The growing complexity of communication networks, especially with the introduction of 5G and future 6G, requires new approaches to ensure that these networks can meet the increasing demands for low latency, high reliability, and efficient resource allocation. Traditional centralized control methods often struggle to cope with the dynamic nature of these networks, particularly in environments with a dense deployment of small cells. These cells, which serve as the backbone of integrated access and backhaul (IAB) systems, are critical for extending coverage and enhancing capacity in urban areas. However, managing these networks effectively requires solutions that can adapt in real-time, optimize performance autonomously, and reduce the operational costs associated with network maintenance.
Agent-based modeling (ABM) offers a promising approach to address these challenges by simulating the behavior of individual network elements as autonomous agents capable of self-organization. By allowing each agent to make local decisions based on its environment and interactions with neighboring agents, ABM can mimic the distributed nature of modern communication networks. This approach can lead to more resilient and adaptive networks, where optimization happens at a local level, reducing the need for extensive centralized control. In this context, the proposed project aims to develop an ABM simulation tool to explore and enhance self-organization mechanisms in communication networks, with a focus on optimizing latency and IAB performance in small cell deployments.
Goal: Developing an Agent-Based Simulation Tool for Distributed Network Optimization
The primary goal of this project is to design and implement an agent-based modeling simulation tool that will allow us to study and improve self-organization in communication networks. This tool will enable the exploration of how autonomous agents within the network can cooperate to optimize key performance metrics such as latency and the efficiency of integrated access and backhaul in small cell environments.
To achieve this, the project will involve creating a robust simulation environment where different network configurations can be tested under varying conditions. The agents in this simulation will represent different network elements, such as base stations or relay nodes, each equipped with decision-making algorithms designed to minimize latency and maximize IAB efficiency. The project will focus on developing and fine-tuning these algorithms to enable the agents to self-organize in a way that optimizes the overall network performance.
Additionally, the tool will be designed to allow for easy customization and extension, so that different optimization strategies can be tested and compared. The ultimate objective is to identify the most effective strategies for distributed network optimization and to provide insights that can be applied to real-world communication networks, particularly in the context of small cell deployments and IAB systems.