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Compressed sensing (CS)-based sparse channel estimation requires the sensing matrix with the minimum mutual coherence (MC), and its corresponding pilot pattern obtain optimal estimation performance. In order to minimize the MC of the sensing matrix, a deterministic optimized pilot pattern allocation scheme based on modified adaptive genetic algorithm (MAGA) is investigated in this paper. By adjusting the probability of mutation and crossover adaptively, the proposed scheme guides the search process to obtain the optimized pilot pattern. This method guarantees the convergence of the optimization process and prevents the process into local optimization to get the global optimization. Compared with the existing methods, simulation results prove that the proposed scheme obtain the sensing matrix with the smaller MC, whose corresponding deterministic pilot pattern effectively improve channel estimation performance.

This paper proposes a novel 3780-point FFT algorithm for the China national broadcasting standard. This new algorithm adopts the pure PFA (prime factor algorithm) and nested Winograd FFT which can realize the identity in the circuit structure. The simulation demonstrates that the new algorithm can achieve the equal accuracy rather than other methods but with the least quantity of multiplication.

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