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A Blind Roll-off Factor Estimation Method for Wireless Signal Identification

Published: 29 August 2023 Publication History
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  • Abstract

    In blind signal detection, exact roll-off factor estimation needs to be performed for matched filtering, but the performance of the present methods is far from satisfactory. Considering this, this paper proposes a new method for the roll-off factor estimation, and the root mean square error (RMSE)criterion is employed to measure its performance advantage compared with the previous estimation methods. To be more specific, the envelope of the impulse response of the raised cosine-roll-off filter is derived with the help of the inverse Fourier transforms, and the roll-off factor is estimated by the ratio of the minimum to the maximum of the envelope. Simulation results show that the RMSE performance of the proposed method can be improved by 78% compared with that of the existing method when the SNR is 15dB. Moreover, the RMSE performance can be found to decrease dramatically with the growth of the roll-off factor when the factor is larger than 0.6.

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    1. A Blind Roll-off Factor Estimation Method for Wireless Signal Identification

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      ITCC '23: Proceedings of the 2023 5th International Conference on Information Technology and Computer Communications
      June 2023
      124 pages
      ISBN:9798400700583
      DOI:10.1145/3606843
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 29 August 2023

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      Author Tags

      1. blind signal detection
      2. roll-off factor estimation
      3. root mean square error
      4. spectrum averaging

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