OFDM 64-QAM BER and SER Performance Plot Analysis

This document explores the performance analysis of OFDM systems employing 64-QAM modulation through BER and SER plots. Orthogonal Frequency Division Multiplexing (OFDM) is a fundamental digital modulation technique in modern wireless communication systems that divides the available frequency spectrum into multiple orthogonal subcarriers. In code implementations, this is typically achieved using Inverse Fast Fourier Transform (IFFT) for transmission and Fast Fourier Transform (FFT) for reception, with cyclical prefix insertion to mitigate inter-symbol interference. 64-QAM (Quadrature Amplitude Modulation) is a high-order modulation scheme where each symbol encodes 6 bits of information by varying both amplitude and phase of the carrier signal. In simulation code, this is implemented by mapping binary sequences to complex constellation points representing 64 possible symbol states. The Euclidean distance between constellation points directly affects the system's error performance. Bit Error Rate (BER) measurement quantifies the ratio of erroneously received bits to total transmitted bits. In simulation implementations, BER is calculated by comparing transmitted and received bit sequences using error counting algorithms, often plotted against Signal-to-Noise Ratio (SNR) to analyze performance degradation under noisy conditions. Symbol Error Rate (SER) represents the probability of incorrect symbol detection, which is particularly important for higher-order modulations like 64-QAM where symbol errors can cause multiple bit errors. SER calculation algorithms typically involve minimum distance detection and error probability computations based on Q-function approximations. The performance plot combines BER and SER curves to visualize system behavior across varying channel conditions. Implementation typically involves Monte Carlo simulations with additive white Gaussian noise (AWGN) channels, where transmitted signals are corrupted with controlled noise levels to generate error statistics. Key MATLAB functions for such implementations include qammod, qamdemod for modulation/demodulation, and awgn for channel noise introduction. This analytical plot serves as a crucial tool for evaluating system robustness, optimizing parameters, and comparing different communication schemes under standardized test conditions, providing valuable insights for wireless system design and performance prediction.