Design of Approximate Polar Maximum-Likelihood Decoder
Kadala Divyavani1, Mamatha Samson2, K.Swaraja3, Padmavati Kora4, Meenakshi.K5
1Kadala Divyavani*, ECE, GRIET, Hyderabad, India.
2Mamatha Samson, ECE, GRIET, Hyderabad, India.
3Swaraja. K, ECE, GRIET, Hyderabad, India.
4Padmavati Kora, ECE, GRIET, Hyderabad, India.
5Meenakshi. K, ECE, GRIET, Hyderabad, India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5086-5092 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3336129219/2019©BEIESP | DOI: 10.35940/ijeat.B3336.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Polar codes, presented by Arikan, accomplish the ability to acquire nearly error-less communication for any given noisy channel of symmetry with “low encoding and decoding complexities” on a huge set of fundamental channels. As of late, polar code turned into the best ideal error-correcting code from the perspective of information theory because of its quality of channel achieving capacity. Though the successive cancellation decoder with approximate computing is efficient, the proposed ML-based decoder is more efficient than the former. As it is equipped with the Modified Processing Element which shows the better performance with the properties of Median Filter. The proposed ML-based decoder diminishes the area and power consumed and logic utilization. In the present paper, effective polar decoder architecture is structured and executed on FPGA utilizing Vertex 5. Here we examine the proposed unique construction that is appropriate for decoding lengthy polar codes with less equipment multifaceted nature.
Keywords: SC Decoding, Approximate computing, IAOU, Media filter, ML decoding.