# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 4 Performance Evaluation  
### Simulation Setup  
We have evaluated the performance of PSC-UL using a simulator implemented in Python. In the default setting, a 100 m \(\times\) 100 m simulation area is divided into 100 cells, where an AP is placed at the center of each cell. One-hundred user stations are randomly deployed in the area, and each station associates with the closest AP. The simulation environment is illustrated in Figure 7. We assume that all user stations are backlogged with packets to send to the AP, so they contend for the channel constantly. We use UDP (User Datagram Protocol) traffic with 1472 bytes of packet size, and the MCS is set so that the link speed is 65 Mbps for 20 MHz channels. We use the log-distance model as the pathloss model. In the model, the reference distance \(d_{0}\) is 1 m, the reference pathloss (\(PL_{0}\)) is 46.67 dB, and the pathloss exponent (\(\gamma\)) is 3. The transmit power is set to 25 dBm for both APs and stations, and the noise floor is set to \(-93.97\) dBm. The minimum SNR required for successfully receiving a packet is set to 23 dBm, and we use 5 dB as margin, unless otherwise specified. We use six metrics to evaluate the performance. The first metric is 'network throughput', which indicates the number of data bits successfully transferred in the network per second. With the same environment and configuration parameters, a method with higher spatial reuse will achieve higher throughput. However, just measuring the network throughput may mislead the results, because high throughput can also be achieved by having a small number of nodes dominate the channel and starving all other nodes. A notion of fairness should also be measured. For that we use 'bottom 50% throughput' and 'bottom 25% throughput'. For these metrics, we sort the node throughput in the ascending order, and select 50% and 25% of the nodes with the least throughput. Then, we aggregate their throughput to obtain the results. If a method achieve a high network throughput but a low bottom 50% or bottom 25% throughput, it means the method is favoring only a few nodes in the network while starving all others. We also measure [PERSON]'s fairness index,  
Figure 7: The default simulation environment. One-hundred APs are placed in a 100 m \(\times\) 100 m area, and 100 user stations are randomly deployed inside the area. which is a more direct metric used to measure fairness of a system. The equation for [PERSON]'s fairness index is shown in Equation (16). In the equation, \(x_{i}\) is the throughput of node \(i\), and \(n\) is the total number of nodes. \[J(x_{1},x_{2},...,x_{n})=\frac{(\sum_{i=1}^{n}x_{i})^{2}}{n\times\sum_{i=1}^{n}x _{i}^{2}} \tag{16}\]  
As the fifth metric, we measure the 'non-starvation ratio', which is the percentage of nodes that transmitted at least one packet successfully during the simulation time, which can be another indication of fairness. Finally, we measure the 'packet delivery ratio'. Packet delivery ratio reveals how aggressively nodes transmitted their packets. If a method has a low delivery ratio, it means a lot of collisions occurred due to aggressive transmissions. Using these evaluation metrics, we compare the performance of five different methods. The first method, \"legacy\" is the original 802.11 DCF with fixed CST. The default CST is set to \(-82\) dBm. The second method is \"OBDS/PD\", where a node may decide to transmit with a lower transmit power when the sensed interference is between \(CST_{min}\) and \(CST_{max}\). In the simulations, \(CST_{min}\) is \(-82\) dBm and \(CST_{max}\) is \(-62\) dBm. The third method is \"Dual-CST\", which is a method proposed in [5] and uses \"advertised CST\" in addition to the original CST. The fourth method is \"PSR\", where an AP advertises acceptable interference in the preamble, and a node in a different BSS may choose to transmit if its interference will not exceed the acceptable interference level at the AP. In PSR, we set \(UL_{\_Target\_RSS1}\) to \(-32\) dBm.