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Dataset for the paper M. Polese, L. Bonati, S. D'Oro, S. Basagni, T. Melodia, "ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms," IEEE Transactions on Mobile Computing, pp. 1-14, July 2022.

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Colosseum O-RAN ColORAN Dataset

This repository contains the dataset for the paper M. Polese, L. Bonati, S. D'Oro, S. Basagni, T. Melodia, "ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms," IEEE Transactions on Mobile Computing, pp. 1-14, July 2022. Please cite the paper if you plan to use it in your publication.

This work was partially supported by the U.S. National Science Foundation under Grants CNS-1923789 and NSF CNS-1925601, and the U.S. Office of Naval Research under Grant N00014-20-1-2132.

Experiment setup

  • Number of Base Stations (BSs): 7
    • Nodes: 1, 8, 15, 22, 29, 36, 43
  • Channel bandwidth: 10 MHz (50 Physical Resource Blocks (PRBs))
  • Number of slices for each BS: 3
  • Scheduling policies available to each slice:
    • Policy 0: Round-robin (RR)
    • Policy 1: Waterfilling (WF)
    • Policy 2: Proportionally fair (PF)
  • Number of User Equipments (UEs): 42
  • Radio Frequency (RF) scenario setup (Colosseum Rome scenario):
    • Medium: UEs uniformly distributed within 50 m of each BS
  • UE Mobility: static
  • Traffic classes:
    • eMBB: Constant bitrate traffic (4 Mbps per UE)
    • MTC: Poisson traffic (30 pkt/s of 125 bytes per UE)
    • URLLC: Poisson traffic (10 pkt/s of 125 bytes per UE)
  • UEs belong to different traffic classes:
    • eMBB UEs (slice 0): 3, 6, 10, 13, 17, 20, 24, 27, 31, 34, 38, 41, 45, 48
    • MTC UEs (slice 1): 4, 7, 11, 14, 18, 21, 25, 28, 32, 35, 39, 42, 46, 49
    • URLLC UEs (slice 2): 2, 5, 9, 12, 16, 19, 23, 26, 30, 33, 37, 40, 44, 47
  • UEs are divided per slice based on traffic types:
    • Slice 0: eMBB UEs
    • Slice 1: MTC UEs
    • Slice 2: URLLC UEs

Training configurations

For each scheduling policy (directories sched0, sched1, sched2), the Resource Block Group (RBG) allocations for each slice are as follows.

Number of RBGs per Slice
Training Slice 0 Slice 1 Slice 2
tr0 2 13 2
tr1 4 11 2
tr2 6 9 2
tr3 8 7 2
tr4 10 5 2
tr5 12 3 2
tr6 14 1 2
tr7 2 11 4
tr8 4 9 4
tr9 6 7 4
tr10 8 5 4
tr11 10 3 4
tr12 12 1 4
tr13 2 9 6
tr14 4 7 6
tr15 6 5 6
tr16 8 3 6
tr17 10 1 6
tr18 2 7 8
tr19 4 5 8
tr20 6 3 8
tr21 8 1 8
tr22 2 5 10
tr23 4 3 10
tr24 6 1 10
tr25 2 3 12
tr26 4 1 12
tr27 2 1 14

About

Dataset for the paper M. Polese, L. Bonati, S. D'Oro, S. Basagni, T. Melodia, "ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms," IEEE Transactions on Mobile Computing, pp. 1-14, July 2022.

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