Abstract
Compressed sensing (CS) technologies have been widely adopted to pilot-assisted orthogonal frequency division multiplexing (OFDM) sparse channel estimation. However, only few works have focused on the location optimization of the pilot pattern. This paper investigates the pilot location optimization for the measurement matrix construction based on the minimum mutual coherence (MC) rule. We consider the design of the deterministic OFDM pilot pattern for solving a combinatorial optimization problem. The proposed approach utilizes the advantages of the Q-bit to update the location of the OFDM pilot pattern. The obtained results show that the proposed approach can form measurement matrix with a smaller MC and the estimated performance can be essentially improved compared with the standard genetic algorithms or random search method.
Highlights
The well-konwn orthogonal frequency division multiplexing (OFDM) theory provides a multicarrier modulation mechanism, which divides the valid spectrum into numerous parallel orthogonal narrow-band sub-channels [1]
The wireless channel will generate the same influence on each transmitted OFDM symbol, which reduces the complexity of channel estimation
The design of the deterministic OFDM pilot pattern can be summarized as an combinatorial problem
Summary
The well-konwn orthogonal frequency division multiplexing (OFDM) theory provides a multicarrier modulation mechanism, which divides the valid spectrum into numerous parallel orthogonal narrow-band sub-channels [1]. B. OUR CONTRIBUTION the QGA is an effective scheme to solve the optimization problem, only few works have focused on the deterministic location design for the OFDM pilot pattern using QGA. The results indicate that our proposed MQGA-based algorithm can design measurement matrix with lower MC and enhance the estimation accuracy by comparing with the standard genetic algorithms and the random method. 2) An MQGA-based optimization scheme is presented that combines the quantum computing and GA to search the optimum deterministic pilot pattern using the minimum MC of the measurement matrix. In this part, the MQGA is applied for solving the combinatorial problem to generate deterministic pilot pattern. The deterministic pilot pattern is critical for enhancing the performance and facilitating the system implementation
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