Abstract:

In order to cope with the complex underwater acoustic channel environment and improve the convergence speed and symbol error rate performance of the channel equalization algorithm, this paper proposed a zero attraction sparse control proportional minimum symbol error rate decision feedback equalization algorithm. On the basis of the proposed sparse control proportional minimum symbol error rate decision feedback equalization algorithm, this algorithm added a sparse constraint of approximate l_{\mathrm{0}} norm to the objective function, which pulls small amplitude equalizer taps toward zero. At the same time, phase-locked loop technology was introduced in the channel equalization process to eliminate the influence of jitter phase noise. The traditional phase-locked loop technology is based on the minimum mean square error criterion. However, existing literature and related experimental simulations have demonstrated that, when the mean square error of the system is the smallest, the symbol error rate is not necessarily the smallest. Aiming at this problem, a phase-locked loop phase tracking algorithm based on the minimum symbol error rate criterion was proposed and embedded in the sparse equalization algorithm. On the Matlab platform, experiments were carried out on the static underwater acoustic channel and the real time-varying underwater acoustic channel, respectively. The results show that the sparse control proportional minimum bit error rate decision feedback equalization algorithm with approximate l₀ norm constraint converges faster without the influence of time-varying phase noise; under the channel condition affected by time-varying phase noise, the phase tracking algorithm based on the minimum bit error rate criterion has faster convergence speed and better bit error rate performance than the fragrance tracking algorithm based on the minimum mean square error criterion.

Key words: channel equalization, sparsity, zero attraction, phase-locked loop, minimum symbol error rate