Performance Analysis of Relay Selection on Cooperative Uplink NOMA Network with Wireless Power Transfer

Abstract

Wireless power transmission in the next-generation wireless networks is the subject that attracts a lot of attention from academia and industry. In this work, we study and analyze the performance of relay selection on uplink non-orthogonal multiple access (NOMA) networks with wireless power transmission. Specifically, the considered system consists of one base station, multiple power-constrained relays and a pair of NOMA users. The best relay (with highest energy harvested from the base station) is chosen to cooperate with two users which use NOMA scheme to send messages to the base station. To analyze the performance, based on the statistical characteristics of signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR), using the Gaussian-Chebyshev quadrature method, the closed-form expressions of outage probability and throughput for two users are derived. In order to understand more details about the behavior of this considered system, the numerical results on outage probability and throughput of a given system are provided following the system key parameters, such as the transmit power, the number of relays, time switching ratio and energy conversion efficiency. In the end, the theoretical result is also verified by using the Monte-Carlo simulation. The simulation results demonstrate that the performance of the system is improved by increasing the number of relays.

Appendices

Appendix A

where step (a) is obtained by letting $z = e^{-\sqrt{\frac{\mu }{(\gamma _4 y - \gamma _{th})}}}$ in which $\mu = \frac{\gamma _{th}}{a \lambda _{r^*} \gamma _{r^*}}$, step (b) and (c) are obtained by applying the Gaussian-Chebyshev quadrature method in which $u_i = \frac{\phi _i + 1}{2}, \phi _i = cos(\frac{2i-1}{2M}\pi ), v_j = \frac{\varphi _j +1}{2}, \varphi _j = cos(\frac{2j - 1}{2H}\pi ), M$ and H are the complexity-vs-accuracy trade-off coefficients.

Appendix B

where step (a) is obtained by using Eqs. (13) and (17), step (b) is obtained by letting $z=e^{-\frac{\mu }{\sqrt{y}}}$ in which $\mu =\frac{\gamma _{th}}{a \lambda _{r^*}^2 \gamma _{r^*}}$, step (c) is obtained by applying the Gaussian-Chebyshev quadrature method in which $u_i = \frac{\beta _i+1}{2}$ with $\beta _i=\cos \left( \frac{2i-1}{2M}\pi \right)$, and M is the complexity-vs-accuracy trade-off coefficient.