Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks

Al-Samman, Ahmed M.; Rahman, Tharek Abd; Al-Hadhrami, Tawfik; Daho, Abdusalama; Hindia, M. H. D. Nour; Azmi, Marwan Hadri; Dimyati, Kaharudin; Alazab, Mamoun

doi:10.3390/electronics8010044

Cited by 36 publications

(29 citation statements)

References 37 publications

(54 reference statements)

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“…$% , 5 PL in dB at a close-in (CI) distance, 5 is 1 m, žŸ (» is the modelling of the shadow fading (SF) as a zero-mean Gaussian-distributed random variable, Ÿ symbolizes the standard deviation (shadowing effect). The Minimum Mean Square Error (MMSE) method is used for the derivation of PLE and minimum standard deviation [125].…”

Section: Path Loss Modelsmentioning

confidence: 99%

“…where ¹ and ˆ are constant coefficients indicating the effect of frequency and distance on PL, Á is denoted as an optimized offset value for PL, is the carrier frequency and ž / • is a Gaussian random variable with a standard deviation of Ÿ / • . The ABG model is resolved by the MMSE method to reduce Ÿ by simultaneously calculating for ¹, Á, and ˆ [125].…”

Section: U Alpha-beta-gamma (Abg) Modelmentioning

confidence: 99%

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Cellular Communications Coverage Prediction Techniques: A Survey and Comparison

et al. 2020

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A lot of effort and time is utilized in the planning and building of the cellular wireless networks to use minimum infrastructural components to provide the best network coverage as well as delivery of quality of service. Generally, path loss models are used for the prediction of wireless network coverage. Therefore, detailed knowledge of the appropriate path loss model suitable for the proposed geographical area is needed to determine the coverage quality of any wireless network design. However, to the best of our knowledge, despite the importance of path loss models, as used for the prediction of wireless network coverage, there doesn't exist any comprehensive survey in this field. Therefore, the purpose of this paper is to survey the existing techniques and mechanisms which can be addressed in this domain. Briefly, the contributions of this paper are: (1) providing a comprehensive and up to date survey of the various network coverage prediction techniques, indicating the different frequency ranges the models were developed, (2) the different suitable terrains for each of the model and the best suit mobile generation were presented, and lastly, (3) providing comparative analysis to aid the planning and implementation of the cellular networks. INDEX TERMS Path loss model, prediction, wireless, propagation scenarios, mobile generations, signal. I. INTRODUCTION The remarkable changes experienced by the development of mobile communication system over the last few years has led to severe challenges to the planning of mobile wireless networks. This fruition journey of the first-Generation (1G) network started back in the year 1979 and has progressed to the presently explored Fifth Generation (5G) network. Each new generation is usually built upon the present generation's needs, which led to research and development for a better technology that will accommodate the needs, capacities, proper availability to the end-user. With this exponential increase in the use of mobile-connected devices as well as the constant expansion of mobile communication networks, the effective provision of the coverage of the mobile networks is imperative for the delivery of quality of service (QoS) [1]. Radio propagation can be defined as the behaviour of radio waves experienced while signals are transmitted from one point to another [2]. Such phenomena like absorptions, reflections, scattering, refractions, among others, affect the radio wave [3]. Therefore, mobile network coverage prediction is a vital and essential task in the planning and deployment of cellular technology.

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Section: Path Loss Modelsmentioning

confidence: 99%

Section: U Alpha-beta-gamma (Abg) Modelmentioning

confidence: 99%

Cellular Communications Coverage Prediction Techniques: A Survey and Comparison

et al. 2020

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“…In the inverse power-law path loss models, the impacts of the environment (outdoor, indoor, rural, urban, suburban, etc.) on the path loss is reflected by the value of the PLE [36][37][38][39]. The path loss models of interest are defined as follows.…”

Section: Propagation Modelmentioning

confidence: 99%

Optimizing the Number of Fog Nodes for Finite Fog Radio Access Networks under Multi-Slope Path Loss Model

et al. 2020

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Fog Radio Access Network (F-RAN) is a promising technology to address the bandwidth bottlenecks and network latency problems, by providing cloud-like services to the end nodes (ENs) at the edge of the network. The network latency can further be decreased by minimizing the transmission delay, which can be achieved by optimizing the number of Fog Nodes (FNs). In this context, we propose a stochastic geometry model to optimize the number of FNs in a finite F-RAN by exploiting the multi-slope path loss model (MS-PLM), which can more precisely characterize the path loss dependency on the propagation environment. The proposed approach shows that the optimum probability of being a FN is determined by the real root of a polynomial equation of a degree determined by the far-field path loss exponent (PLE) of the MS-PLM. The results analyze the impact of the path loss parameters and the number of deployed nodes on the optimum number of FNs. The results show that the optimum number of FNs is less than 7% of the total number of deployed nodes for all the considered scenarios. It also shows that optimizing the number of FNs achieves a significant reduction in the average transmission delay over the unoptimized scenarios.

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“…In fact, some studies have been done, and describe that the bands below 6 GHz work as the main access link for indoor communications, as well as how these will bring more than 600 MHz available for 5G deployment. [9], [11][12].…”

Section: Introductionmentioning

confidence: 99%

Path Loss Characterization in an Indoor Laboratory Environment at 3.7 GHz in Line-Of-Sight Condition

et al. 2020

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The objective of this work is to propose experimental path loss propagation models for communication channels in indoor environments. In this sense, an experimental path loss characterization has been achieved, according to the measurements campaign carried out in a typical scenario of a university campus. These narrowband measurements were collected in the laboratory environment at 3.7 GHz in line-of-sight (LOS) condition. Also, these measurements were carried out at night to simulate stationary channel conditions. Thus, the results obtained show the values of the parameters of the close-in (CI) free space reference distance and floating-intercept (FI) path loss models, in terms of the transmitter and receiver separation distance. It should be noted that these values of the path loss models have been extracted applying linear regression techniques to the measured data. Also, these values agree with the path loss exponent values presented by other researchers in similar scenarios. The path loss behavior can be described with the implementation of these models. However, more measurement campaigns are needed to improve the understanding of propagation channel features, as well as to obtain better precision in the results obtained. This, in order to optimize the deployment and performance of next fifth-generation (5G) networks that combine indoor environments to offer their services and applications.

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Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks

Cited by 36 publications

References 37 publications

Cellular Communications Coverage Prediction Techniques: A Survey and Comparison

Cellular Communications Coverage Prediction Techniques: A Survey and Comparison

Optimizing the Number of Fog Nodes for Finite Fog Radio Access Networks under Multi-Slope Path Loss Model

Path Loss Characterization in an Indoor Laboratory Environment at 3.7 GHz in Line-Of-Sight Condition

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