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Network Slicing with NetSim

  • Overview: Network slicing enables the creation of multiple virtual networks, or slices, on a shared physical infrastructure. Operators need to efficiently allocate RAN resources to meet SLAs across different service categories within each slice.
    • SLA Assurance: SLAs must be met despite challenges such as user mobility, varying channel quality, and network load fluctuations.
    • Resource Allocation Complexity: Ensuring QoS through dynamic resource reservation is a hard problem. RAN is the bottleneck due to its reliance on the most expensive resource - spectrum.
  • NetSim Feature Set:
    • RAN Slicing: Define multiples slices at the network level.
    • Configuration: Set up S-NSSAI, which includes SST (Slice/Service Type) and SD (Slice Differentiator). Associate UEs with slices.
    • Resource Sharing Options:
      • Static Allocation: Allocate a fixed percentage of resources.
      • Dynamic Allocation: Allocates resources in run time to "statistically" meet SLAs using a proprietary online machine learning algorithm.
    • Telemetry: gNB and UE level radio measurements, Per UE and per gNB MAC throughput, Per gNB PRB utilization, Per slice PRB utilization and more. Plots as well as deep dive CSV logs.
  • Benefits of NetSim Simulation:
    • Users can simulate scenarios with network slices and check if SLAs can be met.
    • What-if analysis can be carried out to evaluate different network configurations and their impact on performance.
    • NetSim enables users to test and optimize their slicing algorithms in a controlled environment before real-world deployment.
  • Customization: NetSim includes source C code which users can modify to develop and test their own slicing algorithms.
The overall railway communication network is composed of four rings, which was simulated and analyzed.Traffic models were created for:
RAN Network Slicing in NetSim: Slices can be configured through the GUI. The RAN Intelligent Controller (RIC) with AI/ML capabilities manages the allocation of radio resources (PRBs) across slices, interacting with PHY, MAC, and RLC layers. Source code is provided for users to implement their slicing algorithms in the RIC.

Example Use Case

a. Baseline scenario with 3 UEs at different distances; PFS scheduling (No slicing)

We have a scenario with 3 UEs at different distances, each seeing a time-varying radio channel, scheduled using the proportional fair algorithm. The results are as shown:

System Parameters
CA Configuration n78
Channel Bandwidth 100 MHz
Pathloss Model 3GPPTR38.901-7.4.1
Outdoor Scenario Urban Macro
Fading Rayleigh
Scheduling Type Proportional Fair
EWMA Learning Rate 0.005
UE ID Avg. MAC Throughput (Mbps)
10 70.95
11 43.59
12 24.50

b. Network slicing to provide a 30 Mbps GBR to UE12

From (a), we observe that UE12 is currently achieving only 24.50 Mbps. To meet the 30 Mbps target, additional PRBs must be allocated to UE12. We create an eMBB slice and associate UE 12 to it. We see NetSim results from two slicing options: static resource allocation and dynamic resource allocation.

UE ID Throughput without
slicing (Mbps)
Throughput with Static
Resource Allocation (Mbps)
Throughput with Dynamic
Resource Allocation (Mbps)
10 70.95 35.58 62.68
11 43.59 21.15 38.73
12 24.50 30.00 (Thput. guarantee 30 Mbps) 30.02 (Thput. guarantee 30 Mbps)
Sum 139.04 86.73 131.35

We see that dynamic resource allocation achieves a 51.4% higher throughput while meeting the throughput guarantee for UE12.

c. Configuring multiple slices with dynamic resource allocation in NetSim

We consider the same scenario as in (a) and create two eMBB slices. We associate UE12 to one slice and UE11 to another slice; then configure a GBR of 30 Mbps to UE12 and 45 Mbps to UE11

UE ID Slices Throughput (Mbps)
10 Slice 1, BE 52.44
11 Slice 2 (Thput. guarantee 45 Mbps) 45.01
12 Slice 3 (Thput. guarantee 30 Mbps) 30.02
Sum throughput 127.31

NetSim's in-built dynamic resource allocation algorithm efficiently distributes resources between the slices, ensuring both meet their guaranteed bit rates.