Comparative Study of Efficiency Enhancement Technologies in 5G Networks - A survey

Ibrahim, Salah; Mabrook, M. Mourad; Hussein, Aziza I.; Rahouma, Kamel H.

doi:10.1016/j.procs.2021.02.020

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…In some work, energy utilization issues were analyzed for diverse communication networks. Even though the minimizing transmission power of BS stores the total power utilization, the model as well as the network performance optimization minimizes the energy utilization [2].…”

Section: Multimedia Research (Mr)mentioning

confidence: 99%

“…In 2021, Ibrahim Salah et al [2], worked on the development of mobile communication networks initiating from 1 st -generation to 5 th generation with evaluation studies. Subsequently, the evolution of 5G was considered the next generation and it was summarized by the current research schemes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As stated in eq. ( 1), the corresponding SINR is formulated if u cell edge user is supplied by means of m on subcarrier n which 2  represents thermal noise. Conversely, on the basis of eq.…”

Section: System Modelmentioning

confidence: 99%

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Wireless Communication Network using Hybrid WOA and GWO Algorithm

Gajawada¹

2021

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In subsequent generation networks, to perform an improved communication the Orthogonal Frequency Division Multiple Access (OFDM), as well as Multiple Input-Multiple Output (MIMO) systems, are integrated which makes easy the spatial multiplexing on Resource Blocks (RBs) on the basis of time-frequency. A novel approach to interference alleviation was developed in this paper for 3D antenna array models in OFDMA as well as multi-cell MIMO wireless networks. Therefore, in the 3D MIMO-OFDM system, the Dynamic vertical beamforming is exploited to the cell degree separation user-specific down tilts user. The major contribution of this paper is to maximize the cell edge user's throughput as well as cell center users. Here, the single objective function is the specified multi-objective model that is resolved using the adoption of a novel enhanced approach. This optimization issue is solved using the fine-tuning of particular parameters like RB allocation, allocated power for RB, and cell edge user. A hybrid Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO) algorithm referred to as the Hybrid WOA-GWO approach is used to attain the fine-tuned parameters. At last, the superiority of the adopted technique is examined with existing techniques on the basis of throughput, etc.

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Section: Multimedia Research (Mr)mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Wireless Communication Network using Hybrid WOA and GWO Algorithm

Gajawada¹

2021

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“…In this paper, EE and SE conditions and boundaries for data transmission are mainly being studied [13]. The proposed system model consists of a single-cell [14,15] where one Massive-MIMO antenna BS simultaneously serving a set of User Equipment UE as shown in fig.…”

Section: System Designmentioning

confidence: 99%

“…The block diagram of the proposed system is depicted in Figure (2). The Maximum Energy Efficiency Estimator block is firstly supplied by the number of active users [18,19] served by the cell [20] using the base station database or using spectrum sensing techniques [18,22]. Then, equation ( 4) is used to estimate Max.…”

Section: Fig2 Block Diagram Of the Proposed Adaptive Antenna Selection Systemmentioning

confidence: 99%

Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

Ibrahim

Mabrook

Rahouma

et al. 2021

Preprint

Self Cite

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Given that the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell MMIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is estimated according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

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Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks

Gunasekaran,

Dhanasekaran,

Kumar

et al. 2024

Int J Communication

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ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

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Comparative Study of Efficiency Enhancement Technologies in 5G Networks - A survey

Cited by 21 publications

References 22 publications

Wireless Communication Network using Hybrid WOA and GWO Algorithm

Wireless Communication Network using Hybrid WOA and GWO Algorithm

Energy Efficiency Optimization in Adaptive Massive MIMO Networks for 5G Applications Using Genetic Algorithm

Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks

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