VP8-05 A Study on Applying GA for Performance Improvement of SP Decoder in SMR
Creators: Madoka Nishikawa
Yasuaki Nakamura
Yasushi Kanai
and Yoshihiro Okamoto
Publisher: name: IEEE
Year: 2025

We previously investigated low-density parity-check (LDPC) coding and iterative decoding methods for shingled magnetic recording (SMR) in hard disk drives (HDDs). In sum-product (SP) decoding, we evaluate the log-likelihood (LLR) for check nodes and external information using parity check information [1], and realize that performance could be improved by multiplying the LLR by a weight based on the results. Furthermore, we explore the weights by applying the genetic algorithm (GA) to improve the decoding performance. We use the same read/write system except for the SP decoder shown in [2] in the study and assume an areal recording density of 4 Tbits/inch2. We use the LLR related to message node calculation in the SP decoding [2]. The neural network inputs the LLRs for the target and its adjacent bits to consider the influence of noise depending on the recording pattern. Also, the reliable LLR chosen based on the result of the neural network is updated to realize effective iterative decoding. In the study, we explore the weight based on the parity check information “0”, “1”, “2”, and “3” [1] to be multiplied by the LLR for the check nodes and external information by applying the GA shown in Figure 1. Here, the fitness of the lth individual in the δth generation is defined by fδl = –log10BERSP [2]. BERSP (bit error rate) is calculated by comparing the recorded sequence with the hard decision sequence after turbo equalization. We arrange the weights in one dimension as one chromosome and perform the selection, crossover, and mutation [3]. However, the individuals with the highest fitness are left by applying the elitist preserving selection [3]. Figure 2 shows the BER performance for SNRS (signal-to-noise ratio), where xh = 0.4, Nt = 15, isp= 6, and iglobal = 23 [2]. When we do not apply GA to explore the weights, we refer to the cases with and without the neural network in [2] as conventional methods 1 and 2, respectively. We clarified by Fig. 2 that the proposed method achieved error-free at SNRS = 21.0 dB, and improved the required SNR for error-free decoding by 0.5 and 2.5 dB compared with conventional methods 1 and 2, respectively.References: [1] M. Nishikawa, Y. Nakamura, H. Osawa, Y. Okamoto, Y. Kanai, and H. Muraoka, “A study on iterative decoding with LLR modulator by parity check information in SMR system,” IEEE Trans. Magn., vol.54, no.11, 3001304, Nov. 2018. [2] M. Nishikawa, Y. Nakamura, Y. Kanai, and Y. Okamoto, “Improvement of SP decoding considering the influence of recording patterns by neural network in SMR,” IEEE Trans. Magn., 2024 (to be published). [3] David E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Boston, pp.59－87.
We previously investigated low-density parity-check (LDPC) coding and iterative decoding methods for shingled magnetic recording (SMR) in hard disk drives (HDDs). In sum-product (SP) decoding, we evaluate the log-likelihood (LLR) for check nodes and external information using parity check information [1], and realize that performance could be improved by multiplying the LLR by a weight based on the results. Furthermore, we explore the weights by applying the genetic algorithm (GA) to improve the decoding performance. We use the same read/write system except for the SP decoder shown in [2] in the study and assume an areal recording density of 4 Tbits/inch2. We use the LLR related to message node calculation in the SP decoding [2]. The neural network inputs the LLRs for the target and its adjacent bits to consider the influence of noise depending on the recording pattern. Also, the reliable LLR chosen based on the result of the neural network is updated to realize effective iterative decoding. In the study, we explore the weight based on the parity check information “0”, “1”, “2”, and “3” [1] to be multiplied by the LLR for the check nodes and external information by applying the GA shown in Figure 1. Here, the fitness of the lth individual in the δth generation is defined by fδl = –log10BERSP [2]. BERSP (bit error rate) is calculated by comparing the recorded sequence with the hard decision sequence after turbo equalization. We arrange the weights in one dimension as one chromosome and perform the selection, crossover, and mutation [3]. However, the individuals with the highest fitness are left by applying the elitist preserving selection [3]. Figure 2 shows the BER performance for SNRS (signal-to-noise ratio), where xh = 0.4, Nt = 15, isp= 6, and iglobal = 23 [2]. When we do not apply GA to explore the weights, we refer to the cases with and without the neural network in [2] as conventional methods 1 and 2, respectively. We clarified by Fig. 2 that the proposed method achieved error-free at SNRS = 21.0 dB, and improved the required SNR for error-free decoding by 0.5 and 2.5 dB compared with conventional methods 1 and 2, respectively.References: [1] M. Nishikawa, Y. Nakamura, H. Osawa, Y. Okamoto, Y. Kanai, and H. Muraoka, “A study on iterative decoding with LLR modulator by parity check information in SMR system,” IEEE Trans. Magn., vol.54, no.11, 3001304, Nov. 2018. [2] M. Nishikawa, Y. Nakamura, Y. Kanai, and Y. Okamoto, “Improvement of SP decoding considering the influence of recording patterns by neural network in SMR,” IEEE Trans. Magn., 2024 (to be published). [3] David E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Boston, pp.59－87.