Abstract
Max-Log-MAP (MLMAP) algorithm is a sub-optimal turbo decoding algorithm. There are two distortions which result in the sub-optimal performance of MLMAP algorithm. They are the optimistic effect of reliability values and connection between the intrinsic and extrinsic information. Of the two distortions, the decoding performance is primarily affected due to over-optimistic effect but slightly due to the correlation effect. This paper focusses on reducing the overestimation of reliability values, which depends on SNR. An improved method to enhance the error-correcting performance of MLMAP turbo decoding algorithm is presented. The proposed Max-Log-MAP with Double Optimized Correction Factor (DOCF-MLMAP) turbo decoding algorithm, overcomes the over-optimistic estimation of reliability values that degrade the performance of MLMAP algorithm utilizing a correction factor. A pair of appropriate correction factors (CF) scales the extrinsic information exchanged in every iteration, between the constituent decoders. The selection of correction factors is dependent on Signal to Noise Ratio (SNR). The CFs of both inner and outer decoders are optimized to a lowest Bit Error Rate (BER) for improved performance. From the BER results, it was observed that DOCF-MLMAP is better in performance than MLMAP. DOCF-MLMAP algorithm reaches a BER as low as 1 × 10–6 at 12 dB in AWGN channel. The proposed DOCF-MLMAP algorithm proves to be superior in performance to the former algorithms in fading channel as well. The algorithms were also analyzed for various CODEC parameters.
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References
Aarthi V, Dhulipala VRS (2020) Improved SOVA decoding for UEP wireless transmission of JPEG 2000 images over MIMO-OFDM systems. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02141-5
Aarthi V, Dhulipala VS, Pradeep NS (2019) Reduction Factor Approach to Improve the Performance of Soft Output Viterbi Algorithm in AWGN Channel. In: 2019 IEEE International WIE Conference on Electrical and Computer Engineering, pp 1–4
Aarthi V, Dhulipala VS, Rangababu P (2020) Attenuation Factor approach to minimize the correlation effect in Soft Output Viterbi Algorithm. Phys Commun 39:101021
Bayrakdar ME (2020) Cooperative communication based access technique for sensor networks. Int J Electron 107(2):212–225
Bayrakdar ME, Atmaca S, Karahan A (2013) A slotted ALOHA-Based random access cognitive radio network with capture effect in Rayleigh fading channels. In: 2013 International Conference on Electronics, Computer and Computation, pp 72–75
Bayrakdar ME, Atmaca S, Karahan A (2016) A slotted ALOHA-based cognitive radio network under capture effect in Rayleigh fading channels. Turk J Electr Eng Comput Sci 24(3):1955–1966
Benedetto S, Montorsi G (1996) Iterative decoding of serially concatenated convolutional codes. Electron Lett 32(13):1186–1188
Berrou C, Glavieux A (2003) Wiley Encyclopedia of Telecommunications. https://doi.org/10.1002/0471219282.eot346
Chaikalis C, Liolios C, Vlachos V (2014) Implementation of an improved reconfigurable SOVA/log-MAP turbo decoder in LTE. In: 2014 22nd Telecommunications Forum Telfor, pp 218–221
El Gamal H, Hammons AR (2001) Analyzing the turbo decoder using the Gaussian approximation. IEEE Trans Inf Theory 47(2):671–686
Fowdur TP, Beeharry Y, Soyjaudah KS (2016) A novel scaling and early stopping mechanism for LTE turbo code based on regression analysis. Ann Telecommun 71(7–8):369–388
Gnanasekaran T, Aarthi V, Pradeep NS (2012) Performance enhancement of modified turbo decoding algorithms for mobile WiMAX. Aust J Electr Electron Eng 9(2):153–163
Huang Q, Xiao Q, Quan L, Wang Z, Wang S (2016) Trimming soft-input soft-output viterbi algorithms. IEEE Trans Commun 64(7):2952–2960
Iscan O, Lentner D, Xu W (2016) A comparison of channel coding schemes for 5G short message transmission. In: 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1–6
Lin CH, Guo SW, Ou LA (2018) Analysis and power evaluation of window-stopped parallel turbo decoding for LTE rate matching. IET Commun 12(9):1148–1154
Morgado A, Huq KS, Mumtaz S, Rodriguez J (2018) A survey of 5G technologies: regulatory, standardization and industrial perspectives. Digital Commun Netw 4(2):87–97
Pei R, Wang Z, Huang Q, Wang J (2017) Low complexity SOVA for Turbo codes. China Commun 14(8):33–40
Sadjadpour HR, Sloane NJ, Salehi M, Nebe G (2001) Interleaver design for turbo codes. IEEE J Sel Areas Commun 19(5):831–837
Sakthivel S, Pradeep NS (2018) reciprocal data map coding scheme for image transmission in MIMO-OFDM systems. Wireless Pers Commun 103(4):3145–3161
Schaich F, Sayrac B, Elayoubi S, Belikaidis IP, Caretti M, Georgakopoulos A, Wunder G (2016) FANTASTIC-5G: flexible air interface for scalable service delivery within wireless communication networks of the 5th generation. Trans Emerg Telecommun Technol 27(9):1216–1224
Sklar B (1997) A primer on turbo code concepts. IEEE Commun Mag 35(12):94–102
Vogt J, Finger A (2000) Improving the max-log-MAP turbo decoder. Electron Lett 36(23):1937–1939
Xiang L, Brejza MF, Maunder RG, Al-Hashimi BM, Hanzo L (2019) Arbitrarily parallel turbo decoding for ultra-reliable low latency communication in 3GPP LTE. IEEE J Sel Areas Commun 37(4):826–838
Zhan M, Wu J, Zhang ZZ, Wen H, Wu JJ (2015) Low-complexity error correction for ISO/IEC/IEEE 21451–5 sensor and actuator networks. IEEE Sens J 15(5):2622–2630
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Pradeep, N.S., Aarthi, V. & Dhulipala, V.R.S. Upgraded Max-Log-MAP algorithm using adaptive correction factors for decoders in AWGN and fading channel. J Ambient Intell Human Comput 13, 3245–3255 (2022). https://doi.org/10.1007/s12652-021-03160-6
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DOI: https://doi.org/10.1007/s12652-021-03160-6