The article proposes a method for adapting interleaving/deinterleaving devices in wireless data transmission systems with LDPC codes under conditions of a priori uncertainty by changing the type of interleaving/deinterleaving device to increase the reliability of information transmission. The method is based on calculating the value of the normalized Log-Likelihood Ratio (LLR) and based on it, the type of interleaving/deinterleaving device is selected. This method allows to increase the reliability of information transmission.
life.
Namely:
The rapid development of wireless technologies (Wi-Fi,
WiMAX) and mobile communication technologies (5G) made it possible to implement them in various spheres of 1. in healthcare: development of patient monitoring systems [1], telemedicine, etc.; 2. agriculture: development of resource optimization and control systems for harvesting and increasing yields and assessing efficiency [2]; monitoring systems [3]; 3. industry: development of production line control systems and real-time equipment 4. logistics and transport: development of autonomous vehicle control systems [4]; 5. Internet of Things (IoT) [5, 6]; 6. smart cities: energy consumption monitoring and energy saving systems, security systems [7]; 7. virtual and augmented reality [8]; 8. telemetry, data collection and storage systems [9, 10]. __________________________
Given the high level of technology adoption in our present day and the need to ensure and improve the quality of data transmission, there is a need to use LDPC codes [11, 12], which are used in the 5G standard [13, 14]. Also, they were adopted by WiMax [16], Wi-Fi [15-17] and DVB-S2 [18] technologies. LDPC codes were chosen due to their lower complexity compared to turbo codes (TC) and high efficiency at high coding rates compared to TC ( R = 3/4, 5/6, etc.).
Also, to increase the reliability of information transmission, it is advisable to use an interleaving device. It performs a permutation of bits of an information sequence according to a certain principle and its main function is to reduce the number of group errors by distributing them over time. Interleaving devices have found significant implementation in LDPC codes [19-21] and TC [22-24].
A sufficient number of various types of interleaving devices have been developed, which may differ in the principle and algorithm of interleaving. Fig. 1 presents the classification of interleaving devices according to the principle (algorithm) of permutation of bits in the information sequence. In regular interleaving devices, the permutation of bits in the information sequence is performed analytically. That is, the block size or a mathematical formula can be used to permute the bits. They are less complex to implement than pseudo-random interleaving devices, but also have lower efficiency [25, 26].
Pseudo-random interleaving devices permutate bits in an information sequence according to a pseudo-random principle. The main difficulty in implementing this type of interleaver is that for each data block it is necessary to store an interleaving table, which is required for the deinterleaving operation. This type of interleaver is more efficient than regular interleavers and performs more bit spacing within a block. The S-random interleaving device is one of the most efficient due to the variable bit spacing parameter in the information sequence.
Given their efficiency and properties, it is advisable to use them to increase the reliability of information transmission in wireless data transmission systems.
In [20], a method using LDPC codes and interleavers was developed. The method uses different types of interleavers, with an initial selection of a specific type of interleaver without further adaptation, which is a disadvantage due to the inexpedient use of computing resources.
In [27], the use of LDPC codes and interleaving devices is considered. The use of interleaving devices and the min-sum decoding algorithm improves performance and reaches the efficiency level of the sum-product algorithm (SPA) decoding algorithm. The main disadvantage is the choice of a specific type of interleaving device without further modification or adaptation, which can cause increased use of computational resources and an unreasonable increase in computational complexity for LDPC code decoding methods. We see that interleaving devices are used without further adaptation or possible change of the type of interleaving device, which can lead to inefficient use of computing resources and increased time for data decoding.
The purpose of the article is to develop a method of adapting interleaving/deinterleaving devices in wireless data transmission systems with LDPC codes in conditions of a priori uncertainty by changing the type of interleaving/deinterleaving device to increase the reliability of information transmission.
The encoder input receives an information sequence X and transmits a generator matrix G, which is created based on the parity-check matrix H. Using the generating matrix G, the information block is encoded and the output is the encoded data block C. An encoded sequence is transmitted to the input of the interleaving device, where bits are permuted depending on the type of interleaving device. And from the output of the interleaving device we obtain an interleaved coded CП block. Next, the block is transmitted to the modulator and then passes through the channel, where further demodulation, displacement, and decoding are then performed. At the output, the decoding is estimated by calculating the normalized LLR , where . Depending on the value the type of interleaving/deinterleaving device is selected.
