Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks

Krishnamoorthy Suresh, Raju Kannadasan (), Stanley Vinson Joshua, Thangaraj Rajasekaran, Mohammed H. Alsharif, Peerapong Uthansakul () and Monthippa Uthansakul
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Krishnamoorthy Suresh: Department of Information Technology, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India
Raju Kannadasan: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India
Stanley Vinson Joshua: Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
Thangaraj Rajasekaran: Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602117, India
Mohammed H. Alsharif: Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea
Peerapong Uthansakul: School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Monthippa Uthansakul: School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Sustainability, 2024, vol. 16, issue 17, 1-15

Abstract: Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%.

Keywords: Hybrid Quantum Deep Learning; Base Station Optimizer net; dynamic allocation strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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