## 3 Simulation Results
### Channel Measurements
#### 3.1.2 Scenario 2: Meeting Room  
In this section, we delve into the unique characteristics of the meeting room environment, where our channel measurements were conducted. Figure 6 provides a comprehensive overview, depicting the S21, CFR, CIR, and averaged PDP for the channel within the meeting room.  
The meeting room presented an interesting and realistic setting for indoor wideband measurements, characterized by the presence of various objects and furniture. Notably, the room featured a projector, desks, chairs, and other office equipment, contributing to the complexity of the propagation environment. The abundance of furniture and office elements introduced additional obstacles, creating diverse propagation paths and reflections within the room. This dynamic environment offered valuable insights into real-world indoor channel characteristics, enriching our understanding of wireless communication within confined spaces.  
Figure 6 serves as a visual representation of the channel behavior within the meeting room, capturing the intricate interplay of signals amidst the presence of furniture and office equipment. By analyzing the S21, CFR, CIR, and averaged PDP, we gain valuable insights into the propagation dynamics and multipath effects within this environment.  
Normally, we cannot use measured SISO channels for MIMO simulations directly due to equipment limitations. This is because MIMO systems leverage multiple antennas at both the transmitter and receiver ends to exploit spatial diversity and multiplexing gains, which are not captured in SISO measurements.  
When we conduct SISO channel measurements, we typically only have access to the channel response between a single transmit and single receive antenna pair. This means that important characteristics such as spatial correlation, spatial multiplexing gain, and diversity gain specific to MIMO systems are not accounted for in SISO measurements.  
To use SISO measurements in MIMO simulations despite these limitations, we often need to extrapolate or extend the information obtained from the single transmit and single  
Figure 5: S21, CFR, CIR, and averaged PDP of the channel in the corridor.  
receive antenna pair to approximate the behavior of multiple antennas. This extrapolation involves making assumptions or applying techniques to infer how the channel would behave in an MIMO scenario with multiple antennas at both ends.  
Based on the measured SISO channel data, we extend our understanding to an MIMO context. One common approach is to use spatial correlation models to estimate how the channel characteristics vary across different antenna elements in an MIMO system. Spatial correlation models help us to understand the relationship between the signals received at different antenna elements. By assuming certain correlation structures between antennas, such as the Kronecker model or the exponential correlation model, we can interpolate the SISO measurements obtained from a single antenna pair to predict the behavior of multiple antennas as a virtual MIMO channel.  
Additionally, statistical channel models tailored for MIMO systems can be applied to generate synthetic channel realizations based on the SISO measurements. These models take into account factors such as spatial correlation, antenna spacing, and scattering characteristics to simulate the behavior of MIMO channels.  
In summary, while SISO measurements provide valuable insights into channel characteristics, they typically require adaptation or extension to be applicable for MIMO simulations due to equipment limitations. Reasonable assumptions or models are necessary to bridge this gap and accurately represent the behavior of the MIMO channels in simulations.