Visible to the public Biblio

Filters: Keyword is Turbo code  [Clear All Filters]
2019-11-25
Abdulwahab, Walled Khalid, Abdulrahman Kadhim, Abdulkareem.  2018.  Comparative Study of Channel Coding Schemes for 5G. 2018 International Conference on Advanced Science and Engineering (ICOASE). :239–243.
In this paper we look into 5G requirements for channel coding and review candidate channel coding schemes for 5G. A comparative study is presented for possible channel coding candidates of 5G covering Convolutional, Turbo, Low Density Parity Check (LDPC), and Polar codes. It seems that polar code with Successive Cancellation List (SCL) decoding using small list length (such as 8) is a promising choice for short message lengths (≤128 bits) due to its error performance and relatively low complexity. Also adopting non-binary LDPC can provide good performance on the expense of increased complexity but with better spectral efficiency. Considering the implementation, polar code with decoding algorithms based on SCL required small area and low power consumption when compared to LDPC codes. For larger message lengths (≥256 bits) turbo code can provide better performance at low coding rates (\textbackslashtextless;1/2).
2017-02-14
A. Motamedi, M. Najafi, N. Erami.  2015.  "Parallel secure turbo code for security enhancement in physical layer". 2015 Signal Processing and Intelligent Systems Conference (SPIS). :179-184.

Turbo code has been one of the important subjects in coding theory since 1993. This code has low Bit Error Rate (BER) but decoding complexity and delay are big challenges. On the other hand, considering the complexity and delay of separate blocks for coding and encryption, if these processes are combined, the security and reliability of communication system are guaranteed. In this paper a secure decoding algorithm in parallel on General-Purpose Graphics Processing Units (GPGPU) is proposed. This is the first prototype of a fast and parallel Joint Channel-Security Coding (JCSC) system. Despite of encryption process, this algorithm maintains desired BER and increases decoding speed. We considered several techniques for parallelism: (1) distribute decoding load of a code word between multiple cores, (2) simultaneous decoding of several code words, (3) using protection techniques to prevent performance degradation. We also propose two kinds of optimizations to increase the decoding speed: (1) memory access improvement, (2) the use of new GPU properties such as concurrent kernel execution and advanced atomics to compensate buffering latency.