Abstract
Considering that networks based on New Radio (NR) technology are oriented to provide services of desired quality (QoS), it becomes questionable how to model and predict targeted QoS values, especially if the physical channel is dynamically changing. In order to overcome mobility issues, we aim to support the evaluation of second-order statistics of signal, namely level-crossing rate (LCR) and average fade duration (AFD) that is missing in general channel 5G models. Presenting results from our symbolic encapsulation point 5G (SEP5G) additional tool, we fill this gap and motivate further extensions on current general channel 5G. As a matter of contribution, we clearly propose: (i) anadditional tool for encapsulating different mobile 5G modeling approaches; (ii) extended, wideband, LCR, and AFD evaluation for optimal radio resource allocation modeling; and (iii) lower computational complexity and simulation time regarding analytical expression simulations in related scenario-specific 5G channel models. Using our deterministic channel model for selected scenarios and comparing it with stochastic models, we show steps towards higherlevel finite state Markov chain (FSMC) modeling, where mentioned QoS parameters become more feasible, placing symbolic encapsulation at the center of cross-layer design. Furthermore, we generate values within a specified 5G passband, indicating how it can be used for provisioningoptimal radio resource allocation.
Highlights
IntroductionStarting from 5G New Radio (NR) access networks, the customization of particular radio links for a variety of services, traffic patterns, end-user demographics, and specific user experience of data [1], brings quality of service (QoS) metrics as prior demands, promoting 5G as the first QoS-driven radio network [2]
Starting from 5G New Radio (NR) access networks, the customization of particular radio links for a variety of services, traffic patterns, end-user demographics, and specific user experience of data [1], brings quality of service (QoS) metrics as prior demands, promoting 5G as the first QoS-driven radio network [2].Apart from previous networks, based on a general purpose framework (4G, 3G), the 5G NR approach is a customized, user-optimized concept for each use case and scenario, where particular throughput, latency, packet error rate, packet delay budget, or some other QoS metric is targeted [3]
Time-varying channel properties become more significant, especially in vehicle-to-vehicle (V2V) and other similar mobile scenarios. This leads to a key difference of 5G NR channels with respect to conventional cellular systems—non-stationarity [4]
Summary
Starting from 5G NR access networks, the customization of particular radio links for a variety of services, traffic patterns, end-user demographics, and specific user experience of data [1], brings quality of service (QoS) metrics as prior demands, promoting 5G as the first QoS-driven radio network [2]. Apart from previous networks, based on a general purpose framework (4G, 3G), the 5G NR approach is a customized, user-optimized concept for each use case and scenario, where particular throughput, latency, packet error rate, packet delay budget, or some other QoS metric is targeted [3]. Time-varying channel properties become more significant, especially in vehicle-to-vehicle (V2V) and other similar mobile scenarios. This leads to a key difference of 5G NR channels with respect to conventional cellular systems—non-stationarity [4]. First-order statistics such as probability density function (PDF) and cumulative distributive function (CDF) are sufficient for Entropy 2020, 22, 1151; doi:10.3390/e22101151 www.mdpi.com/journal/entropy
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