A survey on the applications of machine learning, deep learning, and reinforcement learning in wireless communications

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.

A survey on the applications of machine learning, deep learning, and reinforcement learning in wireless communications

Huu Q. Tran, Viet-Thanh Pham, Sy Ngo

Article ID: 3170
Vol 3, Issue 1, 2025

DOI: https://doi.org/10.54517/cte3170

Received: 18 December 2024; Accepted: 26 February 2025; Available online: 20 March 2025; Issue release: 31 March 2025

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Abstract

This survey explores the integration of machine learning (ML), deep learning (DL), and reinforcement learning (RL) within wireless communications. It reviews various methods, algorithms, and applications while addressing the challenges and future research directions in this field. The paper highlights the necessity of intelligent techniques to enhance the performance and management of wireless networks, driven by the increasing complexity and demand for higher efficiency. Key areas of focus include network optimization, resource management, security, signal recognition, channel coding, traffic prediction, access control, and energy optimization. The survey also discusses emerging techniques such as federated learning, transfer learning, and multi-agent reinforcement learning, emphasizing their potential to revolutionize wireless communication systems.

Keywords

DL; edge intelligence; FL; IoT; MARL; ML; RL

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Qatar University, Qatar

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