Positioning in 5G and 6G Networks—A Survey
Abstract
:1. Introduction
- Section 2 is an overview of the characteristics of cellular-network-based positioning—the different types of cellular positioning are summarized here.
- Section 3 describes the capabilities of conventional positioning techniques, and several non-ML positioning solutions are introduced and compared here.
- An extensive study of ML-aided positioning techniques is provided in Section 4, and the different methods are compared to each other based on positioning accuracy.
- Section 5 summarizes the expected advancements of 6G networks in terms of positioning.
- Major results of real-world use cases that have been published within the scientific communities so far are collected in Section 6.
2. Positioning in Cellular Networks
2.1. Cell-Identity-Based Localization Techniques
2.2. Angle-Based Localization Techniques
2.3. Range-Based Localization Techniques
2.4. Fingerprinting-Based Localization Techniques
3. Conventional Positioning Solutions in 5G Networks
3.1. Pure 5G Network-Based Positioning
3.2. Assisted Positioning in 5G Networks
3.3. Lessons Learned
4. Machine-Learning-Aided Positioning in 5G Networks
4.1. Advances in Positioning Aided by Machine Learning
4.2. Lessons Learned
5. Beyond 5G
6. Use-Case Examples
- 1
- 2
- 3
- 4
- Emergency and mission critical use cases are related to emergency services, first responders, alerting nearby responders, emergency vehicle and equipment location [14].
- 5
- Road-related use cases include traffic monitoring, management and control, V2X, car and bike sharing, as well as flow control in transportation hubs and public transportation [99].
- 6
6.1. Positioning in Industrial Settings and Cyber–Physical Systems
6.2. Specialties of V2X Positioning
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Refs. | Algorithm | Input Data Type | Simulation? | Environment | Error |
---|---|---|---|---|---|
[33] | Robust Weighted Least Squares + RANSAC and IDD combined | TDoA | realistic | indoor | <3 m |
[34] | Dynamic reconstruction fingerprint matching algorithm | Received signal strength indicator | simulated | indoor | <1 m |
[35] | Extended Kalman filter | AoA | realistic | outdoor | <1 m |
[36,37] | Extended Kalman filter | Uplink reference signal | simulated | outdoor | <1 m |
[39] | Expectation maximization, subspace-spaced algorithm | Uplink reference signal | simulated | indoor | <1 m |
[40] | Unscented Kalman filter | AoA, ToA | simulated | indoor | <1 m |
[41] | Deriving Cramer–Rao bound | AoA, TDoA | realistic | outdoor (vehicle) | <1 m |
[43] | Taylor series least-square method | GNSS-TOA, 5G-AoA | simulated | outdoor | <10 m (95%) |
[44] | Deriving Ficher information of 5G and GNSS signals | Simulated GNSS, simulated 5G signals | simulated | outdoor | <1 m |
[45] | Particle filter | Real GNSS, simulated 5G signals | simulated | outdoor | <3 m (RMSE) |
[46] | OFDMA-based VLCP | Light signals, RSS | simulated | indoor | <1 m |
Refs. | ML Method | Measurement Type | Simulation/ Realistic | Environment | Error |
---|---|---|---|---|---|
[49] | NN, RF | BRSRP | realistic | outdoor | <10 m (80%) |
[53] | kNN, ELM | CSI | realistic | outdoor | 8.2m |
[54] | NN, TDNN (time-delay neural network) | TOA, code phase estimate | realistic | outdoor | 4.9 m (ranging RMSE) |
[55] | NN | AoA | hybrid | both | 0.4 m |
[56] | Densely connected Neural Network | RSS, GNSS signal | simulation | outdoor | 0.74 m |
[57] | NN, DT | BRSRP | simulation | outdoor | 1.4 m |
[50] | CNN, LSTM, TCN | Beamformed fingerprint | simulation | outdoor | 1.78 m |
[58] | weighted kNN | CSI | simulation | outdoor | 2 m (90%) |
[59] | Deep convolutional Gaussian process | Beamforming images | simulation | outdoor | 2.79 m |
[27] | 13 ML models including NN, kNN, RF | RSRP | simulation | outdoor | 3.3 m (kNN) |
[60,61] | GPR, kNN, SVM | RSRP | simulation | outdoor | 3.5 m |
[62] | Gaussian Processes | RSRP | simulation | outdoor | 10 m |
[63] | NN, kNN, SVM | RSRP | simulation | indoor | 1.6 m |
[64] | DNN | RSS | simulation | indoor | 1.6 m |
[65] | Gaussian Processes | RSRP | simulation | indoor | <2 m |
[66] | kNN | RSS | realistic | indoor | <2 m |
[67] | NN | CSI | simulation | both | <1 m |
Major Factors | 6G | 5G |
---|---|---|
Peak data rate | >100 Gb/s | 10[20] Gb/s |
User experience data rate | >10 Gb/s | 1 Gb/s |
Traffic density | >100 Tb/s/km2 | 10 Tb/s/km2 |
Connection density | >10 million/km2 | 1 million/km2 |
Delay | <1 ms | ms level |
Mobility | >1000 km/h | 350 km/h |
Spectrum efficiency | >3x relative to 5G | 3–5x relative to 4G |
Energy efficiency | >10x relative to 5G | 1000x relative to 4G |
Coverage percent | >99% | ∼70% |
Reliability | >99.999% | ∼99.9% |
Positioning precision | Centimeter level | Meter level |
Receiver sensitivity | <–130 dBm | About –120 dBm |
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Mogyorósi, F.; Revisnyei, P.; Pašić, A.; Papp, Z.; Törös, I.; Varga, P.; Pašić, A. Positioning in 5G and 6G Networks—A Survey. Sensors 2022, 22, 4757. https://doi.org/10.3390/s22134757
Mogyorósi F, Revisnyei P, Pašić A, Papp Z, Törös I, Varga P, Pašić A. Positioning in 5G and 6G Networks—A Survey. Sensors. 2022; 22(13):4757. https://doi.org/10.3390/s22134757
Chicago/Turabian StyleMogyorósi, Ferenc, Péter Revisnyei, Azra Pašić, Zsófia Papp, István Törös, Pál Varga, and Alija Pašić. 2022. "Positioning in 5G and 6G Networks—A Survey" Sensors 22, no. 13: 4757. https://doi.org/10.3390/s22134757
APA StyleMogyorósi, F., Revisnyei, P., Pašić, A., Papp, Z., Törös, I., Varga, P., & Pašić, A. (2022). Positioning in 5G and 6G Networks—A Survey. Sensors, 22(13), 4757. https://doi.org/10.3390/s22134757