OFDM signal detection of GP-periodic stochastic resonance system under PSO algorithm

In order to solve the problems of difficult manual parameter selection and poor demodulation effect in the application of weak OFDM (Orthogonal Frequency Division Multiplexing) signal demodulation in traditional stochastic resonance systems, a weak OFDM signal detection method for GP (Gaussian Potential)-periodic stochastic resonance system based on Particle Swarm Optimization (PWO) algorithm is proposed. This method uses the GP-periodic stochastic system to randomly resonate the two high-frequency signals after the quadrature transformation of the OFDM signal, and uses the relationship between the damping parameters and the signal frequency to downfrequency the two signals, so that the upper and lower signals can have random resonance. Then, the PWO algorithm is used to optimize the system parameters of random resonance to achieve the optimal resonance effect of the upper and lower signals. Finally, the signals after the upper and lower resonances are synthesized to complete the demodulation of the OFDM signal to obtain the final digital sequence. In this paper, the resonance effect of GP-period stochastic resonance system before and after optimization is discussed, the aggregation degree and bit error rate of the constellation diagram of OFDM system induced by GP-periodic random resonance before and after optimization are studied, and the effects of classical bistable random resonance, classical tristability random resonance and the proposed model on OFDM signal detection are compared and analyzed. The simulation results show that compared with the commonly used traditional stochastic resonance model, the degree of aggregation of signal points in the constellation diagram used for OFDM signal detection is higher, and the bit error rate of the OFDM system is reduced by about 50% under the same signal-to-noise ratio.