## 5 Simulation and Results
### Simulation Results and Analysis for Highway Scenarios  
This section will simulate the OFDM-IM method, based on the user needs and scenario adaptation, for highway scenarios. System performance graphs for different values of the N, K, and M index modulation parameters in highway scenarios will be generated. The simulation parameters are listed in Table 6.  
The input dataset for this fuzzy logic simulation includes values of the spectral efficiency, bit error rate, and energy consumption for different (N, K, M) combinations under this channel state scenario. The output is the energy efficiency evaluation Y value. The three decisive factors in maximizing the energy efficiency are used as inputs to the fuzzy logic system. The spectral efficiency, bit error rate, and energy consumption values at an SNR of 15 dB are fed into the system, which has predefined membership functions and fuzzy rules. Based on the defuzzified logical output, the maximum value determines the condition that maximizes the system's energy efficiency. In this scenario, considering users' need to manage charging difficulties, the point with the lowest system energy consumption, relatively high spectral efficiency, and a low system bit error rate is identified as the optimal index parameter point. As illustrated in Figure 15, based on the simulation results, the system performance for various index modulation parameter values in highway scenarios is established. From Figure 15a, it can be seen that, when the number of subcarriers is N = 4, the point with the highest energy efficiency is (N, K, M) = (4, 1, 4). Compared to traditional OFDM, which has (N, K, M) = (4, 4, 4), this optimal point in the OFDM-IM system significantly reduces the bit error rate (BER) and requires only 25% of the energy, even though the spectral efficiency is only 50% of that of traditional OFDM. This configuration sacrifices some spectral efficiency to achieve low energy consumption and a low BER, aligning with the needs of low-energy users and offering superior system performance. Compared to the sub-optimal point (N, K, M) = (4, 2, 2), this optimal point improves both the BER and energy consumption while maintaining the same spectral efficiency, highlighting its comprehensive performance advantage in this scenario. In Figure 15b, where the number of subcarriers is N = 8, the optimal point is (N, K, M) = (8, 2, 4). Although its spectral efficiency is only 50% of that of traditional OFDM (N, K, M) = (8, 8, 4), its BER and energy consumption are significantly lower. Compared to the sub-optimal point (N, K, M) = (8, 3, 4), the optimal point offers better overall performance due to its lower BER and energy consumption, despite its slightly lower spectral efficiency. In the case of N = 12 subcarriers, as shown in Figure 15c, the optimal point (N, K, M) = (12, 4, 4) has spectral efficiency of 66.5% of that of traditional OFDM (N, K, M) = (12, 12, 4). At the same time, it achieves a very low BER and requires only 25% of the energy. This optimal point provides better system performance due to higher spectral efficiency while maintaining low energy consumption and a low BER compared to the sub-optimal point (N, K, M) = (12, 4, 2). Lastly, in the case of N = 16 subcarriers, Figure 15d indicates that the optimal point (N, K, M) = (16, 5, 4) achieves a balance between the spectral efficiency, BER, and energy consumption. At this optimal point, the spectral efficiency of the OFDM-IM system is 75% of that of traditional OFDM (N, K, M) = (16, 16, 4), but its BER is much lower and the energy consumption is only 31.25%. Compared to the sub-optimal point (N, K, M) = (16, 5, 2), the optimal point offers a slightly higher BER but identical energy consumption, while its higher spectral efficiency makes it more suitable for situations where users prioritize spectral efficiency. The sub-optimal point can serve as an alternative option if users place a greater emphasis on minimizing the BER.