| Literature DB >> 35009569 |
Ramraj Dangi1, Praveen Lalwani1, Gaurav Choudhary2, Ilsun You3, Giovanni Pau4.
Abstract
In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article's main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.Entities:
Keywords: 5G; beamforming; machine learning; massive multiple input and multiple output (MIMO); millimeter wave (mmW); mobile edge computing (MEC); small cell
Mesh:
Year: 2021 PMID: 35009569 PMCID: PMC8747744 DOI: 10.3390/s22010026
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576
Summary of Mobile Technology.
| Generations | Access Techniques | Transmission Techniques | Error Correction Mechanism | Data Rate | Frequency Band | Bandwidth | Application | Description |
|---|---|---|---|---|---|---|---|---|
| 1G | FDMA, AMPS | Circuit Switching | NA | 2.4 kbps | 800 MHz | Analog | Voice | Let us talk to each other |
| 2G | GSM, TDMA, CDMA | Circuit Switching | NA | 10 kbps | 800 MHz, 900 MHz, 1800 MHz, 1900 MHz | 25 MHz | Voice and Data | Let us send messages and travel with improved data services |
| 3G | WCDMA, UMTS, CDMA 2000, HSUPA/HSDPA | Circuit and Packet Switching | Turbo Codes | 384 kbps to 5 Mbps | 800 MHz, 850 MHz, 900 MHz, 1800 MHz, 1900 MHz, 2100 MHz | 25 MHz | Voice, Data, and Video Calling | Let us experience surfing internet and unleashing mobile applications |
| 4G | LTEA, OFDMA, SCFDMA, WIMAX | Packet switching | Turbo Codes | 100 Mbps to 200 Mbps | 2.3 GHz, 2.5 GHz and 3.5 GHz initially | 100 MHz | Voice, Data, Video Calling, HD Television, and Online Gaming. | Let’s share voice and data over fast broadband internet based on unified networks architectures and IP protocols |
| 5G | BDMA, NOMA, FBMC | Packet Switching | LDPC | 10 Gbps to 50 Gbps | 1.8 GHz, 2.6 GHz and 30–300 GHz | 30–300 GHz | Voice, Data, Video Calling, Ultra HD video, Virtual Reality applications | Expanded the broadband wireless services beyond mobile internet with IOT and V2X. |
Table of Notations and Abbreviations.
| Abbreviation | Full Form | Abbreviation | Full Form |
|---|---|---|---|
| AMF | Access and Mobility Management Function | M2M | Machine-to-Machine |
| AT&T | American Telephone and Telegraph | mmWave | millimeter wave |
| BS | Base Station | NGMN | Next Generation Mobile Networks |
| CDMA | Code-Division Multiple Access | NOMA | Non-Orthogonal Multiple Access |
| CSI | Channel State Information | NFV | Network Functions Virtualization |
| D2D | Device to Device | OFDM | Orthogonal Frequency Division Multiplexing |
| EE | Energy Efficiency | OMA | Orthogonal Multiple Access |
| EMBB | Enhanced mobile broadband: | QoS | Quality of Service |
| ETSI | European Telecommunications Standards Institute | RNN | Recurrent Neural Network |
| eMTC | Massive Machine Type Communication | SDN | Software-Defined Networking |
| FDMA | Frequency Division Multiple Access | SC | Superposition Coding |
| FDD | Frequency Division Duplex | SIC | Successive Interference Cancellation |
| GSM | Global System for Mobile | TDMA | Time Division Multiple Access |
| HSPA | High Speed Packet Access | TDD | Time Division Duplex |
| IoT | Internet of Things | UE | User Equipment |
| IETF | Internet Engineering Task Force | URLLC | Ultra Reliable Low Latency Communication |
| LTE | Long-Term Evolution | UMTC | Universal Mobile Telecommunications System |
| ML | Machine Learning | V2V | Vehicle to Vehicle |
| MIMO | Multiple Input Multiple Output | V2X | Vehicle to Everything |
A comparative overview of existing surveys on different technologies of 5G networks.
