PERFORMANCE ANALYSIS OF THE USE OF LINEAR APPROXIMATION METHOD TO IMPROVE MULTICARRIER COMMUNICATION IN NOISY ENVIRONMENTS

Summary

Multicarrier communication systems, especially Orthogonal Frequency Division Multiplexing (OFDM), face challenges when it comes to performance, particularly due to high Peak-to-Average Power Ratio (PAPR). This issue can push power amplifiers into nonlinear territory, which, when combined with the additive noise found in wireless channels, leads to higher bit error rates and spectral regrowth. In this study, we introduce and assess a linear approximation method that’s applied at the transmitter to tackle the dual challenges of power amplifier nonlinearity and channel noise in multicarrier communication systems. Our approach uses a first-order Taylor series expansion to linearize the Saleh model of a nonlinear power amplifier. We ran MATLAB simulations with 16-Quadrature Amplitude Modulation (16-QAM) OFDM signals, comparing the performance of our linearized system against the traditional nonlinear Saleh model under three different Additive White Gaussian Noise (AWGN) conditions: high (15 dB), medium (5 dB), and severe (-5 dB) Signal-to-Noise Ratios (SNR). We looked at various performance metrics, including Symbol Error Rate (SER), Error Vector Magnitude (EVM), Modulation Error Ratio (MER), and constellation diagram analysis. The results showed that our linear approximation method significantly boosts system performance across all SNR levels. At SNR = 15 dB, we saw improvements of 31.10% in SER, 31.04% in EVM, and 74.27% in MER. For SNR = 5 dB, the improvements were 19.74% in SER, 24.77% in EVM, and 37.55% in MER. Even in challenging conditions (SNR = -5 dB), our method still managed a 3.37% improvement in SER and 20.28% in EVM. The constellation diagrams illustrated a tighter clustering of received symbols and less distortion when linearization was applied. Overall, our findings indicate that using a straightforward first-order approximation for transmitter-side linearization effectively reduces in-band distortion and enhances resilience against channel noise, providing a computationally efficient alternative to more complex receiver-based compensation techniques.

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