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Adaptive beamforming approach for secure communication in 5G network

The beamforming approach has been emerging as a very important concept for next generation networks. In addition to the improved channel capacity, spectral efficiency, energy efficiency, secrecy rate and secrecy outage probability, the upcoming fifth generation network mainly aims at enhancing the parameters of the channel for secure communication. In this paper, we have implied the allocation of resource blocks adaptively using HMM with a beamforming approach in an intruded network. A system model for secure communication in an intruded network has been discussed using a beamforming approach with the main motive being to provide a security scenario to the data which is transmitted over an unsecured channel in a network. In addition to this we have used the approach of HMM for allocating the resource blocks to the users which have been demanded and applied in order to avoid the intrusion and wastage of resource blocks.

Design and implementation of novel 6G cellular communication architecture using 3D modelling in real environments

Academics and businesses alike are presently fixated on the Sixth Generation (6G) network, which is seen as the telecom industry’s next major game-changer. The 6G architecture has not been finalized and is not yet being used in commercial settings. Research and development for 6G is still in its early stages. Several important features and technologies have surfaced as possible 6G system underpinnings, while development is still in its early phases. To facilitate the next generation of 3D modeling applications, this article suggests a new 6G cellular communication architecture. Aware of 6G’s revolutionary potential, we investigate its fundamental features to build a collaborative, real-time 3D modeling environment with unmatched capabilities. The goal of this project is to design the architecture for the next generation of communications systems. This will incorporate elements from two existing designs: the decoupled RAN design, which improves security and smooth data sorting, and the high-level design, which incorporates numerous protocols inside the antenna for stringent protection. Lastly, we delve into the possible sector-specific disruptions caused by this design and examine its wider implications for the future of 3D modeling. We hope that by introducing this new 6G architecture, we may inspire more study and development towards a day when 6G technology completely changes the game when it comes to 3D modeling.

Artificial neural network-based home energy management system for smart homes

Abdullahi Ijala, Olabode Idowu-Bismark, Jemitola Olugbeji, Ali Obadiah, Oluseun Oyeleke

Article ID: 2372
Vol 2, Issue 1, 2024

DOI: https://doi.org/10.54517/cte.v2i1.2372

Received: 10 November 2023; Accepted: 28 December 2023; Available online: 12 January 2024;
Issue release: 30 March 2024

VIEWS - 5366 (Abstract)

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Abstract

Energy efficiency is widely recognized as one of the most significant and economical ways to lower greenhouse gas (GHG) emissions. The aims and goals are that smart meters can evaluate and communicate in-depth real-time electricity usage, enable remote real-time monitoring and management of power consumption, and provide consumers with real-time pricing and analyzed usage information. The house energy management controller decides which loads will be powered based on the real home energy demands and the predefined load priorities. Artificial intelligence (AI) is being used increasingly in control applications due to its great effectiveness and efficiency. As a result, in this work, the author designed, simulated, and optimized an artificial neural network-based model simulation framework that simulates a home with a variety of home appliances and optimizes the total energy consumption of the home realistically through intelligent control of home appliances. The MATLAB application was used to model and examine the performance of four common household appliances: the water heater (WH), washing machine (WM), air conditioner (AC), and refrigerator (RG). The result shows a considerable reduction and savings in energy consumption without a decrease in consumer comfort.

Keywords

home energy management; smart home; artificial neural network; energy consumption; energy saving

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Qatar University, Qatar

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