Abstract
In frequency hopping spread spectrum (FHSS) systems, twomajor problems are timing synchronization and frequency estimation. A blind estimation scheme is presented for estimating frequency and transition time without using reference signals. The scheme is robust in the sense that it can avoid the unbalanced sampling block problem that occurs in existing maximum likelihood-based schemes, which causes large errors in one of the estimates of frequency. The proposed scheme has a lower computational cost than the maximum likelihood-based greedy search method. The estimated parameters are also used for the subsequent time and frequency tracking. The simulation results demonstrate the efficacy of the proposed approach.
Highlights
Frequency hopping spread spectrum (FHSS) techniques are widely used in military communications for combating narrowband interference and for security purposes
Ko et al [13] proposed a blind maximum likelihood- (ML-) based iterative algorithm for frequency estimation and synchronization using a twohop model; it yielded more than one solution, raising the problem of convergence to the solution that is associated with the hopping frequency
In the ML-based estimation approach, if the transition time in the processing data block between two hopping frequencies is close to the boundary value, the data block is in an unbalance situation of sampled signals in the frequency components
Summary
Frequency hopping spread spectrum (FHSS) techniques are widely used in military communications for combating narrowband interference and for security purposes. Liang et al [7] proposed a revisable jump Markov chain Monte Carlo(RJMCMC-) based algorithm for estimating frequency and timing parameters It requires that the hyperparameter is known in advance. In the ML-based estimation approach, if the transition time in the processing data block between two hopping frequencies is close to the boundary value, the data block is in an unbalance situation of sampled signals in the frequency components. In this scenario, the performance of one estimation of frequency is severely degraded.
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