Developing programhardware interface for polar codes characteristics studying
DOI: 10.34759/trd202111607
Аuthors
^{1}^{*}, ^{1}^{**}, ^{2}^{***}1. National Research University of Electronic Technology, Bld. 1, Shokin Square, Zelenograd, Moscow, Russia, 124498
2. National Research University of Electronic Technology "MIET", 1, Shokin Square, Zelenograd, Moscow, 124498, Russia
*email: leshvol@mail.ru
**email: 79999924816@ya.ru
***email: makrotheeast@gmail.com
Abstract
Polar codes are the new generation of noise immune codes, employed in the standard of a new generation of mobile communication. Since these codes are not algebraic and, hence, do not have precisely specified characteristics due to the soft reception, all results shuld be obtained only through mathematical modelling, or by the testbench testing. which allows giving the code effectiveness evaluation in certain conditions without mathematical calculations.
This article describes the work on the test bench development according to the principles of rapid prototyping for performing fullscale modeling of various types of signalcode structures based on the polar noiseimmune codes being employed in the new generation communication systems. An overview of decoding techniques of polar codes being tested is presented, and mathematical foundation of these techniques is described to to a small extent.
Several development tools were used for the test bench development: Vivado, Matlab, and Verilog language. The hardware part of the test bench is the Diligent Zedboard PCB based on the Zinq7000 of the Xilinx Company.
The article describes the key points of the test bench creation. It demonstrates the evaluation technique for the signal coding effectiveness at the white noise impact.
Data on the effectiveness and hardware costs for various types of decoders were obtained.
The polar codes in the control channel and LDPC codes in the data transmission channel were selected as basic polar codes for the 5G Standard. The polar codes, initially proposed by Erikan in 2009, are the first type of the structural channel codes, for which maximum throughput capacity of a binary symmetric channel without memory was proved, i.e. the case of reaching the Shennon boundary while employing the soft decoder of sequential exclusion. This decoder is of low hardware complexity compared to the brute force methods such as Chase's algorithms.
The basic idea behind the polar coding is to convert pairs of identical binary input channels (each bit of the input word is called a channel) into two sundry types of channels of different quality, when one is better and the other is worse than the original one. This will allow using nearly ideal channels for transmitting data to the recipient of the message, with this, presetting the bits (e.g., zero) in noisy channels so that decoding relies on values known to the receiver. Noisy and noiseless channels are called frozen and unfrozen, respectively. Only unfrozen channels are transmitting data. Selection of channels partition technique and the set of frozen channels fully determines the polar code.
There is a problem of optimal dynamic allocation of timefrequency resources to subscribers. It takes on a special character with their high mobility. The 5G next generation communication systems imply extremely high users’ mobility, and frequent switching between the base stations. For timely power adjustment, devices need to transmit information quickly to the base stations, and this, in turn, requires application of short code structures in service channels. These are exactly the polar codes, which demonstrate the best noise immunity at the same length compared to the other types of codes.
The same problem is the case when data transferring between drones or in selforganizing networks, and it can be solved using the specified class of codes.
Since the discovery of polar codes, several algorithms have been proposed for decoding polar codes. The two most popular of these algorithms are sequential exclusion decoding, which was suggested in the original article, and list decoding. The first method consists in data bits decoding one by one, and each decoded data bit together with previoiusly decoded bits is being used for the next data bit decoding by means of the received signal. This results in extremely long time for all bits decoding. Thus, the polar decoding seems unsuitable for the real time applications. Let us consider the main stpes of polar codes decoding.
Keywords:
antinoise coding, polar coding, prototyping of communication systems, SoC, FPGA, field testsReferences

Arikan E. Channel polarization: A method for constructing capacityachieving codes for symmetric binaryinput memoryless channels, IEEE Transactions on information Theory, 2009, vol. 55, no. 7, pp. 3051  3073. DOI: 10.1109/TIT.2009.2021379

