carrier components) in order to maximize user throughput with the minimum number of active SCCs, thereby maximizing energy efficiency. Li et al [40] proposed a user scheduling algorithm based on the UCB strategy in reinforcement learning that continuously updates the estimated behavioral value of each user. This approach avoids the phenomenon of "extreme unfairness" during the exploration phase, reduces algorithm complexity, and improves system throughput.…”

Section: Related Workmentioning

confidence: 99%

Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing

Wang

Dong²,

Fan

2023

Mathematics

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The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.

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“…To illustrate our approach and contributions, we focus on a state-of-the-art technology example, a multiuser scheduling scheme [13][14][15]. In conventional parallel multiuser scheduling, a scheduled user's signal is detected by correlating signals from all scheduled users, leading to multiple-user interference (MUI).…”

Section: Introductionmentioning

confidence: 99%

Joint Statistics of Partial Sums of Ordered i.n.d. Gamma Random Variables

Nam,

Yoon,

Choi

2023

Mathematics

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From the perspective of wireless communication, as communication systems become more complex, order statistics have gained increasing importance, particularly in evaluating the performance of advanced technologies in fading channels. However, existing analytical methods are often too complex for practical use. In this research paper, we introduce innovative statistical findings concerning the sum of ordered gamma-distributed random variables. We examine various channel scenarios where these variables are independent but not-identically distributed. To demonstrate the practical applicability of our results, we provide a comprehensive closed-form expression for the statistics of the signal-to-interference-plus-noise ratio in a multiuser scheduling system. We also present numerical examples to illustrate the effectiveness of our approach. To ensure the accuracy of our analysis, we validate our analytical results through Monte Carlo simulations.

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Multiuser Scheduling Algorithm for 5G IoT Systems Based on Reinforcement Learning

Cited by 9 publications

References 32 publications

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

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

Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing

Joint Statistics of Partial Sums of Ordered i.n.d. Gamma Random Variables

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