## 3 Interference Coordination Scheme Analysis
### User Pairing Scheme Combined with ABS
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Macrocell layout & 500 m macro-layer with 3 macrocell \\ Picocell layout & 2 picocell per macrocell \\ Number of users & 60 per macrocell \\ User layout & 20 users randomly distributed within 80 m range per picocell, the remaining users randomly distributed per macrocell \\ Transmit power & MBS: 46 dBm; PBS: 30 dBm \\ Bandwidth & 20 MHz \\ Path loss & MBS to user : \(128.1+37.6\)\(\log_{10}(R)\) dB in km \\ PBS to user : \(140.7+36.7\)\(\log_{10}(R)\) dB in km \\ Shadow fading & 10 dB \\ Noise power & \(-174+10\)\(\log_{10}(BW)\) dBm \\ Static power & MBS: 10 W; PBS: 0.1 W \\ Times of Monte Carlo simulation & 100,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters simulation. The relation between the average throughput of edge users and the fairness index under the random user pairing scheme is shown in Figure 4a. Under the random user pairing scheme with fixed power allocation, the power allocation factor has an optimal value with the best performance in edge users because there is an optimal resource allocation strategy in the network. As the power allocation factor increases, the fairness index of the networks increases. Since the power allocation factor is higher, the performance difference between strong users and weak users is smaller and the fairness is getting better with the decline of edge user performance. Although the network fairness is better, the network performance is worse which is not desirable. Moreover, the comparison results of power allocation schemes are shown in Figure 4b. Each power allocation scheme adopts the random user pairing scheme and the joint optimization algorithm of adaptive CRE and ABS shown in this paper. The simulation only compares the independent power allocation schemes to illustrate the effectiveness of the proposed power allocation scheme. The optimization scheme of most papers is a scheme of integrating power allocation with other aspects of optimization. It is difficult to simulate the effectiveness of the power allocation scheme alone. Therefore, it is not considered in this paper. Due to the difference in users in different pairs, a fixed power allocation scheme cannot meet the requirement of each pair of users at the same time and the dynamic power allocation can operate efficiently [14]. The proposed power allocation scheme dynamically allocates the power allocation factor of users in the same pair to ensure that the throughput difference of users in the same pair is minimized. That can balance the performance of network users and have better performance in network edge users with similar network fairness than the dynamic power allocation scheme in reference [14]. Compared with the fixed power allocation scheme, the proposed power allocation scheme can improve network fairness while ensuring the performance of edge users. The proposed power allocation is used to equalize the performance of users in the same pair. Due to the different transmit power levels in macrocells and picocells, the balanced results of user throughput in macrocells and picocells are different. The difference between balanced user throughput may be greater than that of unbalanced user throughput and the fairness index is slightly lower than the maximum fairness index of the fixed power allocation scheme. Figure 3: Throughput performances of edge users and fairness index: (**a**) under proposed user pairing scheme with different power allocation factors; (**b**) under different user pairing schemes (\(\alpha=0.9\)). Through the above analysis, both the user pairing scheme and power allocation scheme have an impact on the performance of edge users and network fairness. Moreover, the user pairing scheme has a greater impact. Simulation results under different schemes are shown in Table 2. The schemes of Table 2 contain the scheme in Figures 3b and 4b with the best performance of edge users.