| Authors& References | MIMO | NOMA | MmWave | 5G IOT | 5G ML | Small Cell | Beamforming | MEC | 5G Optimization |
|---|---|---|---|---|---|---|---|---|---|
| Chataut and Akl [ | Yes | - | Yes | - | - | - | Yes | - | - |
| Prasad et al. [ | Yes | - | Yes | - | - | - | - | - | - |
| Kiani and Nsari [ | - | Yes | - | - | - | - | - | Yes | - |
| Timotheou and Krikidis [ | - | Yes | - | - | - | - | - | - | Yes |
| Yong Niu et al. [ | - | - | Yes | - | - | Yes | - | - | - |
| Qiao et al. [ | - | - | Yes | - | - | - | - | - | Yes |
| Ramesh et al. [ | Yes | - | Yes | - | - | - | - | - | - |
| Khurpade et al. [ | Yes | Yes | - | Yes | - | - | - | - | - |
| Bega et al. [ | - | - | - | - | Yes | - | - | - | Yes |
| Abrol and jha [ | - | - | - | - | - | Yes | - | - | Yes |
| Wei et al. [ | - | Yes | - | - | - | - | - | - | |
| Jakob Hoydis et al. [ | - | - | - | - | - | Yes | - | - | - |
| Papadopoulos et al. [ | Yes | - | - | - | - | - | Yes | - | - |
| Shweta Rajoria et al. [ | Yes | - | Yes | - | - | Yes | Yes | - | - |
| Demosthenes Vouyioukas [ | Yes | - | - | - | - | - | Yes | - | - |
| Al-Imari et al. [ | - | Yes | Yes | - | - | - | - | - | - |
| Michael Till Beck et al. [ | - | - | - | - | - | - | Yes | - | |
| Shuo Wang et al. [ | - | - | - | - | - | - | Yes | - | |
| Gupta and Jha [ | Yes | - | - | - | - | Yes | - | Yes | - |
| Our Survey | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Figure 1Systematic layout representation of survey.
Research groups working on 5G mobile networks.
| Research Groups | Research Area | Description |
|---|---|---|
| METIS (Mobile and wireless communications Enablers for Twenty-twenty (2020) Information Society) | Working 5G Framework | METIS focused on RAN architecture and designed an air interface which evaluates data rates on peak hours, traffic load per region, traffic volume per user and actual client data rates. They have generate METIS published an article on February, 2015 in which they developed RAN architecture with simulation results. They design an air interface which evaluates data rates on peak hours, traffic load per region, traffic volume per user and actual client data rates.They have generate very less RAN latency under 1ms. They also introduced diverse RAN model and traffic flow in different situation like malls, offices, colleges and stadiums. |
| 5G PPP (5G Infrastructure Public Private Partnership) | Next generation mobile network communication, high speed Connectivity. | Fifth generation infrastructure public partnership project is a joint startup by two groups (European Commission and European ICT industry). 5G-PPP will provide various standards architectures, solutions and technologies for next generation mobile network in coming decade. The main motto behind 5G-PPP is that, through this project, European Commission wants to give their contribution in smart cities, e-health, intelligent transport, education, entertainment, and media. |
| 5GNOW (5th Generation Non-Orthogonal Waveforms for asynchronous signaling) | Non-orthogonal Multiple Access | 5GNOW’s is working on modulation and multiplexing techniques for next generation network. 5GNOW’s offers ultra-high reliability and ultra-low latency communication with visible waveform for 5G. 5GNOW’s also worked on acquiring time and frequency plane information of a signal using short term Fourier transform (STFT) |
| EMPhAtiC (Enhanced Multicarrier Technology for Professional Ad-Hoc and Cell-Based Communications) | MIMO Transmission | EMPhAtiC is working on MIMO transmission to develop a secure communication techniques with asynchronicity based on flexible filter bank and multihop. Recently they also launched MIMO based trans-receiver technique under frequency selective channels for Filter Bank Multi-Carrier (FBMC) |
| NEWCOM (Network of Excellence in Wireless Communications) | Advanced aspects of wireless communications | NEWCOM is working on energy efficiency, channel efficiency, multihop communication in wireless communication. Recently, they are working on cloud RAN, mobile broadband, local and distributed antenna techniques and multi-hop communication for 5G network. Finally, in their final research they give on result that QAM modulation schema, system bandwidth and resource block is used to process the base band. |
| NYU New York University Wireless | Millimeter Wave | NYU Wireless is research center working on wireless communication, sensors, networking and devices. In their recent research, NYU focuses on developing smaller and lighter antennas with directional beamforming to provide reliable wireless communication. |
| 5GIC 5G Innovation Centre | Decreasing network costs, Preallocation of resources according to user’s need, point-to-point communication, Highspeed connectivity. | 5GIC, is a UK’s research group, which is working on high-speed wireless communication. In their recent research they got 1Tbps speed in point-to-point wireless communication. Their main focus is on developing ultra-low latency app services. |
| ETRI (Electronics and Telecommunication Research Institute) | Device-to-device communication, MHN protocol stack | ETRI (Electronics and Telecommunication Research Institute), is a research group of Korea, which is focusing on improving the reliability of 5G network, device-to-device communication and MHN protocol stack. |
Figure 2Pictorial representation of multi-input and multi-output (MIMO).
