# GPU Accelerated PIC and SIC for OFDM-NOMA
## 5 Conclusions  
In this work, a CUDA platform was proposed to implement computationally-challenging interference cancellation schemes in the reverse channel of OFDM-NOMA networks. Both SIC and PIC schemes were implemented, and their algorithms along with OFDM were illustrated. Finally, their computation times were compared. Numerical results showed significant speedups of the PIC scheme with GPU implementation as compared to CPU. Furthermore, for a large number of users, PIC was found to be approximately 75-times and 220-times faster than SIC on GPU for FFT sizes of 4096 and 2048, respectively.  
5G base stations are expected to face computationally-heavy tasks for baseband processing. In addition to interference cancellation, some other techniques such as MIMO at improved data rates are going to put a tremendous baseband processing load on 5G base stations. The results here demonstrated that PIC on GPU took only 3 ms to decode 50 users and left room for other computationally-heavy processes to satisfy the strict latency requirement of 5G networks.  
[PERSON] is the first author who proposed the main idea, analyzed and simulated the system, and presented the writing--original draft preparation, writing--review and editing, visualization. [PERSON] is the second who has experience in wireless communication research. He has made a supervision, review, and given the first author some useful comments and funding acquisition for this research. [PERSON] is the third who analyzed the data and contributed the analysis tools. All authors read and approved the final manuscript.  
This research was partially supported by the Universiti Putra Malaysia under the Grant Putra, GP/2017/9569600.  
**Acknowledgments:** We would like to thank School of Engineering, Nazarbayev University for providing us the GPU equipment for computation and also we would like to thank our colleagues from Faculty of Computer Science and Information Technology, Universiti Putra Malaysia as the NVIDIA GPU Education Center, who provided insight and expertise that greatly assisted the research.  
\begin{table}
\begin{tabular}{r c c c c} \hline \hline \multirow{2}{*}{**Number of Users**} & \multicolumn{2}{c}{**CPU**} & \multicolumn{2}{c}{**GPU**} \\ \cline{2-5}  & **SIC** & **PIC** & **SIC** & **PIC** \\ \hline
50 & 27.70 & 44.01 & 68.69 & 1.97 \\
100 & 45.49 & 84.90 & 138.39 & 2.08 \\
150 & 68.22 & 130.17 & 208.28 & 2.19 \\
200 & 91.05 & 173.69 & 281.20 & 2.21 \\
250 & 113.73 & 223.56 & 353.08 & 2.23 \\
300 & 135.94 & 269.35 & 426.68 & 2.26 \\
350 & 157.98 & 315.15 & 500.45 & 2.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the computational times of SIC and PIC in ms with an FFT size of 2048.  
\begin{table}
\begin{tabular}{r c c c c} \hline \hline \multirow{2}{*}{**Number of Users**} & \multicolumn{2}{c}{**CPU**} & \multicolumn{2}{c}{**GPU**} \\ \cline{2-5}  & **SIC** & **PIC** & **SIC** & **PIC** \\ \hline
50 & 46.65 & 87.83 & 69.22 & 2.54 \\
100 & 93.77 & 172.86 & 141.49 & 3.22 \\
150 & 142.67 & 263.28 & 213.03 & 4.13 \\
200 & 188.77 & 349.27 & 285.72 & 4.81 \\
250 & 236.25 & 442.43 & 360.51 & 5.52 \\
300 & 279.16 & 530.04 & 439.31 & 6.00 \\
350 & 330.65 & 616.26 & 514.95 & 6.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the computational times of SIC and PIC in ms with an FFT size of 4096.  
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.