## 6 Numerical Results
### Performance in the Asymmetric Networks  
In the simulations above, the users are assumed to be free from large-scale fading. However, in practical communication systems, different users might locate randomly and suffer from different levels of large-scale fading. In this section, we evaluate the IA schemes in more practical asymmetric networks. It is assumed that the transmitters are uniformly located in a 10 km \(\times\) 10 km square with the receivers randomly distributed around the associated transmitters. The distance between each transmitter-receiver pair is 1 km, and the path-loss exponent \(\alpha\) is set to be 3.76 [13]. Different from the simulations in Subsection 6.1, the grouping combinations in the asymmetric networks might have an impact on the performance of the JSCC-IA scheme. Since brute-force searching of all the possible grouping combinations is unacceptable for the networks with large number of users, the RGS JSCC-IA scheme is introduced in Section 5, which can reduce the computational complexity and select better grouping combinations. In this subsection, we evaluate the RGS JSCC-IA scheme and compare it with the conventional IA system. The preset objective minimal received SINR and maximal number of iterations for the RGS JSCC-IA scheme are set to be \(\xi\) = 15 dB and as \(\Theta\) = 20, respectively. The error performances of the conventional IA, JSCC-IA and RGS JSCC-IA schemes are compared in Figures 11 and 12 for the (4 \(\times\) 4, 1)\({}^{20}\) and (6 \(\times\) 6, 2)\({}^{18}\) networks, respectively. From the results, we can observe that the conventional IA system can achieve better BER in the asymmetric than the symmetric networks for the reason that the interference is relatively weaker due to the large-scale fading. In spite of this, the proposed JSCC-IA scheme can still achieve lower average BER than the conventional IA system. Figure 11: The average experimental BER performances of the conventional IA scheme, the JSCC-IA schemes with identical/different propagation delays and the RGS JSCC-IA schemes with identical/different propagation delays in the (4 \(\times\) 4, 1)\({}^{20}\) asymmetric networks. For the (4 \(\times\) 4, 1)\({}^{20}\) network in Figure 11, given the BER of 10\({}^{-5}\), the \(E_{b}/N_{0}\) required by the JSCC-IA system with identical propagation delay is 0 dB, compared with the 4 dB required by the conventional IA system. When RGS algorithm is applied, it provides approximately 2 dB \(E_{b}/N_{0}\) gains over the non-RGS JSCC-IA scheme at the BER of 10\({}^{-5}\), and the gain increases with the decrease in BER. Although the different propagation delays degrade the performance of the JSCC-IA system, the difference of the \(E_{b}/N_{0}\) between the JSCC-IA schemes with identical and different delays is no more than 2 dB at the same BER. Besides, the JSCC-IA scheme with different propagation delays still outperforms the convention IA system, especially when the RGS algorithm is applied. At \(E_{b}/N_{0}\) = 2 dB, the BER of the conventional IA scheme, the RGS JSCC-IA schemes with identical and different propagation delays is 10\({}^{-4}\), 10\({}^{-7}\) and 10\({}^{-6}\), respectively. For the (6 \(\times\) 6, 2)\({}^{18}\) case in Figure 12, the BER performance of the JSCC-IA scheme is better than that of the conventional IA system. Considering the \(E_{b}/N_{0}\) of 2 dB, the BER of the JSCC-IA schemes with identical and different propagation delays is respectively 10\({}^{-7}\) and 10\({}^{-6}\), while the conventional IA system can only reach the level of 10\({}^{-4}\). When the RGS algorithm is applied, the RGS JSCC-IA scheme can show even better performances. Given the BER of 10\({}^{-8}\), the RGS strategy can provide 4 dB \(E_{b}/N_{0}\) gains over the non-RGS JSCC-IA scheme for the scenario of different propagation delays. Moreover, the gain increases with the raise in \(E_{b}/N_{0}\), implying that the RGS algorithm can select better grouping combinations.