Figure 4 - uploaded by Md. Maruf Ahamed
Content may be subject to copyright.
Source publication
The 5G cellular network is no longer hype. Mobile network operators (MNO) around the world (e.g., Verizon and AT&T in the USA) started deploying 5G networks in mid-frequency bands (i.e., 3–6 GHz) with existing 4G cellular networks. The mid-frequency band can significantly boost the existing network performance additional spectrum (i.e., 50 MHz–100...
Contexts in source publication
Context 1
... higher-frequency bands, the individual 5G small cell site will have 100 m (in radius) cell coverage. When the MNOs need to deploy more than one small cell to cover the deployment area, the adjacent small cell location must maintain a certain distance called inter-site distance (ISD), shown in Figure 4 [16]. The users can get more than one dominant receive signals from the nearby cell sites if the minimum ISD is not maintained, called outof-cell interference, which can significantly degrade the network performances. ...
Context 2
... paper considers the dense urban environment for the network planning and coverage analysis. In the test environment, the network layout consists of 19 sites (i.e., tier 2) placed in a hexagonal layout, each with three sectors, as shown in Figure 4. ...
Citations
... The mmWave frequency spectrum was brought under the fifth generation (5G) category by the World Radiocommunication Conference (WRC) in 2019. The applications utilizing this spectrum expect a minimum bandwidth support of 400 to 800 MHz with a data rate of 1-20 Gbps through various modulation techniques [3,4]. In this spectrum, the 28 and 38 GHz bands are licensed for mobile and satellite applications as they are more suitable for long-range communication. ...
A double-stub matching technique is used to design a dual-band monopole antenna at 28 and 38 GHz. The transmission line stubs represent the matching elements. The first matching network comprises series capacitive and inductive stubs, causing impedance matching at the 28 GHz band with a wide bandwidth. On the other hand, the second matching network has two shunt inductive stubs, generating resonance at 38 GHz. A Smith chart is utilized to predict the stub lengths. While incorporating their dimensions physically, some of the stub lengths are fine-tuned. The proposed antenna is compact with a profile of 0.75λ1×0.66λ1 (where λ1 is the free-space wavelength at 28 GHz). The measured bandwidths are 27–28.75 GHz and 36.20–42.43 GHz. Although the physical series capacitance of the first matching network is a slot in the ground plane, the antenna is able to achieve a good gain of 7 dBi in both bands. The proposed antenna has a compact design, good bandwidth and gain, making it a candidate for 5G wireless applications.
... Authors in [8] incorporated cost considerations into their allocation algorithm for optimal VPNF placement in mobile virtual cores. Authors in [9] examined different mathematical methods used to optimize the planning and deployment of 5G networks for better performance and efficiency. In [10], a secure and trustworthy system for cloud computing using blockchain technology, which creates a hierarchical structure for managing trust among different users and devices, was proposed. ...
The rise of 5G technology has brought with it a surge in diverse services with demanding and varying requirements. Network fragmentation has emerged as a critical technique to address this challenge, enabling the creation of virtual network segments on a shared infrastructure, allowing for efficient resource utilization and improved performance. This paper investigates the potential of network fragmentation, combined with optimized resource allocation, to enhance the performance of 5G core networks. A novel framework that integrates these two techniques is proposed. The former takes into account factors, such as network traffic patterns, service requirements, and resource availability. The framework aims to optimize network performance metrics, namely throughput, latency, and resource utilization. The experimental results demonstrate the effectiveness of the proposed framework, showcasing a significant improvement in overall network performance, paving thus the way for efficient and robust 5G service delivery.
... Table 1 shows a comparison of different technology generations. We are presently engaged in the development and execution of the necessary infrastructure for extensive deployment of 5G coverage [24]. ...
p>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.</p
... Although the academic and industrial research communities have extensively explored the potential of 5G, focusing on its technological capabilities, potential applications, and societal benefits, there is a notable gap in the literature regarding the systematic and strategic planning required for the effective deployment of 5G networks [4]. While numerous studies have highlighted the challenges associated with 5G deployment, there is a lack of research offering concrete, methodological solutions to these challenges [5,6]. In particular, the strategic prioritization of service areas-a key aspect of network deployment that directly impacts service quality and network coverage-has not been adequately addressed [7]. ...
