Abstract
This paper presents a performance evaluation approach using the BERTool module provided under Matlab/Simulink software package. It compares different Quadrature Amplitude Modulation (QAM) techniques at different bit rates (8, 16, 32, 64, 128, and 256) based on the Bit Error Rate (BER) versus the Ratio of Bit Energy to Noise Power Spectral Density (Eb/No). The paper provides a detailed model built in Simulink to simulate the QAM techniques along with the results of the performance analysis. Analysis of the impact of variations in the different model parameters on the resulting error rate in the transmitted data has been conducted as well. The motivation for this paper is to review, model, and simulate the QAM technique at its various bit rates using the Matlab/Simulink. Also, this paper serves an educational purpose for researchers in the wireless communication field or related topics by illustrating a step-by-step approach to build the model and simulate the system using the Matlab/Simulink in conjunction with the BERTool used for performance analysis to evaluate different QAM wireless communication techniques. The Monte Carlo simulation is utilized by the BERTool to conduct the performance analysis. The resulting bit error rate from the simulation at the different QAM transmission rates (8 to 256) showed the variation of the error values for each bit rate versus different noise power spectral densities (Eb/No). Also, the results show a comparison between the resulting transmission errors in the received signal at different noise or Eb/No levels. Since Eb/N 0 is defined as the ratio of bit energy per symbol to noise power spectral density, in decibels, then increasing this ratio should result in less overall transmission errors and decreasing this ratio should result in higher transmission error. This illustrates how the model captures the variation of the signal power to the power of the applied noise during the transmission process. Also, the model simulates the impact of changing the power of the transmitted signal on the generated Noise Variance by the Additive White Gaussian Noise (AWGN) generator. The simulation illustrates that as the power of the transmitted signal increases, the error rate increases too as a result of the logic implemented in the AWGN generator which in turn increases the noise component imposed to the transmitted signal.