Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

Naeem, Muddasar; Coronato, Antonio; Ullah, Zaib; Bashir, Sajid; Paragliola, Giovanni

doi:10.3390/s22218278

Cited by 11 publications

(10 citation statements)

References 39 publications

(42 reference statements)

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“…The intelligent control needs data or experience to design [51][52]. Reinforcement learning (RL), unlike other artificial intelligence algorithms, is a learning method that does not require any rules [53][54][55][56]. RL is a machine learning method that regards the feedback of the environment as an input and adapts the environment.…”

Section: Introductionmentioning

confidence: 99%

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Hanh

Cong

2023

Journal of Robotics and Control

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Obstacle avoidance for mobile robot to reach the desired target from a start location is one of the most interesting research topics. However, until now, few works discuss about working of mobile robot in the dynamic and continuously changing environment. So, this issue is still the research challenge for mobile robots. Traditional algorithm for obstacle avoidance in the dynamic, complex environment had many drawbacks. As known that Q-learning, the type of reinforcement learning, has been successfully applied in computer games. However, it is still rarely used in real world applications. This research presents an effectively method for real time dynamic obstacle avoidance based on Q-learning in the real world by using three-wheeled mobile robot. The position of obstacles including many static and dynamic obstacles and the mobile robot are recognized by fixed camera installed above the working space. The input for the robot is the 2D data from the camera. The output is an action for the robot (velocities, linear and angular parameters). Firstly, the simulation is performed for Q-learning algorithm then based on trained data, The Q-table value is implemented to the real mobile robot to perform the task in the real scene. The results are compared with intelligent control method for both static and dynamic obstacles cases. Through implement experiments, the results show that, after training in dynamic environments and testing in a new environment, the mobile robot is able to reach the target position successfully and have better performance comparing with fuzzy controller.

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Section: Introductionmentioning

confidence: 99%

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Hanh

Cong

2023

Journal of Robotics and Control

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“…In the present era of emerging wireless communication, Multiple input multiple output-MIMO antennas are the best solutions for enhancing the channel capacity and its reliable operation [1][2][3]. As the demand for high data rates with reliable communication increases, these MIMO antennas are the viable solution and are used for the operational frequencies in terms of GHz [4].…”

Section: Introductionmentioning

confidence: 99%

“…In the same way, MIMO wireless communication systems can be analyzed as a logical extension for the given antennas utilized for a long time for the improvisation of wireless communication systems [24,25]. As multiple antennas are used in MIMO wireless communication technology, it can considerably increase the channel capacity by governing or obeying Shannon's law [2]. Raising the count of antennas in the receiver and the count of antennas in the transmitter makes it possible to examine the linearity variation in the throughput of the channel's capability with all pairs of antennas added to the wireless communication system.…”

Section: Introductionmentioning

confidence: 99%

Six-element MIMO antenna structure with squared ring structure with multiband and high gain characteristics for C/X/Ku/K band applications

Almawgani,

Sorathiya,

Dabhi

et al. 2023

Phys. Scr.

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Using ring resonator structure, we have presented a structure of the six-element multiple input multiple output (MIMO) antenna structure. The proposed structure is numerically investigated and experimentally verified for antenna performance in the 1 to 25 GHz wideband frequency range. We have designed and verified the five antenna structure types, which consist of the various forms of ring/patch with different ground plane geometries. Various performance parameters like gain, directivity, reflections, and electric field distributions are analyzed for MIMO antennas. Essential performanceparameters observed for the MIMO antennas are also presented in this manuscript to identify the behaviour of the structure with different physical conditions. These parameters are also investigated for the identification of suitable ranges for the multi-channel transmission capability of the communication system. Return loss and Gain values are< -12 dB and ~ 20 dBi achieved with ~ 12 GHz of the operating bandwidth. Overall, the antenna offers minimum return loss values of −25 dB for the operating band of 19 to 25 GHz with anoperational bandwidth of 6 GHz. The designed antennas are tested using a ground patch and a rectangular patch in a scattered manner, and the obtained results are utilized in the Ultrawideband MIMO antenna applications. These antennas are also used in various band operations in satellite communication, including C/X/Ku/K bands.

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“…AI has significant application in many areas including:healthcare [8,25], UAVs, 5G and autonomous control [15], risk management [20,26], communication [14,11].…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Monitoring System for Personalized Home Medication

Ismail¹,

Fiorino²,

Abbas³

et al. 2023

Workshop Proceedings of the 19th International Conference on Intelligent Environments (IE2023)

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This paper introduces an Artificial Intelligence (AI)-enabled system to assist patients to follow a treatment plan at home. The deep learning model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The CNN model is trained for each patient based on his/her prescription medicine schedule. The advantage of the system is the dynamic functionality that makes it a good solution for personalized medication. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors.

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Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

Cited by 11 publications

References 39 publications

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Six-element MIMO antenna structure with squared ring structure with multiband and high gain characteristics for C/X/Ku/K band applications

Cloud-Based Monitoring System for Personalized Home Medication

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