Fig. 3 shows a structural scheme of a modified LDPC code decoder for iteration I, including the deinterleaving device and the control unit for changing the type of deinterleaving device.
The input of the deinterleaving device receives a coded sequence from the channel, where, depending on the type of deinterleaving device, bits are rearranged to their initial positions. We obtain a vector of a priori values LLR . For further decoding process, a vector of a priori values of the LLR
is transmitted to the decoder input and parity-check matrix H.
As a result of the decoding process, a vector of a posteriori values of the LLR is obtained at the output. Vectors of a priori/posteriorі values of LLR / are sent to the calculation block of the normalized LLR
Algorithm for implementing the method of adaptive selection of the type of interleaving/deinterleaving device: 1) Formation of the input information sequence: where
, U – the number of bits in the input information sequence. 2) From the channel output, we form the a priori values of the LLR : 4) We calculate the number of sign changes : 5) We calculate the normalized value
: 3) From the decoder output we obtain the posteriori values of the LLR : where U – the number of bits in the input information sequence. 6) Based on normalized value , we select the type of interleaving device:
, , (1) (2) (3) (4) (5)
A simulation model was developed in the QT Creator 6.7, with the help of which the results of the method were evaluated. Decoding algorithm – Sum-product algorithm.
Fig. 4 shows the result of the developed method in comparison with the standard algorithm. The bit error probability was estimated from the signal-to-noise ratio .
The Consultative Committee for Space Data Systems (CCSDS) standard parity-check matrix was used, U = 128 bits, encoding rate R = 1/2. 6. Conclusions
1. The article proposes a method for adapting interleaving/deinterleaving devices in wireless data transmission systems with LDPC codes under conditions of a priori uncertainty by changing the type of interleaving/deinterleaving device to increase the reliability of information transmission.
2. Using the developed method allows obtaining an energy gain of 0.1 dB for the CCSDS standard code U = 128 bits and coding rate R = 1/2.
3. Further research is planned on the topic of multiparameter adaptation of wireless data transmission systems, including interleavers.
The author(s) have not employed any Generative AI tools.
[1] Darwish, A. Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring / A. Darwish, A.E. Hassanien, // Sensors – 2011, – P. 5561–5595. https://doi.org/10.3390/s110605561 [2] Jawad, H.M. Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review / H.M. Jawad, R. Nordin, S.K. Gharghan, A.M. Jawad, M. Ismail // Sensors – 2017, 17, 1781. https://doi.org/10.3390/s17081781 [3] Pandikumar, S. Upgrading Industrial Automation With 5G and IoT / , S. Pandikumar, K V.
Shaheena, T. Dinesh // QTanalytics Publication (Books) – 2024, P. 57-77. https://doi.org/10.48001/978-81-980647-5-2-5 [4] Nzeyimana E. T. The Impact of 5G Technology on Autonomous Vehicles / E. T. Nzeyimana // Research invention journal of biological and applied sciences – 2024, 3(2): P. 36-39. [5] Zaitsev, S.V., Sokorinskaya, N.V., Vasylenko, V.M. et al. Optimization of Turbo Code Encoding/Decoding Processes for Development of 5G Mobile Communication Systems. Radioelectron.Commun.Syst. 64, 440–450 (2021). https://doi.org/10.3103/S0735272721080045 [6] V. Vladyslav, K. Volodymyr, Z. Sergei and U. Anna, "Adaptive turbo codes for safety in wireless Internet of Things," 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), Kyiv, UKraine, 2018, pp. 188-193, doi: 10.1109/DESSERT.2018.8409125. [7] M. J. Shehab, I. Kassem, A. A. Kutty, M. Kucukvar, N. Onat and T. Khattab, "5G Networks Towards Smart and Sustainable Cities: A Review of Recent Developments, Applications and Future Perspectives," in IEEE Access, vol. 10, pp. 2987-3006, 2022, doi: 10.1109/ACCESS.2021.3139436 [8] P. Pramod Kumar, Ravikumar Thallapalli, R. Akshay, K. Sridhar Sai, K. Srikar Sai, G. Sai Srujan; State-of-the-art: Implementation of augmented reality and virtual reality with the integration of 5G in the classroom. AIP Conf. Proc. 24 May 2022; 2418 (1): 020069. https://doi.org/10.1063/5.0081774 [9] Chuai, Xy., Shen, Js., Chen, Md. et al. The Improvement and Application of a Wireless Real-Time Telemetry Seismic System. Appl. Geophys. 