## 3 Non-Orthogonal Multiple Access
Moreover, users located at the edge of the cell are more subject to inter-cell interference, requiring more power to keep the signal-to-noise ratio at an acceptable level. Naturally, the fading effects, and the power control used to mitigate these effects, can modify these power scales. We can associate the two users considered in Figure 6 to the scenario depicted in Figure 5. The reference user, with a power of 1, corresponds to User 1 of Figure 5 (farther from the base station), and the interfering user, with a power of 0.5, corresponds to User 2 of Figure 5 (closer to the base station). Let us focus on the scenario employed in Figure 6. Considering that the propagation losses occurring in the distance between the location of User 2 and the location of User 1 is PATH_LOSS, at User 2's location (the reference user) the received power of User 1 is 1, and the received power of User 2 is 0.5. Similarly, at User 1's location (the interfering user), the power of 1 is 1 + PATH_LOSS (PATH_LOSS having a positive effect), while the power of User 2 is 0.5 + PATH_LOSS (PATH_LOSS having a positive effect). Consequently, regardless of the considered user (reference user or interfering user), the difference of powers between them is kept unchanged. The performance was evaluated using Monte Carlo simulations, observing the bit error rate (BER) as a function of \(E_{b}/N_{0}\), where \(E_{b}\) is the energy of the transmitted bits and \(N_{0}\) is the one-sided power spectral density of the noise. Ideal channel estimation and the SC-FDE were assumed [11; 15], with quaternary phase shift keying (QPSK) modulation and a block length of N = 256 symbols (with similar results observed for other values of N, provided that N > 1). A Rayleigh fading channel was considered, with 16 uncorrelated equal power paths. The duration of the useful part of the blocks (N symbols) was 1 \(\upmu\)s, and the cyclic prefix had a duration of 0.125 \(\upmu\)s. The match filter bound (MFB) curve is a way to measure the channel modeled by the sum of the delayed and independently Rayleigh-fading rays, which can be viewed as a lower bound [34]. Figure 6: Results for two NOMA users with powers of (1, 0.5), with \(4\times 32\) MIMO. As seen in Figure 6, the results with conventional NOMA are quite limited, due to the existence of a high level of interference. Note that the SIC that is part of the receiver of the reference user (User 1 of Figure 5) only detects, regenerates, and cancels user signals with powers higher than that of the reference user, which is not the case here as the interfering user has a power of 0.5, which is not cancelled. This explains the low performance achieved with conventional NOMA. Cooperative NOMA comprises the detection of other users with SIC on the receiver of the interfering users (closer to the base station and, therefore, with lower power). Therefore, the interfering signals associated with all users are cancelled. The power of User 2 (seen by User 1 as an interfering user) is higher than that of User 1, being detected, regenerated, and cancelled using SIC to allow the detection of the data sent to User 2. Cooperative NOMA considers that User 2 retransmits the symbols detected by SIC (typically using time-division multiplexing). These two signals are combined to improve performance. In Figure 6, we can see that the combination of signals performed with cooperative NOMA results in a high level of performance improvement, as compared with conventional NOMA. In fact, the performance of cooperative NOMA is very close to the MFB. Figure 7 shows the BER performance in the same scenario as that of Figure 6, the only difference being that the power of users is (0.5, 1) (instead of (1, 0.5)). In this scenario, the reference user is closer to the base station (power of 0.5), while the interfering user is farther from the base station (power of 1).