In the rapidly evolving telecommunications landscape, the shift towards advanced communication technologies marks a critical milestone. This transition promises to revolutionize connectivity by enabling seamless data downloads, high-quality video streaming, and instant access to applications. However, adapting to these advanced technologies poses significant challenges for infrastructure expansion, requiring innovative investment and deployment strategies. These strategies aim not only to enhance service quality but also to ensure extensive network coverage. To address the need for systematic planning in infrastructure investment, this paper presents a novel methodology that combines the Full Consistency Method (FUCOM) with cosine similarity analysis. This integrated approach effectively prioritizes service areas for the deployment of 5G technology, emphasizing the importance of detailed planning in mobile strategy development. By leveraging FUCOM to determine the weights of various criteria and employing cosine similarity analysis to rank service areas, the methodology facilitates efficient resource allocation and service quality enhancements. Empirical validation using real data from a Turkish telecommunications company confirmed the effectiveness of the proposed algorithm. The results indicate that this integrated approach can significantly advance the telecommunications industry by providing essential insights for companies seeking to improve service quality amidst the transition to 5G and beyond. The successful implementation of the proposed algorithm demonstrates its effectiveness in addressing the challenges faced by telecommunications companies and underscores the importance of a data-driven approach in strategic decision-making and resource allocation. Furthermore, the findings suggest that the integrated FUCOM and cosine similarity analysis approach can offer a valuable tool for telecommunications companies worldwide, offering a systematic method for prioritizing infrastructure investments and enhancing network performance.
... As the current 5G infrastructure in Kiel does not allow the use of millimeter-wave base stations and low-frequency bands, the range does not extend beyond 2-3 kilometers from the coastline. However, the exact range may be affected by factors such as signal propagation, antenna configuration, and the placement of 5G infrastructure along the coast [4]. ...
... A comparison of research on 5G networks, coverage and throughput performance is summarized in Table 14. Of the twelve publications on 5G network coverage and/or throughput analyses, simulation software employed includes Atoll [31], [44], [45], Mentum Planet [30], [35], [39], [42], [43], Matlab [32], [41], and Omnet++ [46]. In addition to simulation, two papers [31], [45] also conducted direct field measurements (drive test method) to verify the simulations. ...
... Maximum SS-RSRP, SS-SINR, and data rate become performance metrics in [39]. Other criteria are employed, such as network availability and reliability [32], as well as pathloss [30], [35]. While SS-RSRP, SS-SINR and throughput parameters are more common to be chosen as KPI parameters, the use of average or maximum values does not represent the real costumers' experience and network performance. ...
... 5G Frequency Band[30]-[32] ...
... 3.4.0 [13] [14] [15]. Dalam ...
Implementasi jaringan 5G New Radio (NR) di Indonesia belum menyeluruh dan baru direalisasikan di beberapa kota besar, salah satu frekuensi yang digunakan adalah 2300 MHz dengan bandwidth 50 MHz. Teknologi seluler 5G memberikan tingkat layanan yang lebih baik daripada 4G, yaitu dari rerata kelajuan data sampai dengan 100 Mbps, latensi rendah sampai dengan 1 ms terhadap pengguna layanan. Pada penelitian ini dilakukan analisa dan perencanaan cakupan dan kapasitas jaringan 5G NR di Kota Semarang menggunakan perangkat lunak radio planning Atoll. Penggelaran gNodeB 5G NR dilakukan dengan memanfaatkan eNode 4G LTE 1800 MHz yang telah tersedia menggunakan model propagasi 3GPP TR 38.901 dengan metode Urban Macro (UMa) Line of Sight (LOS). Hasil simulasi menunjukkan bahwa rerata throughput 5G setelah dilakukan penggelaran menghasilkan dengan nilai sebesar 112 Mbps downlink dan 102 Mbps uplink. Untuk cakupan area simulasi jaringan 5G nilai SINR (Signal to Noise Interference Ratio) telah memenuhi target KPI (Key Performance Indicator) operator, sedangkan nilai RSRP (Reference Signal Receive Power) masih dibawah nilai KPI. Setelah dilakukan penggelaran, trafik pengguna untuk jaringan 5G dapat diakses oleh pengguna sampai dengan 98% dengan maksimal throughput tertinggi untuk downlink sebesar 264 Mbps dan uplink sampai dengan 372 Mbps.