22, 291–304 (2025). https://doi.org/10.1007/s11770-025-1190-3 [10] Chochliouros, I.P. et al. (2019). Inclusion of Telemetry and Data Analytics in the Context of the 5G ESSENCE Architectural Approach. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2019. IFIP Advances in Information and Communication Technology, vol 560. Springer, Cham. https://doi.org/10.1007/978-3-030-19909-8_4 [11] Gallager R. Low-Density Parity-Check Codes / Gallager R. // IEEE Transactions on
Information Theory. – 1962. – Vol. 8, N 1. – P. 21 – 28. [12] Gallager R. Low-Density Parity-Check Codes / Gallager R. – M.I.T. Press, Cambridge,
MA, 1963. – 90 p. [13] Mejmaa, B.; Marktani, M.A.; Akharraz, I.; Ahaitouf, A. An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA. Computers 2024, 13, 195. https://doi.org/10.3390/computers13080195 [14] F. Li et al., "Review on 5G NR LDPC Code: Recommendations for DTTB System," in IEEE
Access, vol. 9, pp. 155413-155424, 2021, doi: 10.1109/ACCESS.2021.3121587 [15] Wu, Y.; Wu, B.; Zhou, X. High-Performance QC-LDPC Code Co-Processing Approach and VLSI Architecture for Wi-Fi 6. Electronics 2023, 12, 1210. https://doi.org/10.3390/electronics12051210 [16] S. H. Gupta and B. Virmani, "LDPC for Wi-Fi and WiMAX technologies," 2009 International Conference on Emerging Trends in Electronic and Photonic Devices & Systems, Varanasi, India, 2009, pp. 262-265, doi: 10.1109/ELECTRO.2009.5441120 [17] Y. Wu and B. Wu, "A Low-Latency Dual-Path QC-LDPC Decoder for IEEE 802.11ax," in IEEE Access, doi: 10.1109/ACCESS.2023.3247958 [18] M. K. Yadav and K. K. Parhi, "Design and Implementation of LDPC Codes for DVBS2," Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005., Pacific Grove, CA, USA, 2005, pp. 723-728, doi: 10.1109/ACSSC.2005.1599847 [19] Nowak S. An interleaving scheme for efficient binary LDPC coded higher-order modulation / S. Nowak and R. Kays // 2010 International ITG Conference on Source and Channel Coding (SCC). – 2010. Siegen, Germany, pp. 1-6. [20] Baldi M. Interleaved Product LDPC Codes / M. Baldi, G. Cancellieri and F. Chiaraluce //
IEEE Transactions on Communications. – 2012. vol. 60, no. 4, pp. 895-901. [21] Zhang, L. Joint optimizing of interleaving and LDPC decoding for burst errors in PON systems / L. Zhang, C. Yang, F. Zhang // Science China Information Science. – 2020.
Vol. 63, Article Number. 129302, pp. 1-3. [22] Sadjadpour, H. Interleaver Design for Turbo Codes / H. Sadjadpour, N. Sloane, M. Salehi,
G. Nebe // Selected Areas in Communications, IEEE Journal on. 19. – 2001 – P. 831 - 837. [23] Sun J. Interleavers for turbo codes using permutation polynomials over integer rings / J.
Sun, O. Y. Takeshita // IEEE Transactions on Information Theory, 2005, vol. 51, no. 1, pp. 101-119. [24] Garzón-Bohórquez R. Protograph-Based Interleavers for Punctured Turbo Codes / R.
Garzón-Bohórquez, C. Abdel Nour and C. Douillard // IEEE Transactions on Communications, – 2018. vol. 66, no. 5, pp. 1833-1844. [25] Crozier S. New High-Spread High-Distance Interleavers for Turbo-Codes // Commun.
Research Centre. – Ottawa. – P. 2-5. [26] Barbulescu A., Pietrobon S. Turbo Codes: a tutorial on a new class of powerful error correcting coding schemes. Part I: Code Structures and Interleaver Design // University of South Australia, 1998. – October. – P. 15-20. [27] Pathak, P., Bhatia, R. Investigation of LDPC codes with interleaving for 5G wireless networks. Ann. Telecommun. (2024). https://doi.org/10.1007/s12243-024-01054-0