Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks
Abstract
:1. Introduction
- A.
- 5G Characteristics
- B.
- 5G Standards
- C.
- 5G Architecture
Data Flow
2. Related Work
- The suggested urban-suburban model is new concerning the 5G application. The performance of the 5G network is studied concerning different parameters that affect the performance such as the non-orthogonality factor, the height of the mobile, the height of the base station, and the effect of load concentration. Moreover, comparative results are conducted to compare the realistic urban-suburban network with the non-realistic ideal free-space network.
- The main outcome and contribution of this work can be formulated as follows:
- ○
- The main goal of this research is to study the effect of different performance indicators on 5G performance and capacity. These indicators can be introduced to mobile operators to be considered as planning factors in the design of 5G network. Therefore, with the proposed model in this research, the end-user can have continuous connectivity under different propagation environments.
- ○
- It is shown in this research that studying 5G networks in more realistic environments degrades the network performance in areas where the load is concentrated. Therefore, a proper network-level call admission control algorithm that balances the load and manages the network resources is strongly needed. This is a very important issue to be considered by the research community while studying such systems.
- ○
- The introduced urban/suburban model in this research is very important because firstly, the real capacity of 5G is based on areas where the load and the density of the traffic is high. Secondly, the infrastructure of 5G which is based on mmWave can cover only short distances. Therefore, to ensure that networks provide connections with high-data throughput, low latency and other features already guaranteed by 5G, the focus will be on realistic small cell base stations.
- ○
- This research work also demonstrated the effect of the distance between base stations on the network capacity since capacity is a very critical issue in 5G. This issue is considered an optimization factor regarding cost performance and must be considered in the planning of 5G design and infrastructure.
- Different traffic scenarios and distributions should be considered in future work such as uniform and on-uniform traffic distribution over the network.
- To study the model in a more realistic status, the mobility model can be integrated into the model considering the seamless soft-handover principle of 5G between the target cell and the neighboring cells.
- I did not find any similar model scenario in the literature, otherwise, the results of the proposed model can be compared with other people’s work.
3. The Proposed Model
3.1. Modeling Assumptions
- The arrival process of the session over the whole network is assumed to be a Poisson process.
- The traffic is assumed to be uniformly distributed over the coverage area of each Node B.
- The service time is modeled by a Pareto random variable to represent the WWW nature of the traffic. The service time is modelled as a Pareto random variable which represents the self-similar behavior of WWW traffic. During a packet call, several packets may be generated, which means that the packet call establishes a burst sequence of packets. After the document has entirely arrived at the terminal, the user consumes a certain amount of time to study the information. This time interval is called reading time (s). Pareto parameters are obtained from the 3GPP standard [38] and given in Table 2.
- Two traffic scenarios are assumed: Homogeneous and Hotspot. In the homogeneous case, the load is equal for all cells. In the hotspot scenario, we assign twice as many calls to the central cell than each of the other six border cells.
- The mobile station is assumed to be stationary. Mobility is not considered in the current analysis.
- All cells are assumed to be the same size.
- The call admission control algorithm (CACA) proposed in [39] is applied in this study. The analysis is based on the soft handover principle in 5G networks, which allows the user equipment (UE) to connect gently with many Node Bs at the same time, as illustrated in Figure 4. Given that the signals will be conveyed, no resources will be granted until the UE meets the admission requirements defined by Node B, which are characterized in our model as the minimum bit rate threshold and minimum distance. This research effort assumes multi-cell deployment, as indicated in Figure 3, where the user equipment (UE) is softly connected with more than one Node B at the same time, However, during the soft handover process, UE will only be linked to one Node B based on the strongest signal, which is computed in the model using a threshold of minimum bit rate, minimum distance, and interference levels between the cell and other surrounding cells. The two parameters needed for the CACA are the maximum distance, dmax and the maximum number of active users, nmax. Note that all symbols and associated descriptions used in the following equations are listed in Table 1.
Parameter | Value |
---|---|
Average Packet size | 480 bytes |
Average requested file size (25 × 480 bytes) | 12 Kbytes |
The average number of packet calls within a session | 5 |
Average reading time between packet calls | 412 s |
The average amount of packets within a packet call | 25 |
Average inter-arrival time between packets | 0.0625 s |
Wavelength,λ | 0.15 m |
Signal-to-Noise Ratio, SNR | 2 dB |
Thermal Noise, N0 | −103 dBm |
Distance between Nodes B, d | 1000 m, 4000, 7000 m |
Number of codes, N | 64 |
Frequency, f | 20 GHz |
Height of the mobile, hm | 2 m, 5 m |
Height of the base station, hb | 50 m, 100 m |
Service Factor, S(SF/SINR) | 16, 32, 64, 128 |
Cell radius, r | 1, 7 km |
Spreading factor, SF | 32, 64, 128, 256 chips/symbol |
Maximum transmission power, Psmax | 125 W |
Non-orthogonality factor,ε | 0.30, 0.40, 0.50 |
Traffic intensity over the network (Erlang), λ/µ | 200, 250, …, 450 |
- λ is the wavelength
- PSmax is the maximum transmission power of the UE
- N0 is the thermal noise.
