review · Heliyon
Designing reliable indoor 5G and future 6G wireless networks requires accurate path loss modelling to properly budget signal strength and ensure high-quality connectivity. Channel modelling at millimetre wave frequencies often produces variable network capacity estimates depending on the specific model applied. A comprehensive review examined millimetre wave measurement studies and compared the performance of three standard models: the Close-In free space reference model, the Floating Intercept model, and the Alpha-Beta-Gamma model across line-of-sight and non-line-of-sight conditions. The assessment shows that both the Close-In free space reference model and the Floating Intercept model deliver the best performance for indoor millimetre wave propagation. Identifying these models assists network planners in balancing model accuracy with ease of implementation when forecasting indoor coverage.
Indoor environments present complex obstacles that disrupt high-frequency millimetre wave signals. Determining reliable mathematical models for how signals weaken over distance allows network designers to calculate precise link budgets. This ensures indoor spaces achieve dependable high-speed wireless connectivity for current 5G deployments and prospective 6G architectures without costly over-engineering or coverage gaps.
This research supports telecommunications equipment vendors, network planning engineers, and indoor infrastructure providers in selecting optimal path loss models for 5G and early-stage 6G deployments. Because the findings are based on a comparative literature survey and secondary measurement analyses, the work represents early-stage to applied network planning methodology, assisting technical teams in refining simulation tools before deploying physical hardware in enclosed environments.
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Millimeter wave path loss modeling is essential for reliable system design and accurate link budget calculations. The motivation for this research is that channel modeling in 5G millimeter wave propagation in an indoor environment is a current research topic in which capacity differences have been noticed as a result of different models being utilized. Existing models for future millimeter wave propagation must be tested and improved in order to aid link design. The improvements in the path loss models will allow engineers and researchers to budget for 5G wireless networks with better quality in an indoor environment. In this paper, we discuss the survey of indoor environment undertaking for both line of sight (LOS) and non-line of sight (NLOS) scenarios as well as the comparison of path loss performance analysis of the three commonly used models: Close-In (CI) free space reference model, Floating Intercept (FI), and Alpha-Beta-Gamma (ABG) models at some selected frequencies. The review looked at how to determine efficient path loss models which is a major challenge in millimeter wave propagation. The paper also focuses on the measurement work done in millimeter wave research in interior environments. The analysis of path loss and shadow fading in different frequency bands are presented. The researchers whose publications were examined for this study used a range of methodologies to forecast path loss models and propagation parameters of millimeter wave communication channel. This will help design engineers and researchers calculate budgets for a suitable 5G and even forecasted 6G wireless network in an inside environment. Another purpose of this paper is to get a thorough understanding of the best route loss model, especially for interior situations, and to improve it in future research to provide a better line of fit and simplicity among the three fundamental path loss models: CI, ABG, and FI. In both LOS and NLOS scenarios, the study found that the CI free space reference model and the FI path loss models are the best path loss models for indoor millimeter wave propagation. Future research will focus on how to improve the appropriate model for path loss model estimate in both LOS and NLOS situations in an indoor environment with the best line of fit and the easiest implementation.
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DOI: 10.1016/j.heliyon.2022.e11581
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