System-Level Simulation

Operates on a discrete event simulation paradigm where the system state changes only at scheduled points in time, such as packet arrivals or channel quality indicator (CQI) reports. This avoids simulating every symbol period, enabling the efficient modeling of millions of events over long simulation intervals. The core loop advances the clock from event to event, triggering the MAC scheduler, admission control, and handover algorithms precisely when needed.

Replaces computationally expensive link-level bit-error rate calculations with abstracted performance curves. Instead of simulating every bit, the system uses a mutual information effective SINR mapping (MI-ESM) or similar model to predict the block error rate (BLER) based on the instantaneous signal-to-interference-plus-noise ratio (SINR). This abstraction bridges the gap between a detailed link-level simulation and a large-scale network model, making multi-cell scenarios computationally tractable.

Employs Geometry-Based Stochastic Channel Models (GSCMs) to generate realistic, correlated channel parameters for each UE. Unlike simple statistical models, GSCMs ensure spatial consistency, meaning a moving UE experiences smoothly evolving path loss, shadow fading, and angle of arrival. This is critical for testing beamforming and handover algorithms, which fail under unrealistic, independently generated channel snapshots. A shadow fading map and path loss map are often pre-generated from a propagation model.

Integrates traffic generators that create synthetic data packets conforming to specific application models like File Transfer Protocol (FTP), HTTP browsing, or Voice over IP (VoIP). This is combined with a user mobility model that simulates UE movement using statistical patterns (e.g., random waypoint) or predefined routes. The interplay of bursty traffic and mobility creates the dynamic load imbalances that predictive load balancing and proactive caching algorithms are designed to solve.

Models the cross-layer interaction between the Radio Resource Control (RRC), Packet Data Convergence Protocol (PDCP), Radio Link Control (RLC), and Medium Access Control (MAC) layers. This allows the simulation to capture the impact of MAC scheduler decisions on end-to-end latency and throughput. For example, a scheduling delay caused by resource starvation directly manifests as a queuing delay in the RLC buffer, providing a true measure of Quality of Service (QoS) for each bearer.

A simulation methodology that models a single communication link between a transmitter and receiver to evaluate physical layer performance. Unlike system-level simulation, it focuses on block error rate (BLER) and throughput for a specific connection under detailed channel conditions.

• Provides the abstracted performance curves used by system-level simulators
• Models bit-level processing, modulation, and coding schemes
• Essential for generating accurate link-to-system mapping tables

A mathematical formulation that predicts the path loss and signal characteristics of radio waves as they travel through an environment. These models are the bedrock of any system-level simulation, determining the received signal strength at every user equipment.

• Empirical models (e.g., Okumura-Hata) use measurement-based formulas
• Deterministic models (e.g., ray tracing) compute exact paths using 3D geometry
• Semi-deterministic models combine both approaches for a balance of accuracy and speed

A statistical or trace-based model that simulates the movement patterns, speed, and direction changes of user equipment within a network simulation. Mobility triggers handovers and causes rapid channel variations, making it a critical dynamic input.

• Random Waypoint is a classic statistical model for research
• Trace-based models replay real-world GPS or call detail records
• Directly impacts handover simulation and spatial consistency requirements

A software or hardware tool that creates synthetic data packets conforming to specific application patterns and protocols. It loads the simulated network with realistic demand, allowing the MAC scheduler and resource allocation algorithms to be stress-tested.

• Models full buffer, FTP, HTTP, and video streaming traffic
• Generates Quality of Service (QoS) requirements per flow
• Essential for evaluating admission control and load balancing algorithms

A simulation paradigm where the system state changes only at discrete points in time upon the occurrence of scheduled events. This is the computational engine underlying system-level simulators like ns-3, enabling efficient modeling of complex networks without simulating every instant.

• Events include packet arrivals, scheduling decisions, and mobility updates
• Far more computationally efficient than continuous time-step simulation
• Allows simulation of thousands of users over long time periods

A logical function in a base station that allocates time-frequency radio resources to user equipment. It is the central decision-making algorithm tested in system-level simulation, balancing channel quality, QoS requirements, and fairness.

• Common algorithms include Proportional Fair, Round Robin, and Max C/I
• AI-driven schedulers are a primary focus of modern RAN optimization
• Performance is measured by cell throughput, user fairness, and latency