# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## 4 Experiment Results
This result comes from the fact that SCATTER PHY ran on a powerful server, with 12 cores and 128 GB of RAM memory. Figure 7 depicts the comparison of the spectrogram and spectrum of filtered versus unfiltered transmissions. SCATTER PHY was able to enable or disable the FPGA based transmission filters at the command line. These results were saved with an Anritsu MS2690A Signal Analyzer. In the experiment, the signal analyzer sat beside the USRP hardware. The shown results were saved with SCATTER PHY's Tx gain set to 3 dB (this was configured through the control messages), Tx center frequency set to 1.9925 GHz (this was configured through at command line at start-up), and with the USRP's Tx output connected to the signal analyzer via a coaxial-cable connected to an attenuator with 20 dB of attenuation. As can be seen, OOBE (i.e., the OFDM lateral skirts) were mitigated when the 128 order FPGA based FIR filters were enabled. The results show the transmission of only one of two PHYs, the outcome being the same for the other PHY. Figure 8 compares the Mean Absolute Percentage Error (MAPE) of the fine CP based fractional CFO estimation method against fractional CFO estimation based on the synchronization signal. The MAPE for the CFO estimation is defined as:  
\[\text{MAPE }\%=\frac{100}{N}\sum_{i=1}^{N}\left|\frac{\text{CFO}^{\text{ est.}}(i)-\text{CFO}(i)}{\text{CFO}(i)}\right|, \tag{5}\]  
where \(\text{CFO}^{\text{est. }}(i)\) is the estimated CFO value for the \(i^{\text{th}}\) trial, \(\text{CFO}(i)\) is the randomly generated CFO, which is applied to the transmitted signal, for the \(i^{\text{th}}\) trial, and \(N\) is the number of trials over which the CFO is averaged. The results were obtained by connecting the Tx port to the Rx port of the same USRP so that the frequency offset caused by the HW was minimal or nonexistent and adding Additive White Gaussian Noise (AWGN) plus the desired frequency offset at the SW level just before the subframes were transmitted. The CFO applied to the signal was drawn from a uniform distribution varying from \(-\)7.5 kHz to +7.5 kHz, i.e., \(+/-\) half subcarrier spacing. As can be seen, the fine CFO estimation algorithm, which was based on the CP portion of the OFDM symbols, outperformed the Synch based CFO algorithm even when only two consecutive CPs were averaged. Additionally, we see that the performance of the CP based estimation improved as the number of averaged CPs increased; however, the downside of averaging more CPs was an increase in the processing time. The CP based CFO estimation method employed by SCATTER PHY was an improvement over the CFO estimation implemented by srsLTE. The SCATTER PHY implementation employed Single-Instruction Multiple-Data (SIMD) instructions in order to decrease the CFO estimation and correction computational time, allowing it to use more averaged CPs. Additionally, SCATTER PHYimplemented a more accurate and fine-grained complex exponential signal for generating the correction signal, making it possible to generate signals with frequencies that were closer to the estimated ones. Figure 6: SCATTER PHY throughput for several Tx bandwidths and Modulation Code Scheme (MCS) values. Figure 7: Comparison between unfiltered and filtered based COT transmissions. (**a**) Spectrogram without filtering. (**b**) Spectrum without filtering. (**c**) Spectrogram with filtering enabled. (**d**) Spectrum with filtering enabled. Figure 9 presents the comparison of the Packet Reception Rate (PRR) for each one of the signals carried by a SCATTER PHY subframe, namely synchronization, control, and data signals over several SNR and BW values. The SNR was calculated based on the power of a 1 ms subframe; therefore, before adding noise to the transmitted subframe, the subframe power was calculated, and then, the necessary noisy power to achieve the desired SNR was calculated. The MCS used for modulating the user data was set to zero, which was the most robust coding scheme, allowing SCATTER PHY to decode data in low SNR scenarios.