# A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling
## 2 Related Work  
We observe two sides of applications following the topic of LCR and AFD. One side considers relations to the higher network levels where, according to comprehensive survey [11], applications of LCR and AFD are used through Markov chain and decision process synthesis, handoff algorithms analysis to packet error rate (PER) evaluation [12], packet buffer length optimization, adaptive modulation and coding with interleaved and non-interleaved schemes creation, and throughput estimation for different communication protocols, with given reference to papers covering the matter. On the other side, many investigations are done to estimate these signal statistics regarding specific channel configurations [13; 14; 15] and various signal distributions [16]. Channel modeling for 5G has evolved during the past few years. Much effort was shown in the survey in [17] to aggregate the related topic. Related authors distinguish three approaches to the field: channel measurements, scenario-specific 5G channel modeling, and general 5G models. Only few measurements and scenario-specific channel models tackled LCR and AFD in order to characterize various aspects of the dynamic behavior of envelope fluctuations in [18], and use it for higher layer (FSMC) modeling scenarios [19; 20]. Although in [19], wideband was observed, neither of them included these metrics in the wideband aspect. In [21], the validity of the FSMC approach was confirmed by field measurements in a case-specific, high-speed train (HST) scenario, but there is not found in previous papers for the general 5G model. Furthermore, the complexity of analytical expressions derived in these scenario-specific models is often numerically intensive. From the general channel 5G modeling view, the survey also includes 3 GPP's study of actual channel model tools for next generation network evaluation and design [22]. First comes 3 GPP's NR (New Radio) channel model in the form of tapped delay line (TDL) and clustered delay line (CDL) channels and implemented in MATLAB's 5G Toolbox, bringing all relevant setups for testing, signal generation and analysis, as map-based propagation simulating scenarios [23]. In QuaDRiGa (QUAsi Deterministic RadIo channel GenerAtor) [24], a geometry-based stochastic channel model is used of multiple-input multiple-output (MIMO) radio links, providing all relevant parameters as absolute path delays, Doppler spread, channel impulse response (CIR), etc. Based on this model, Quamcom proposed a simulator [25] for further research and development of the 5G NR physical layer [26]. Statistical, cluster-based, modeling tool, COST 2100 [27], was developed and used for analysis, some time before ITU-R's vision of 2020 and beyond, focused on multi-user, distributed MIMO and moving Tx-Rx scenarios in the 4G communication system. The program METIS [28] belongs to the deterministic types of channel modeling tools and was intended to complement time-consuming and expensive measurement campaigns for overall 5G scenarios, offering a wide range of parameters and scenario's setup, but, as previous, finalizing just with PDF and CDF statistics. To mention at the end, but not as final on the list, the NYUSIM model [29], built up from extensive propagation measurements carried out during a time period of four years and proposed as a statistical spatial channel model using a time cluster-spatial lobe approach. Designed primarily for mmWave directional channels, as presented, it does not cover second-order statistics analysis, although fading behavior in this range is literally highly dynamic [30]. Among this group of models, only the more general 5G channel model (MG5 GCM) supports the four most challenging scenarios--massive MIMO, V2V, HST, and mmWave [31]. A common attribute for all of the mentioned work is the deficiency of the general channel modeling approach, in the sense of second-order statistics evaluation and translation to informational level models, what this work finds as area of contribution.