How to Start Cloud RAN Projects Using OMNeT++

To start a Cloud Radio Access Network (Cloud RAN) project in OMNeT++ which encompasses to replicate a distributed radio access network including centralized baseband processing units that are presented within the cloud. For 5G networks and beyond, cloud RAN is a crucial enabler to provide enhanced efficiency, scalability, and cost savings. Below is a simplified approach to get started:

Steps to Start Cloud RAN Projects in OMNeT++

  1. Understand Cloud RAN
  • Concept:
    • Centralized Processing: Baseband is centralized processing within the cloud.
    • Distributed Remote Radio Heads (RRHs): RRHs are utilized to users and associate to the centralized pool through the high-speed fronthaul links that were distributed.
    • Fronthaul Networks: High-capacity links among the RRHs and the centralized processing unit (CU/DU).
  • Key Features:
    • Low latency.
    • Resource pooling for better efficiency.
    • It supports for scalable and dynamic sets up.
  1. Prepare the OMNeT++ Environment
  • Install OMNeT++:
    • Go to the official site of OMNeT++ to download and install it on the system.
    • Make sure that all necessary dependencies are properly set up.
  • Install INET Framework:
    • INET framework offers models for wired, wireless interaction, and protocol stack simulation.
    • Copy the INET repository from git clone https://github.com/inet-framework/inet.git.
    • Execute and experiment the INET making sure that proper configuration.
  1. Research and Plan
  • Define Objectives:
    • Study project goals like fronthaul optimization, resource allocation, latency minimization, or scalability.
  • Select Metrics:
    • We should choose performance parameters such as network latency, throughput, resource utilization, and energy efficiency.
  • Use Cases:
    • 5G enhanced Mobile Broadband (eMBB).
    • Ultra-Reliable Low-Latency Communication (URLLC).
    • Cloud RAN support for massive IoT (mMTC).
  1. Design the Cloud RAN Model
  • Network Architecture:
    • Centralized Baseband Unit (BBU) Pool: It placed within the cloud for baseband processing that are centralized.
    • Remote Radio Heads (RRHs): Distributed nodes are nearby to the user in RRHs.
    • Fronthaul Links: High-speed links such as optical fiber or mmWave to associate the RRHs to BBUs.
  • System Components:
    • BBU: It supports to replicate the cloud processing center.
    • RRH: Design wireless nodes for user interaction.
    • Fronthaul Network: We execute it including INET’s wired/wireless models.
  1. Implement Cloud RAN in OMNeT++
  • Extend INET Framework:
    • Mimic wired (e.g., fiber) and wireless (e.g., LTE or 5G NR) interaction with the help of INET’s models.
    • Prolong the existing modules, inserting Cloud RAN-specific aspects for INET framework.
  • Develop Custom Modules:
    • BBU Pool:
      • It supports for centralized processing unit.
      • Execute the scheduling and resource allocation mechanisms.
    • RRHs:
      • We enhance the Wireless access points in RRHs for user devices.
      • Mimic antenna characteristics and interaction with BBUs.
    • Fronthaul Links:
      • Design the fronthaul links like network capacity and latency.
      • We need to replicate the congestion control and traffic engineering.
  • Simulation Scenarios:
    • Experiment the simulation scenarios like mobility, handover, and dynamic resource allocation.
    • Change volume of RRHs, user density, and fronthaul bandwidth.
  1. Run Simulation
  • Set Up Configuration Files:
    • We need to configure the network topology and metrics using .ned files.
    • In .ini files, we can set the simulation metrics such as fronthaul latency, RRH density.
  • Execute Simulations:
    • In the OMNeT++ IDE, we execute the simulation scenarios.
    • According to the preliminary outcomes to debug and enhance the model.
  1. Analyze Results
  • Metrics to Evaluate: Now, we estimate the performance parameters such as:
    • Latency among the RRHs and BBUs.
    • Fronthaul network utilization.
    • Throughput and QoS parameters for end-users.
    • Energy efficiency of the centralized processing.
  • Visualization:
    • For simulation visualization, we can utilise the built-in tools of OMNeT++.
    • Transfer information to external tools like Python, MATLAB, or R for analysis.
  1. Optimize and Extend
  • Optimization:
    • Test with fronthaul compression methods for optimization.
    • We should measure the diverse scheduling procedures for resource pooling.
  • Extensions:
    • For dynamic resource allocation, insert the machine learning models.
    • To mimic scenarios including heterogeneous networks (HetNets).
    • Integrate the support for multi-access edge computing (MEC).

This manual explored the Cloud RAN Projects that were simulated and analysed using OMNeT++ environment through given detailed procedure. Further updates on this subject will be shared. Drop us a message we will guide you immediately with best results.