Multivariate regressive deep stochastic artificial learning for energy and cost efficient 6G communication

Ramesh, S.; Ramachandran, Manikandan; Patan, Rizwan; Al‐Turjman, Fadi

doi:10.1016/j.suscom.2021.100522

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Power consumption and battery duration are common issues in wireless communication networks. Whereas, 6G is confronted with some new challenges, including but not limited to the following aspects [ 50 , 51 , 52 ].…”

Section: Applications and Challenges Of The Envisioned 6g Contextmentioning

confidence: 99%

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

et al. 2023

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With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions.

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Section: Applications and Challenges Of The Envisioned 6g Contextmentioning

confidence: 99%

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

et al. 2023

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“…Power optimization and energy-efficient methods must be designed to meet the challenges of next-generation networks. A fresh approach for 6G network was proposed by the author of [25] and is known as Multivariate Regressive Deep Stochastic Artificial Structure, learning to adjust the different data packets to improve communication that is energy and cost-conscious. In order to locate the useful node in the hidden layer, multivariate regression is performed.…”

Section: High Energy Efficiencymentioning

confidence: 99%

Review on Machine Learning for Intelligent Routing, Key Requirement and Challenges Towards 6G

Langpoklakpam,

Murry

2023

Comput. Networks Commun.

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The constant desire for faster data rates, lower latency, improved reliability, global device integration, and pervasiveness are some of the factors driving the development of next-generation communication systems. Sixth-generation (6G) networks have received a lot of attention from the industry and academics as fifth-generation (5G) communications are being rolled out globally. With the proliferation of smart devices and the Internet of Things (IoT), 6G networks will require ultra-reliable and low-latency communication. Routing protocols have a significant role in improving the performance of a network. Traditional routing techniques will have difficulty coping with the highly complex and dynamic 6G environments. Recently, machine learning (ML), a key component of artificial intelligence, is emerging as the key to managing complex and dynamic networks efficiently. However, there are still several significant challenges that need to be addressed. In this paper, we provide an overview of current machine-learning techniques used in network routing. Lastly, we highlight open research problems that need to be addressed and prospects for future research.

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“…The utilization of multi-UAVs is a significant model to combat the distribution of RPW in palm plantations. In order to improve the energy consumption and cost-aware transmission, a novel method named MRDSASL was presented in 6G network [19]. Then, multi-variate regression function was utilized as a threshold to analyze the evaluated node condition.…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

Hans¹,

Ghosh²,

Kataria³

et al. 2022

Computers, Materials &Amp; Continua

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With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with Routing Planning for 6G-enabled UAV communication (EADCRP-6G) technique. The goal of the proposed EADCRP-6G technique is to conduct energy-efficient cluster-based data collection and optimal route planning for 6G-enabled UAV networks. EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) and organize these clusters. Besides, Artificial Fish Swarm-based Route Planning (AFSRP) technique is applied to choose an optimum set of routes for UAV communication in 6G networks. In order to validated whether the proposed EADCRP-6G technique enhances the performance, a series of simulations was performed and the outcomes were investigated under different dimensions. The experimental results showcase that the proposed model outperformed all other existing models under different evaluation parameters.

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Multivariate regressive deep stochastic artificial learning for energy and cost efficient 6G communication

Cited by 8 publications

References 24 publications

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

Review on Machine Learning for Intelligent Routing, Key Requirement and Challenges Towards 6G

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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