Explore MATLAB source code curated for "PN码" with clean implementations, documentation, and examples.
Implementation of BPSK modulation and demodulation in Simulink using random signal sources combined with PN code spreading techniques, with final demodulated output showing high consistency with original random signals across various test scenarios.
MATLAB-based PN Code Simulation - PN codes play a crucial role in communication systems, particularly in upcoming 3G mobile communication standards. This experiment demonstrates generation, properties, and implementation using MATLAB's communication toolbox functions.
MATLAB-based implementation of PN code acquisition and tracking using matched filters, featuring code examples and algorithm explanations to aid learners in understanding spread spectrum communication techniques
This compressed folder contains over twenty MATLAB simulation programs covering GMSK signal modulation simulation, PN code generation, FIR filter design, Gold sequence generation, along with numerous classic filter transformation mini-programs demonstrating practical algorithm implementations.
Pseudo-random codes, also known as PN codes, are essential sequences in spread spectrum communication. Spread spectrum communication fundamentally operates by modulating original signals with highly correlated pseudo-random sequences, causing the signal to manifest according to the sequence's characteristics. The implementation involves generating m-sequences using linear feedback shift registers (LFSRs) with specific primitive polynomials. The MATLAB code below demonstrates m-sequence generation where different polynomial configurations produce sequences of varying orders, with key parameters including tap positions and initial states determining correlation properties and sequence length.
A direct sequence spread spectrum communication system with interference, where PN codes are generated using m-sequence generators and transmitted through an additive white Gaussian noise (AWGN) channel. The received signal at the receiver comprises "useful signal + AWGN + interference." The system performs despreading and decision operations to recover user signals. Key implementation aspects include generating m-sequences using linear feedback shift registers (LFSRs), implementing correlation-based despreading algorithms, and employing threshold detection for signal decisions. The system demonstrates bit error rate (BER) performance versus Eb/N0 ratio under different spreading gains (10, 30, 50), showing how processing gain affects interference rejection capability.
Implementing PN code as signal source with timing synchronization and carrier recovery techniques to achieve correct information reception at the receiver end, including digital signal processing implementation methods.