The emerging 5G services offer numerous new opportunities for networked applications. In this study, we seek to answer two key questions: i) is the throughput of mmWave 5G predictable, and ii) can we build "good" machine learning models for 5G throughput prediction? To this end, we conduct a measurement study of commercial mmWave 5G services in a major U.S. city, focusing on the throughput as perceived by applications running on user equipment (UE).

Instructions: 

DATASET WEBSITE: https://lumos5g.umn.edu/

## OVERVIEW

Lumos5G 1.0 is a dataset that represents the `Loop` area of the IMC'20 paper - "Lumos5G: Mapping and Predicting Commercial mmWave 5G Throughput". The Loop area is a 1300 meter loop near U.S. Bank Stadium in Minneapolis downtown area that covers roads, railroad crossings, restaurants, coffee shops, and recreational outdoor parks.

This dataset is being made available to the research community.

## DATASET COLUMNS AND DESCRIPTION

The description of the columns in the dataset CSV, from left to right, are:

- `run_num`: Indicates the run number. For each trajectory and mobility mode, we conduct several runs of experiments.
- `seq_num`: This is the sequence number. For each run, the sequence number acts like an index or a per-second timeline.
- `abstractSignalStr`: Indicates the abstract signal strength as reported by Android API (https://developer.android.com/reference/android/telephony/SignalStrength...()). No matter whether the UE was connected to 5G service or not, this column always reported a value associated with the LTE/4G radio. Note, if one is interested to understand the signal strength values related to 5G-NR, we refer them to other columns such as `nr_ssRsrp`, `nr_ssRsrq`, and `nr_ssSinr`.
- `latitude`: The latitude in degrees as reported by Android's API (https://developer.android.com/reference/android/location/Location#getLat...()).
- `longitude`: The longitude in degrees as reported by Android's API (https://developer.android.com/reference/android/location/Location#getLon...()).
- `movingSpeed`: The ground mobility/moving speed of the UE as reported by Android's API (https://developer.android.com/reference/android/location/Location#getSpeed()). The unit is meters per second.
- `compassDirection`: The bearing in degrees as reported by Android's API (https://developer.android.com/reference/android/location/Location#getBea...()). Bearing is the horizontal direction of travel of this device, and is not related to the device orientation. It is guaranteed to be in the range `(0.0, 360.0]` if the device has a bearing.
- `nrStatus`: Indicates if the UE was connected to 5G network or not. When `nrStatus=CONNECTED`, the UE was connected to 5G. All other values of `nrStatus` such as `NOT_RESTRICTED` and `NONE` indicate the UE was not connected to 5G. `nrStatus` was obtained by parsing the raw string representation of `ServiceState` object (https://developer.android.com/reference/android/telephony/ServiceState#t...()).
- `lte_rssi`: Get Received Signal Strength Indication (RSSI) in dBm of the primary serving LTE cell. The value range is [-113, -51] inclusively or CellInfo#UNAVAILABLE if unavailable. Reference: TS 27.007 8.5 Signal quality +CSQ.
- `lte_rsrp`: Get reference signal received power (RSRP) in dBm of the primary serving LTE cell.
- `lte_rsrq`: Get reference signal received quality (RSRQ) of the primary serving LTE cell.
- `lte_rssnr`: Get reference signal signal-to-noise ratio (RSSNR) of the primary serving LTE cell.
- `nr_ssRsrp`: Obtained by parsing the raw string representation of `SignalStrength` object (https://developer.android.com/reference/android/telephony/SignalStrength...()). `nr_ssRsrp` was a field in this object's `CellSignalStrengthNr` section. In general, this value was only available when the UE was connected to 5G (i.e., when `nrStatus=CONNECTED`). Reference: 3GPP TS 38.215. Range: -140 dBm to -44 dBm.
- `nr_ssRsrq`: Obtained by parsing the raw string representation of `SignalStrength` object (https://developer.android.com/reference/android/telephony/SignalStrength...()). `nr_ssRsrq` was a field in this object's `CellSignalStrengthNr` section. In general, this value was only available when the UE was connected to 5G (i.e., when `nrStatus=CONNECTED`). Reference: 3GPP TS 38.215. Range: -20 dB to -3 dB.
- `nr_ssSinr`: Obtained by parsing the raw string representation of `SignalStrength` object (https://developer.android.com/reference/android/telephony/SignalStrength...()). `nr_ssSinr` was a field in this object's `CellSignalStrengthNr` section. In general, this value was only available when the UE was connected to 5G (i.e., when `nrStatus=CONNECTED`). Reference: 3GPP TS 38.215 Sec 5.1.*, 3GPP TS 38.133 10.1.16.1 Range: -23 dB to 40 dB
- `Throughput`: Indicates the throughput perceived by the UE. iPerf 3.7 was used to measure the per-second TCP downlink at the UE.
- `mobility_mode`: Indicates the grouth truth about the mobility mode when the experiment was conducted. This value can either be walking or driving.
- `trajectory_direction`: Indicates the ground truth about the trajectory direction of the experiment conducted at the Loop area. `CW` indicates clockwise direction, while `ACW` indicates anti-clockwise. Note, the driving experiments were only conducted in `CW` direction as certain parts of the loop were one way only. Walking-based experiments were conducted in both directions.
- `tower_id`: Indicates the (anonymized) tower identifier.

