Self-encoded spread spectrum synchronization with genetic algorithm and Markov chain analysis

In self-encoded spread spectrum (SESS), the traditional transmitting and receiving PN code generators are not needed. Instead, the spreading codes are extracted from the user´s information source, resulting in time-varying random codes. In this paper, we analyze SESS synchronization by means of a genetic model and Markov chain. This approach employs genetic search algorithm in the sequence generation and revision. Initial acquisition is achieved when the transmitter spreading sequence has been reproduced at the receiver to within an acceptable number of initial chip errors. In tracking, the reproduced sequence with initial chip errors is then transited into the error-free state. The analytical and simulation results demonstrate the veracity of genetic model and Markov chain analysis for SESS synchronization.