Performance comparison of channel coding schemes for 5G massive machine type communications

Abstract

<p><span>Channel coding for the fifth generation (5G) mobile communication is currently facing new challenges as it needs to uphold diverse emerging applications and scenarios. Massive machine-type communication (mMTC) constitute one of the main usage scenarios in 5G systems, which promise to provide low data rate services to a large number of low power and low complexity devices. Research on efficient coding schemes for such use case is still ongoing and no decision has been made yet. Therefore, This paper compares the performance of different coding schemes, namely: tail-biting convolutional code (TBCC), low density parity check codes (LDPC), Turbo code and Polar codes, in order to select the appropriate channel coding technique for 5G-mMTC scenario. The considered codes are evaluated in terms of bit error rate (BER) and block error rate (BLER) for short information block lengths (K ≤ 256). We further investigate their Algorithmic complexity in terms of the number of basic operations. The Simulation results indicate that polar code with CRC-aided successive cancelation list decoder has better performance compared with other coding schemes for 5G-mMTC scenario.</span></p>

Single parity check node adapted to polar codes with dynamic frozen bit equivalent to binary linear block codes

“…Given that the computational complexity of the SC decoder is solely dependent on block length 𝑛, it is obvious that the complexity of the polar code with the SC decoding method is lower than that of other coding schemes. This is confirmed by [27]. The adaptation offered in this work of a derivative of the SC technique allows us to take advantage of its low complexity for the benefit of different classes of popular linear codes.…”

“…The polar code equivalent to the E-BCH code (64,24), illustrated in Figure 4(a), is defined so that the positions {8,12,16,24,28,30,31,32,40,44,46,47,48,52,54,55,56,58,59,60,61,62,63,64} are information bits, the positions {15, 20,22,23,26,27,29,36,38,42,43,45,50,51,53,57} are dynamic frozen bits, and the remainder positions are static frozen bits. In addition to the optimization proposed by the SSCL technique as described in [18], the special node proposed in this work allows even more to cut the decoding tree to the met of the eighth node at depth 3 and the eighth, twelfth and fourteenth nodes at depth 4, which are SPCs with dynamic frozen bits.…”

Efficient systematic turbo polar decoding based on optimized scaling factor and early termination mechanism

“…For systematic code, 𝑥 1,ℱ = 𝑢 and 𝑣 1,ℱ 𝑐 is set to zero vector. Using this assumption and from (2) and (3) one can calculate the parity bits as (4).…”

“…Recently, polar code (PC) [1] has attracted a lot of attention in the source and channel coding research field [2]. This is mainly due to its ability to achieve channel capacity over the binary discrete memoryless channel with minimal encoding and decoding complexity.…”

Enhancement of code division multiple access system performance using raptor codes

“…Channel coding can minimize multi-access interference (MAI) by decreasing the transmitted power at the expense of increasing the transmitted signal bandwidth through the redundant codes. Also, by employing efficient coding algorithms with error-correcting capabilities and high coding gain, system performance can be improved by minimizing the bit error rate [7], [8]. There are two types of channel codes: block codes and convolutional codes.…”