Note: We found that availability (and at times even the values) of `lte_rssi`, `nr_ssRsrp`, `nr_ssRsrq` and `nr_ssSinr` were not reliable. Since these values were sampled every second, at certain times (e.g., boundary cases), we might still find NR-related values when `nrStatus` is not equal to `CONNECTED`. However, in this dataset, we still include all the raw values as reported by the APIs.

## CITING THE DATASET

```
@inproceedings{10.1145/3419394.3423629,
author = {Narayanan, Arvind and Ramadan, Eman and Mehta, Rishabh and Hu, Xinyue and Liu, Qingxu and Fezeu, Rostand A. K. and Dayalan, Udhaya Kumar and Verma, Saurabh and Ji, Peiqi and Li, Tao and Qian, Feng and Zhang, Zhi-Li},
title = {Lumos5G: Mapping and Predicting Commercial MmWave 5G Throughput},
year = {2020},
isbn = {9781450381383},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3419394.3423629},
doi = {10.1145/3419394.3423629},
booktitle = {Proceedings of the ACM Internet Measurement Conference},
pages = {176–193},
numpages = {18},
keywords = {bandwidth estimation, mmWave, machine learning, Lumos5G, throughput prediction, deep learning, prediction, 5G},
location = {Virtual Event, USA},
series = {IMC '20}
}
```

## QUESTIONS?

Please feel free to contact the FiveGophers/Lumos5G team for questions or information about the data (arvind@cs.umn.edu,eman@cs.umn.edu,zhzhang@cs.umn.edu,fengqian@umn.edu,fivegophers@umn.edu)

## LICENSE

Lumos5G 1.0 dataset is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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22 Views

We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band 5G carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance, and explore the feasibility of using location and possibly other environmental information to predict the network performance.

Instructions: 

DATASET WEBSITE: https://fivegophers.umn.edu/www20/

## OVERVIEW

5Gophers 1.0 is a dataset collected when the world's very first commercial 5G services were made available to consumers. It should serve as a baseline to evaluate the 5G's performance evolution over time. Results using this dataset is presented in our measurement paper - "A First Look at Commercial 5G Performance on Smartphones".

This dataset is being made available to the research community.

## FILES and FOLDER STRUCTURE

All the files are in CSV format with headers that should hopefully be self-explainatory.

