top of page

Convolutional polar codes: major performance gain for 5G telecommunications



At the dawn of the deployment of "5G" technology in cellular digital telecommunications networks on a planetary scale, the industry is looking for methods to optimize the transmission of information in a "noisy channel" in order to reduce errors. to a minimum and save costs.

The transmission of information, without errors, in a noisy communication channel has been possible since the 1940s. The solution was to send a greater number of bits than the bits of information so that these become redundant and resist the errors. A formula therefore dictates the optimal performance depending on the rate of errors introduced into the channel, which is called the capacity of the channel. However, these solutions include several drawbacks, the main one being the excessive amount of calculations when decoding the information after it has been transmitted in the channel. Recently, the family of polar codes, with a yield arbitrarily close to the capacity of the channel, made it possible to counter this problem with a decoding time proportional to the quantity of information to be transmitted. The disadvantage of these codes is that they are necessary in very large size to reach the probability of error 0, or even that at small size, the probability of errors remains high. The present invention solves these problems.


Resulting from physics research at the University of Sherbrooke in Canada, the invention of convolutional polar codes is a game-changer. The principle of successive decoding is the same as for conventional polar codes, but the algorithm and the communication protocol carrying out this calculation are new. The most important innovation is the use of tensor networks in error correction. Tensor networks are a recently introduced tool in quantum mechanics to perform certain calculations. We translate the problem of decoding into this language, and thus arrive at an understanding of the efficient decoding of polar codes. It gives us a new perspective on certain problems and allows us to perform calculations that would be very complex without this language.

Convolutional polar code technology is a family of correcting codes that generalize polar codes. They make it possible to reach an error probability of 0 twice as quickly as polar codes and, when the quantity of information is of modest size, arrive at substantially lower error rates than polar codes.

A program in C++ and Julia has been designed and allows the encoding of information in a convolutional polar code, the decoding of information in a noisy channel, and the simulation of the noisy channel. Polar codes are already part of 5G international standards, and the study of integrating these codes into ASIC integrated circuits is planned.



  • A technology offering a major performance gain for 5G in telecommunications.

  • Standardization: polar codes, an error correction technology essential to the standardization of 5G communications, were adopted in 2016 by the "enhanced mobile broadband" (eMBB) standard which manages 5G. The present innovation enables an improved 5G!

  • Error suppression rate: higher than polar codes – more reliable communication or use of smaller codes.

  • Advantage vs. competition: more efficient than polar codes – allows you to communicate more reliably or even use smaller codes.

    • A major company in the industry confirms having programmed the code and observed the performance described.

  • Speed: similar decoding time as with polar codes.

  • Extensive applications: the decoding technique can even be applied to channels with memory.

  • Channel capacity: asymptomatic impairment of channel capacity.

  • Market: In April 2019, the “Global Mobile Suppliers Association” identified 224 operators in 88 countries that are actively investing in 5G.

    • Beginning in 2019, the rollout of 5G will expand to hundreds of millions of wireless devices.

    • The jump from 4G to 5G is much bigger than previous jumps, as 5G has the potential to be up to 100 times faster. This will allow in the future, among other things, autonomous cars.


  • Reliability: Substantially lower error rates than with polar codes.

  • Numerical proof that convolutional codes*:

    • Reach channel capacity asymptotically.

    • Are decodable in linear time.

    • The probability of errors tends to 0 twice as quickly as for polar codes.

  • Efficiency: less redundancy for the same content. For similar redundancy, probability of errors decreased.

  • Development of dedicated chipsets: encoders and decoders for polar codes are already implemented at scale in VLSI-Chips and work is planned for convolutional polar codes.


  • Telecommunications

  • Information storage

  • Decoding polar codes on channels with memory

  • Satellite communications



  • TRL5 – Technology proven in simulation and integration in VLSI-Chip is planned.

  • Licenses available.


  • Filing of a provisional US application on March 3, 2017: 62/466,414.

  • Filing of a PCT application on March 5, 2018: PCT/CA2018/050259.


  • Commercial partner

  • development partner

  • Looking for investments

Project Director: François Nadeau

bottom of page