- S is the spreading factor (the ratio between the bandwidth of the user signal and the transmitted bandwidth) = Spreading factor (SF)/Signal-to-Noise ratio (SNR).
- ε is the non-orthogonality factor (interference factor).
- Assuming that the cell coverage is defined by a radius, r then the maximum number of active users in the cell can be defined as:
3.2. Model Solution by MOSEL-2
- First, the high-level system description is created using the MOSEL-2 tool in a simple C-like language. The model description is saved as “filename.mos”, which specifies the intended performance measures. Without programmer intervention, the described model is transferred to the assessment environment, where all subsequent steps are carried out.
- Second, the model is turned into a tool for creating input files using MOSEL-2 environment. This tool can be either C-based Stochastic Petri Net (C-Based SPNP) or TimeNet. After selecting a specific tool, the MOSEL-2 environment will invoke the tool.
- Third, the utility handles the input file in the following two ways, depending on options given in the command-line arguments.
- (a).
- Numerical analysis: during this analysis, the entire state space of the system is produced using the modeling language’s semantic rules. The obtained semantic model is linked to the stochastic process. The stochastic process will then be solved using the numerical solution algorithms that are accessible.
- (b).
- Simulation: the tool will evaluate the model using discrete event simulation, eliminating the need to construct the state space.
- The obtained results from discrete event simulation or numerical solutions are saved in a file with the tool structure.
3.3. Capacity Bounds Derivation
- where,
- is a propagation loss in environment of type E, in dB. E is 1 for urban and 2 for suburban.
- is the correction factor for each environment, E: E = 0 for urban and suburban models.
- f is the frequency of the transmission in MHz.
- hb is the height of base station or transmitter in meters.
- hm is the height of the mobile or receiver in meters.
- d is the distance between the transmitter and the receiver in kilometers.
- a (hm) is the mobile antenna correction factor.
- Therefore,
- 1.
- The propagation model for the urban environment is given as:
- where
- for the urban environment.
- 2.
- The propagation model for the urban environment is given as:
- where,
- is the transmitted power and is the received power.
- Moving the fraction to the left hand side, (7) can be rewritten as:
- where,
- is a function of the height of the mobile, hm, the height of the base station, hb and the frequency, f.
- is the correction parameter for each type of environment, E, which is a function based on the height of the mobile, hb, is given as:
- By introducing the distance, d, (12) can be rewritten as:
- For urban model:
- For suburban model:
4. Simulation Results with Discussion
4.1. Effect of Non-Orthogonality Factor, ε
4.2. Effect of Load Concentration on the Inner Cell
4.3. Effect of Load Concentration on the Outer Cells
4.4. Comparative Results
4.5. Effect of the Height of the Mobile, hm and the Height of the Base Station, hb
4.6. Effect of the Cell Raduis, r on the Network Performance
4.7. Effect of the Maximu Distance, dmax on the Network Performance
5. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Symbol | Description |
---|---|
PLE | The path loss model for Environment E (1 for urban, 2 for suburban) |
f | The frequency in MHz |
hb | The height of the base station or transmitter in meters |
hm | The height of the mobile or receiver in meters |
d | The distance between transmitter and receiver in kilometers, km |
a(hm) | The correction factor for the mobile antenna. |
PS | The transmitted power |
PR | The received power at Node B. |
CFE | The correction factor for each environment E |
mmWave | Millimeter waves |
KE (hm, hb, f) | The function of hm, the height of the mobile and hb, the height of the base station, and the frequency, f. |
IoT | Internet-of-things |
NR 5G | 5G new radio |
3GPP | Third Generation Partnership Project |
MIMO | Multiple input multiple output |
gNBs | gNodeB |
AN | Access Network Layer |
AMF | Access and Mobility Management Function |
OAI | Open Air Interface |
AUSF | Authentication Server Function |
SMF | Session Management Function |
URLLC | Ultra-reliable low-latency communication |
UPF | User Plane Function |
TN layer | Transport network layer |
TAC | Tracking Area Code |
SLAs | Service Level Agreements |
5G SA | 5G standalone |
mMTC | Massive machine-type communication |
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Zreikat, A.I.; Mathew, S. Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks. Computers 2024, 13, 108. https://doi.org/10.3390/computers13040108
Zreikat AI, Mathew S. Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks. Computers. 2024; 13(4):108. https://doi.org/10.3390/computers13040108
Chicago/Turabian StyleZreikat, Aymen I., and Shinu Mathew. 2024. "Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks" Computers 13, no. 4: 108. https://doi.org/10.3390/computers13040108
APA StyleZreikat, A. I., & Mathew, S. (2024). Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks. Computers, 13(4), 108. https://doi.org/10.3390/computers13040108