Transceiver System Simulation for BER vs SNR Performance Analysis

This document presents a comprehensive transceiver system simulation that analyzes the relationship between Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR). The primary objective is to evaluate and compare the BER performance of three distinct receiver algorithms: Least Mean Squares (LMS), Recursive Least Squares (RLS), and Maximum Likelihood Sequence Estimation (MLSE), specifically designed to mitigate the effects of Rayleigh fading channels through advanced equalization techniques. The transceiver architecture comprises multiple critical stages implemented through systematic MATLAB coding. The simulation begins with a modulation module where input data streams are encoded using digital modulation schemes (typically QPSK or 16-QAM), implemented through MATLAB's comm.PSKModulator or comm.RectangularQAMModulator objects. Following modulation, a pilot insertion module strategically adds known reference symbols using frame-based processing for subsequent channel estimation, crucial for tracking time-varying channel conditions. The signal then propagates through a composite channel model combining Additive White Gaussian Noise (AWGN) and Rayleigh fading effects. The AWGN channel is simulated using awgn() function with configurable SNR levels, while the Rayleigh fading component employs comm.RayleighChannel object with adjustable Doppler spread and path delays to mimic realistic multipath propagation environments. In the reception phase, the system implements three parallel receiver chains for performance comparison. The LMS receiver utilizes adaptive filtering with stochastic gradient descent optimization, typically implemented through dsp.LMSFilter with configurable step sizes. The RLS receiver employs recursive covariance matrix inversion for faster convergence using dsp.RLSFilter. The MLSE receiver implements the Viterbi algorithm through comm.ViterbiDecoder for optimal sequence detection against inter-symbol interference. Each receiver performs channel estimation using their respective algorithms: LMS employs iterative coefficient updates, RLS uses exponential weighting for historical data, while MLSE leverages trellis-based path metrics. After channel equalization, pilot symbols are extracted and the signal undergoes demodulation using corresponding demodulator objects to recover the original transmission data. Through extensive Monte Carlo simulations with varying SNR conditions and multiple transmission frames, this analysis provides quantitative insights into each receiver's effectiveness in combating Rayleigh fading challenges, offering practical implementation guidelines for wireless communication system designers. Performance metrics include BER curves, convergence rates, and computational complexity comparisons across different channel conditions.