5Gophers-v1.0
├── All-Carriers
│   ├── 01-Throughput
│   ├── 02-Round-Trip-Times
│   └── 03-User-Mobility
└── mmWave-only
├── 03-UE-Panel (LoS Tests)
├── 04-Ping-Traces (Latency Tests)
├── 05-UE-Panel (NLoS Tests)
├── 06-UE-Panel (Orientation Tests)
├── 07-UE-Panel (Distance Tests)
├── 08-Web-Page-Load-Tests
├── 09-HTTPS-CDN-vs-NonCDN (Download Test)
└── 10-HTTP-vs-HTTPS (Download Test)

## CITING THE DATASET

```
@inproceedings{10.1145/3366423.3380169,
author = {Narayanan, Arvind and Ramadan, Eman and Carpenter, Jason and Liu, Qingxu and Liu, Yu and Qian, Feng and Zhang, Zhi-Li},
title = {A First Look at Commercial 5G Performance on Smartphones},
year = {2020},
isbn = {9781450370233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3366423.3380169},
doi = {10.1145/3366423.3380169},
booktitle = {Proceedings of The Web Conference 2020},
pages = {894–905},
numpages = {12},
location = {Taipei, Taiwan},
series = {WWW ’20}
}
```

## QUESTIONS?

Please feel free to contact the FiveGophers team for information about the data (fivegophers@umn.edu, naray111@umn.edu)

## LICENSE

5Gophers 1.0 dataset is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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15 Views

This is DMRS data sets. 

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100 Views

This dataset contains the database of the transport block (TB) configurations .

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64 Views

The following data set is modelled after the implementers’ test data in 3GPP TS 33.501 “Security architecture and procedures for 5G System” with the same terminology. The data set corresponds to SUCI (Subscription Concealed Identifier) computation in the 5G UE (User Equipment) for IMSI (International Mobile Subscriber Identity) based SUPI (Subscription Permanent Identifier) and ECIES Profile A.

Instructions: 

The following data set is modelled after the implementers’ test data in 3GPP TS 33.501 “Security architecture and procedures for 5G System” with the same terminology. The data set corresponds to SUCI (Subscription Concealed Identifier) computation in the 5G UE (User Equipment) for IMSI (International Mobile Subscriber Identity) based SUPI (Subscription Permanent Identifier) and ECIES Profile A, the IMSI consists of MCC|MNC: '274012'. 

In the 5G system, the globally unique 5G subscription permanent identifier is called SUPI as defined in 3GPP TS 23.501. For privacy reasons, the SUPI from the 5G devices should not be transferred in clear text, and is instead concealed inside the privacy preserving SUCI. Consequently, the SUPI is privacy protected over-the-air of the 5G radio network by using the SUCI. For SUCIs containing IMSI based SUPI, the UE in essence conceals the MSIN (Mobile Subscriber Identification Number) part of the IMSI. On the 5G operator-side, the SIDF (Subscription Identifier De-concealing Function) of the UDM (Unified Data Management) is responsible for de-concealment of the SUCI and resolves the SUPI from the SUCI based on the protection scheme used to generate the SUCI. 

The SUCI protection scheme used in this data set is ECIES Profile A. The size of the scheme-output is a total of 256-bit public key, 64-bit MAC & 40-bit encrypted MSIN. The SUCI scheme-input MSIN is coded as hexadecimal digits using packed BCD coding where the order of digits within an octet is same as the order of MSIN. As the MSINs are odd number of digits, bits 5 to 8 of final octet is coded as ‘1111’.  

# Example Python code to load data into Spark DataFrame

df = spark.read.format("csv").option("inferSchema","true").option("header","true").option("sep",",").load(“5g_suci_using_ecies_profile_a_100k.gz”)

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2373 Views

The demo data set consists the propagation path distances of AT & T North America Netowork Topology. The geographical node positions (latitude and longitude) along with the adjacency matrix has been found out from International Topology Zoo and the data set has been formed using the available data. This set has been used in Joint localization prolem of Controller and Hypervisor instances in vSDN enebled 5G Network. 

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69 Views

This dataset accompanies a paper that discusses the advantages of a 3GPP-compliant service-based architecture platform that demonstrates the concept of cloud-native service orchestration and routing for a media vertical sector application. Cloud-native service orchestration and routing is a complete end-to-end approach that enables virtualisation and management of multiple layers in the OSI model, which provides considerable flexibility and control to achieve delivery of QoS to users in the face of varying demand, at reasonable cost.

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182 Views

This data set is for the paper titled:

 

Channel Coding in 5G New Radio

Tutorial Overivew and Performance Comparison with 4G LTE

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700 Views