Kuznetsov V.S., Volkov A.S., Bykov A.V. Naukoemkie tekhnologii v kosmicheskikh issledovaniyakh Zemli, 2018, vol. 10, no. 5, pp. 46 – 55. DOI: 10.24411/24095419201810165

Eslami A., PishroNik H. A practical approach to polar codes, 2011 IEEE International Symposium on Information Theory Proceedings, Saint Petersburg, 2011, pp. 16  20. DOI: 10.1109/ISIT.2011.6033837

Varakin L.E. Sistemy svyazi s shumopodobnymi signalami (Communication systems with noiselike signals), Moscow, Radio i svyaz', 1985, 384 p.

Torrieri D. Principles of spreadspectrum communication systems, Springer, 2015, 641 p. DOI: 10.1007/9781441995957

Simon M.K., Omura J.K., Scholtz R.A., Levitt B.K. Spread Spectrum Communication Handbook, New York, McGrawHill, 1994, 1228 p.

Borodin V.V., Petrakov A.M., Shevtsov V.A. Trudy MAI, 2015, no. 81. URL: http://trudymai.ru/eng/published.php?ID=57894

Borodin V.V., Petrakov A.M., Shevtsov V.A. Elektrosvyaz', 2016, no. 11, pp. 41  45.

Shevtsov V.A., Borodin V.V., Krylov M.A. Trudy MAI, 2016, no. 85. URL: http://trudymai.ru/eng/published.php?ID=66417

Chen K., Niu K., Lin J.R. List successive cancellation decoding of polar codes, Electronics letters, 2012, vol. 48, no. 9, pp. 500  501. DOI:10.1049/EL.2011.3334

Zhang Y. et al. A modified belief propagation polar decoder, IEEE communications letters, 2014, vol. 18, no. 7, pp. 1091  1094. DOI: 10.1109/LCOMM.2014.2316365

Li B., Shen H., Tse D. An adaptive successive cancellation list decoder for polar codes with cyclic redundancy check, IEEE Communications Letters, 2012, vol. 16, no. 12, pp. 2044  2047.

Sklyar B. Tsifrovaya svyaz'. Teoreticheskie osnovy i prakticheskoe primenenie (Digital communication. Theoretical basics and practical application), Moscow, Vil'yams, 2003, 146 p.

Zhan G. et al. Highspeed FPGAbased phase measuring profilometry architecture // Optics express, 2017, vol. 25, no. 9, pp. 10553  10564. DOI: 10.1364/OE.25.010553

Kuznetsov V.S., Volkov A.S., Solodkov A.V., Coroka V.G. Trudy MAI, 2020, no. 111. URL: http://www.trudymai.ru/eng/published.php?ID=115131. DOI: 10.34759/trd20201119

Kuznetsov V.S., Volkov A.S., Solodkov A.V., Chugunov I.V. TComm: Telekommunikatsii i transport, 2020, vol. 14, no. 7, pp. 13  19.

Lyalin K.S., Khasanov M.S., Meleshin Yu.M., Kuz'min I.A. Trudy MAI, 2018, no. 103. URL: http://trudymai.ru/eng/published.php?ID=100800

Kaushal S., Kaur G. FPGA Implementation of MIMO System using Xilinx System for Video Transmission, IOSR Journal of Electronics and Communication Engineering, 2014, vol. 9, no. 3, pp. 41 – 44. DOI: 10.9790/283409324144

Shiro G.E., Kuznetsov M.S. Izvestiya vuzov. Elektronika, 2014, no. 6, pp. 67  72.

Pamuk A. An FPGA implementation architecture for decoding of polar codes, 8th International Symposium on Wireless Communication Systems, Aachen, 2011, pp. 437  441. DOI: 10.1109/ISWCS.2011.6125398

Crockett L.H. et al. The Zynq Book: Embedded Processing with the Arm CortexA9 on the Xilinx Zynq7000 All Programmable Soc, Strathclyde Academic Media, 2014, 484 p.
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