... Therefore, correct dimensioning of the system and optimization of the use of its resources are important [5,6]. For this reason, 5G networks, especially their mathematical modeling, are the focus of extensive scientific research [7][8][9][10][11][12]. Reference [7] introduces a versatile mathematical methodology to assess performance reliability improvement algorithms for 5G systems. ...
... Reference [11] proposed a new optimization algorithm that corrects the coverage results and restores the true value of 5G coverage. Reference [12] outlined the planning for 5G coverage, initially using a conventional three-sector cell, and suggested an enhanced cell structure featuring six sectors. This updated configuration incorporates an advanced antenna system to deliver improved 5G coverage. ...
Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this article is to analyze traffic levels, aiming to optimize traffic management and handling. This article considers the two most popular CAC mechanisms: the resource reservation mechanism and the threshold mechanism. It presents an analytical approach to occupancy distribution and blocking probability calculation in 5G mobile networks, incorporating connection handoff and CAC mechanisms for managing multiple traffic streams generated by multi-service sources. Due to the fact that the developed analytical model is an approximate model, its accuracy was also examined. For this purpose, the results of analytical calculations of the blocking probability in a group of 5G cells are compared with the simulation data. This paper is an extended version of our paper published in 17th ConTEL 2023.
... However, the successful deployment and optimization of 5G networks require accurate prediction of network coverage. The ability to predict coverage is crucial for efficient network planning and optimization, as it helps network operators to identify coverage gaps, optimize network resources, and improve the quality of service for end-users [2]. ...
5G technology is a key factor in delivering faster and more reliable wireless connectivity. One crucial aspect in 5G network planning is coverage prediction, which enables network providers to optimize infrastructure deployment and deliver high-quality services to customers. This study conducts a comprehensive analysis of machine learning algorithms for 5G coverage prediction, focusing on dominant feature parameters and accuracy. Notably, the Random Forest algorithm demonstrates superior performance with an RMSE of 1.14 dB, MAE of 0.12, and R2 of 0.97. The CNN model, the standout among deep learning algorithms, achieves an RMSE of 0.289, MAE of 0.289, and R2 of 0.78, showcasing high accuracy in 5G coverage prediction. Random Forest models exhibit near-perfect metrics with 98.4% accuracy, precision, recall, and F1-score. Although CNN outperforms other deep learning models, it slightly trails Random Forest in performance. The research highlights that the final Random Forest and CNN models outperform other models and surpass those developed in previous studies. Notably, 2D Distance Tx Rx emerges as the most dominant feature parameter across all algorithms, significantly influencing 5G coverage prediction. The inclusion of horizontal and vertical distances further improves prediction results, surpassing previous studies. The study underscores the relevance of machine learning and deep learning algorithms in predicting 5G coverage and recommends their use in network development and optimization. In conclusion, while the Random Forest algorithm stands out as the optimal choice for 5G coverage prediction, deep learning algorithms, particularly CNN, offer viable alternatives, especially for spatial data derived from satellite images. These accurate predictions facilitate efficient resource allocation by network providers, ensuring high-quality services in the rapidly evolving landscape of 5G technology. A profound understanding of coverage prediction remains pivotal for successful network planning and reliable service provision in the 5G era.
... Decoupling can increase network flexibility by allowing for a more dynamic allocation of resources. With the traditional approach, resources are shared between the downlink and uplink on a fixed basis, which can lead to inefficient use of resources [5]. Decoupling allows for more dynamic resource allocation based on each channel's specific needs, improving flexibility and efficiency. ...
... In addition, decoupling can also increase the complexity of network management and optimization. To evaluate the impact of decoupling on 5G networks, Cortes [5] conducted a literature review and analyzed the results of several studies. Cortes identified several key factors that can affect the performance of decoupling, including the type of traffic (e.g., delay-sensitive or delaytolerant), the density of user equipment, and the network topology. ...
... Decoupling can also improve network flexibility by allowing for more efficient allocation of resources. At [5] the authors conducted a comprehensive survey on uplinkdownlink decoupling in 5G networks. The authors discussed the potential benefits of decoupling network flexibility, including dynamically allocating resources based on traffic demands and support for new services and applications. ...
