Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
2. Background, Related Work, and Contributions
2.1. Channel Impulse Response, Power Delay Profile, and Propagation Parameters
2.2. Beamforming Strategies in 5G New Radio
2.3. Relevant Experimental Studies
2.4. Contributions and Innovation
- An extensive set of measurements collected in an indoor environment in the city of Rome, Italy is analyzed, covering two 5G networks under deployment by two network operators. A passive channel measurement approach was used. The standardized downlink reference signals of a commercial 5G network were collected and used for extracting the channel impulse responses. The channel propagation characterization was performed based on the actual configurations applied in a commercial 5G deployed network, including the carrier frequency, bandwidth, antenna, beam-based transmission, etc.
- Channel propagation characterization is presented for two different deployed networks, i.e., single Wide-beam and multiple SSB based 5G networks. Multipath components were extracted from measurements and used to carry out a comprehensive study of time and power-related channel parameters for different measurements locations and operators. The number of paths, interarrival times between paths, RMS delay spread, and mean excess delay are evaluated on the collected channel measurements in order to characterize time-related channel aspects. In contrast, MPC power attenuation, path loss, and Rician K-factor are studied in order to characterize power-related aspects.
- The channel parameters’ dependencies are also studied. A comprehensive correlation analysis between the aforementioned channel parameters is carried out in order to highlight and quantify the correlation between heterogeneous propagation parameters.
3. Experimental Setup and Dataset
3.1. Measurement System and Methodology
3.2. Measurement Campaigns
3.3. Collected Dataset
4. Statistical Analysis of Channel Propagation Characteristics
4.1. Time-Related Characteristics
4.1.1. Number of Paths and Interarrival Times
4.1.2. Mean Excess Delay
4.1.3. RMS Delay Spread
4.2. Power-Related Characteristics
4.2.1. Power Decay
4.2.3. Rician K-Factor
4.3. Correlation Analysis
- The 5G system operating with SSB-based transmission, the echoes arrived at the receiver with smaller mean excess delays but were relatively weaker. While in the 5G system operating with the wide beam-based transmission, echoes arrived at the receiver with large excess delays but were relatively much stronger.
- The RMS delay spread is lower for the 5G system, using a single wide beam, than the multiple SSBs-based transmission systems.
- The system with multiple SSB-based transmission, the measured PL is on average about 20 dB higher than the PL predicted by the 3GPP model. While in case of single wide-beam-based transmission, the PL estimation by 3GPP model is more accurate. A possible explanation for this difference is that the 3GPP model does not account for SSB-based transmission, leading to a PL estimate for single wide-beam-based transmission more accurate than for the SSB-based transmission.
- The 5G system employing a single wide beam gives a higher K-factor than the multiple SSBs-based transmission systems.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Characterized Channel Propagation Parameters|
|Location||DIET department building, second floor, Sapienza University of Rome, Rome, Italy|
|Thickness of the window glass||Total 10 mm|
|Height of buildings||About 50 m|
|Surrounding||Typical office building, window-to-wall ratio is about 2:1.|
|Wall thickness||Internal walls ≈ 15 cm|
Perimeter walls ≈ 30 cm
|Inside arrangements||Each office contains chair, tables, shelves attached with walls.|
|No. of |
|No. of PDPs|
|No. of PDPs|
|RMS Delay Spread for Op1||RMS Delay Spread for Op2|
|Reference||RMS Delay Spread|
|Locations||K-Factor for Op1||K-Factor for Op2|
|No. of |
|Mean Excess |
|No. of paths||1||−0.15||0.55||0.46||0.36||−0.28|
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Ali, U.; Caso, G.; De Nardis, L.; Kousias, K.; Rajiullah, M.; Alay, Ö.; Neri, M.; Brunstrom, A.; Di Benedetto, M.-G. Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks. Future Internet 2022, 14, 239. https://doi.org/10.3390/fi14080239
Ali U, Caso G, De Nardis L, Kousias K, Rajiullah M, Alay Ö, Neri M, Brunstrom A, Di Benedetto M-G. Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks. Future Internet. 2022; 14(8):239. https://doi.org/10.3390/fi14080239Chicago/Turabian Style
Ali, Usman, Giuseppe Caso, Luca De Nardis, Konstantinos Kousias, Mohammad Rajiullah, Özgü Alay, Marco Neri, Anna Brunstrom, and Maria-Gabriella Di Benedetto. 2022. "Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks" Future Internet 14, no. 8: 239. https://doi.org/10.3390/fi14080239