Summary of massive MIMO-based approaches in 5G technology.
| Approach | Throughput | Latency | Energy Efficiency | Spectral Efficiency |
|---|---|---|---|---|
| Panzner et al. [ | Good | Low | Good | Average |
| He et al. [ | Average | Low | Average | - |
| Prasad et al. [ | Good | - | Good | Avearge |
| Papadopoulos et al. [ | Good | Low | Average | Avearge |
| Ramesh et al. [ | Good | Average | Good | Good |
| Zhou et al. [ | Average | - | Good | Average |
Figure 3Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).
Summary of NOMA-based approaches in 5G technology.
| Approach | Spectral Efficiency | Fairness | Computing Capacity |
|---|---|---|---|
| Al-Imari et al. [ | Good | Good | Average |
| Islam et al. [ | Good | Average | Average |
| Kiani and Nsari [ | Average | Good | Good |
| Timotheou and Krikidis [ | Good | Good | Average |
| Wei et al. [ | Good | Average | Good |
Figure 4Pictorial representation of millimeter wave.
Summary of existing mmWave-based approaches in 5G technology.
| Approach | Transmission Rate | Coverage | Cost |
|---|---|---|---|
| Hong et al. [ | Average | Average | Low |
| Qiao et al. [ | Average | Good | Average |
| Wei et al. [ | Good | Average | Low |
Figure 5Pictorial representation of IoT with 5G.
Summary of IoT-based approaches in 5G technology.
| Approach | Data Rate | Security Requirement | Performance |
|---|---|---|---|
| Akpakwu et al. [ | Good | Average | Good |
| Khurpade et al. [ | Average | - | Average |
| Ni et al. [ | Good | Average | Average |
The state-of-the-art ML-based solution for 5G network.
| Author References | Key Contribution | ML Applied | Network Participants Component | 5G Network Application Parameter | |||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|
|
|
| |||
| Alave et al. [ | Network traffic prediction | LSTM and DNN | ✓ | ✓ | * | ✓ | ✓ | ✓ | X |
| Bega et al. [ | Network slice admission control algorithm | Machine Learning and Deep Learing | ✓ | X | X | ✓ | ✓ | ✓ | X |
| Suomalainen et al. [ | 5G Security | Machine Learning | X | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Bashir et al. [ | Resource Allocation | Machine Learning | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | X |
| Balevi et al. [ | Low Latency communication | Unsupervised clustering | X | ✓ | X | ✓ | ✓ | ✓ | X |
| Tayyaba et al. [ | Resource Management | LSTM, CNN, and DNN | ✓ | ✓ | X | ✓ | ✓ | ✓ | ✓ |
| Sim et al. [ | 5G mmWave Vehicular communication | FML (Fast machine Learning) | X | ✓ | * | ✓ | ✓ | ✓ | X |
| Li et al. [ | Intrusion Detection System | Machine Learning | X | ✓ | X | ✓ | ✓ | ✓ | ✓ |
| Kafle et al. [ | 5G Network Slicing | Machine Learning | X | ✓ | X | ✓ | ✓ | ✓ | ✓ |
| Chen et al. [ | Physical-Layer Channel Authentication | Machine Learning | X | ✓ | X | X | X | X | ✓ |
| Sevgican et al. [ | Intelligent Network Data Analytics Function in 5G | Machine Learning | ✓ | X | ✓ | X | X | * | * |
| Abidi et al. [ | Optimal 5G network slicing | Machine Learning and Deep Learing | X | ✓ | X | ✓ | ✓ | ✓ | * |
Figure 6Pictorial representation of machine learning (ML) in 5G.
