Reduced Complexity Blind Frequency and Transition Time Estimation in Frequency Hopping Systems

• Main Authors: Kuo-Ching Fu, 傅國清
• Other Authors: Yung-Fang Chen
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/53299419100650821495

博士 === 國立中央大學 === 通訊工程學系 === 101 === Frequency hopping spread spectrum (FHSS) is a technology for combating narrow band interference. Two important parameters required for estimation in FHSS are transition time and hopping frequency. We proposed three algorithms for estimating transition time and hopping frequency in FHSS. In the first algorithm, blind subspace-based schemes with a maximum likelihood (ML) criterion for estimating frequency and transition time without using reference signals are proposed. The selection of the related parameters is discussed. Subspace-based algorithms are applied with the help of the proposed block selection scheme. The performance is improved with a block selection algorithm (BSA) to overcome the unbalanced processing block problems in various algorithms. The proposed method significantly reduces computational complexity compared with a greedy search ML-based algorithm. The performance is shown to outperform an existing iterative ML-based algorithm with a comparable complexity. Another proposed algorithm uses the concept of the alternative projection algorithm, which reduces a multi variable search problem to a single variable search problem. The proposed algorithm does not require the simultaneous search of all times and frequencies. Therefore, the computation complexity is reduced tremendously. The scheme is robust in the sense that it can avoid the unbalanced sampling block problem that occurs in existing maximum likelihood-based schemes. The unbalanced sampling block problem would cause 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 third proposed algorithm presents a blind scheme for estimating frequency and transition time in an iterative fashion: the iterative disassemble and assemble (IDNA) algorithm. The algorithm is developed on the basis of the “divide and conquer” approach. The proposed scheme disassembles a high order polynomial into several monomial functions. The solutions for the monomial functions are calculated iteratively, and are then assembled into a final estimation result. The proposed scheme does not require initial random guesses as with common iterative algorithms. Improper initial guesses may suffer from the problem of convergence to the local maximum. The proposed approach can converge to the global maximum in order to achieve the solution. The proposed scheme offers a much lower computational complexity than that of the maximum likelihood greedy search algorithm. Moreover, it also outperforms existing schemes with a comparable complexity. Moreover, an approximation version of the proposed scheme is derived and can be used to further reduce the computational complexity.