The state-of-the-art ML-based approaches in 5G technology.
| Approach | Energy Efficiency | Quality of Services (QoS) | Latency |
|---|---|---|---|
| Fang et al. [ | Good | Good | Average |
| Alawe et al. [ | Good | Average | Low |
| Bega et al. [ | - | Good | Average |
Summary of Optimization Based Approaches in 5G Technology.
| Approach | Energy Efficiency | Power Optimization | Latency |
|---|---|---|---|
| Zi et al. [ | Good | - | Average |
| Abrol and jha [ | Good | Good | - |
| Pérez-Romero et al. [ | - | Average | Average |
| Lähetkangas et al. [ | Average | - | Low |
Types of Small cells.
| Types of Small Cell | Coverage Radius | Indoor Outdoor | Transmit Power | Number of Users | Backhaul Type | Cost |
|---|---|---|---|---|---|---|
| Femtocells | 30–165 ft | Indoor | 100 mW | 8–16 | Wired, fiber | Low |
| Picocells | 330–820 ft | Indoor | 250 mW | 32–64 | Wired, fiber | Low |
| Microcells | 1600–8000 ft | Outdoor | 2000–500 mW | 200 | Wired, fiber, Microwave | Medium |
Figure 7Pictorial representation of communication with and without small cells.
Figure 8Pictorial Representation of communication with and without using beamforming.
Figure 9Pictorial representation of cloud computing vs. mobile edge computing.
Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).
| Approach | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Panzner et al. [ | Good | Low | Good | - | Avg | - | - | - | - | - | - | - | - | - |
| Qiao et al. [ | - | - | - | - | - | - | - | Avg | Good | Avg | - | - | - | - |
| He et al. [ | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
| Abrol and jha [ | - | - | Good | - | - | - | - | - | - | - | - | - | - | Good |
| Al-Imari et al. [ | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
| Papadopoulos et al. [ | Good | Low | Avg | - | Avg | - | - | - | - | - | - | - | - | - |
| Kiani and Nsari [ | - | - | - | - | Avg | Good | Good | - | - | - | - | - | - | - |
| Beck [ | - | Low | - | - | - | - | - | Avg | - | - | - | Good | - | Avg |
| Ni et al. [ | - | - | - | Good | - | - | - | - | - | - | Avg | Avg | - | - |
| Elijah [ | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
| Alawe et al. [ | - | Low | Good | - | - | - | - | - | - | - | - | - | Avg | - |
| Zhou et al. [ | Avg | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
| Islam et al. [ | - | - | - | - | Good | Avg | Avg | - | - | - | - | - | - | - |
| Bega et al. [ | - | Avg | - | - | - | - | - | - | - | - | - | - | Good | - |
| Akpakwu et al. [ | - | - | - | Good | - | - | - | - | - | - | Avg | Good | - | - |
| Wei et al. [ | - | - | - | - | - | - | - | Good | Avg | Low | - | - | - | - |
| Khurpade et al. [ | - | - | - | Avg | - | - | - | - | - | - | - | Avg | - | - |
| Timotheou and Krikidis [ | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
| Wang [ | Avg | Low | Avg | Avg | - | - | - | - | - | - | - | - | - | - |
| Akhil Gupta & R. K. Jha [ | - | - | Good | Avg | Good | - | - | - | - | - | - | Good | Good | - |
| Pérez-Romero et al. [ | - | - | Avg | - | - | - | - | - | - | - | - | - | - | Avg |
| Pi [ | - | - | - | - | - | - | - | Good | Good | Avg | - | - | - | - |
| Zi et al. [ | - | Avg | Good | - | - | - | - | - | - | - | - | - | - | - |
| Chin [ | - | - | Good | Avg | - | - | - | - | - | Avg | - | Good | - | - |
| Mamta Agiwal [ | - | Avg | - | Good | - | - | - | - | - | - | Good | Avg | - | - |
| Ramesh et al. [ | Good | Avg | Good | - | Good | - | - | - | - | - | - | - | - | - |
| Niu [ | - | - | - | - | - | - | - | Good | Avg | Avg | - | - | - | |
| Fang et al. [ | - | Avg | Good | - | - | - | - | - | - | - | - | - | Good | - |
| Hoydis [ | - | - | Good | - | Good | - | - | - | - | Avg | - | Good | - | - |
| Wei et al. [ | - | - | - | - | Good | Avg | Good | - | - | - | - | - | - | - |
| Hong et al. [ | - | - | - | - | - | - | - | - | Avg | Avg | Low | - | - | - |
| Rashid [ | - | - | - | Good | - | - | - | Good | - | - | - | Avg | - | Good |
| Prasad et al. [ | Good | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
| Lähetkangas et al. [ | - | Low | Av | - | - | - | - | - | - | - | - | - | - | - |