Irregular Vector Turbo Codes with Low Complexity

The term Block Turbo Code typically refers to the iterative decoding of a serially concatenated two-dimensional systematic block code. This paper introduces a Vector Turbo Code that is irregular but with code rates comparable to those of a Block Turbo Code (BTC) when the Bahl Cocke Jelinek Raviv (BCJR) algorithm is used. In Block Turbo Codes, the horizontal (or vertical) blocks are encoded first and the vertical (or horizontal) blocks second. The irregular Vector Turbo Code (iVTC) uses information bits that participate in varying numbers of trellis sections, which are organized into blocks that are encoded horizontally (or vertical) without vertical (or horizontal) encoding. The decoding requires only one soft-input soft-output (SISO) decoder. In general, a reduction in complexity, in comparison to a Block Turbo Code was achieved for the same very low probability of bit error (10−5 ). Performance in the AWGN channel shows that iVTC is capable of achieving a significant coding gain of 1.28 dB for a 64QAM modulation scheme, at a bit error rate (BER) of 10−5over its corresponding Block Turbo Code. Simulation results also show that some of these codes perform within 0.49 dB of capacity for binary transmission over an AWGN channel.

Introducing perforation when applying turbo codes

The problem of adapting the degree of redundancy introduced in the process of error-correcting coding to the changing characteristics of the data transmission channel is urgent. Turbo codes, used in a variety of digital communication systems, are capable of correcting multiple errors occurring in the data transmission channel. The article compares the decoding efficiency for various options for introducing perforation into the code sequence generated by the turbo code encoder. Based on the comparison results, recommendations were made on the most appropriate option for the introduction of perforation.

Combining serial and parallel decoding for turbo codes

Reducing the decoding latency of the turbo codes is important to real-time applications. Conventionally, the decoding of the turbo codes (TC) runs in serial fashion, which means only one of the constituent soft decoders runs at a time. Parallel decoding (PD) refers to running the soft decoders in parallel. Although it delivers the output faster (compared to the serial decoding (SD)), it affects the bit- and frame-error rates. This paper proposes a decoding procedure that combines both PD and SD. It bridges the two decoding modes to determine the best combination scheme to achieve the required level of performance at an acceptable decoding latency. Presented results show how this procedure can mitigate the performance degradation at a slight increase in the decoding latency.

A Parallel Turbo Decoder Based on Recurrent Neural Network

A neural network-based decoder, based on a long short-term memory (LSTM) network, is proposed to solve the problem of high decoding delay caused by the poor parallelism of existing decoding algorithms for turbo codes. The powerful parallel computing and feature learning ability of neural networks can reduce the decoding delay of turbo codes and bit error rates simultaneously. The proposed decoder refers to a unique component coding concept of turbo codes. First, each component decoder is designed based on an LSTM network. Next, each layer of the component decoder is trained, and the trained weights are loaded into the turbo code decoding neural network as initialization parameters. Then, the turbo code decoding network is trained end-to-end. Finally, a complete turbo decoder is realized. Simulation results show that the performance of the proposed decoder is improved by 0.5–1.5 dB compared with the traditional serial decoding algorithm in Gaussian white noise and t-distribution noise. Furthermore, the results demonstrate that the proposed decoder can be used in communication systems with various turbo codes and that it solves the problem of high delay in serial iterative decoding.

Comparison between Different Channel Coding Techniques for IEEE 802.11be within Factory Automation Scenarios

This paper presents improvements in the physical layer reliability of the IEEE 802.11be standard. Most wireless system proposals do not fulfill the stringent requirements of Factory Automation use cases. The harsh propagation features of industrial environments usually require time retransmission techniques to guarantee link reliability. At the same time, retransmissions compromise latency. IEEE 802.11be, the upcoming WLAN standard, is being considered for Factory Automation (FA) communications. 802.11be addresses specifically latency and reliability difficulties, typical in the previous 802.11 standards. This paper evaluates different channel coding techniques potentially applicable in IEEE 802.11be. The methods suggested here are the following: WLAN LDPC, WLAN Convolutional Codes (CC), New Radio (NR) Polar, and Long Term Evolution (LTE)-based Turbo Codes. The tests consider an IEEE 802.11be prototype under the Additive White Gaussian Noise (AWGN) channel and industrial channel models. The results suggest that the best performing codes in factory automation cases are the WLAN LDPCs and New Radio Polar Codes.

An Efficient Turbo Decoding and Frequency Domain Turbo Equalization for LTE Based Narrowband Internet of Things (NB-IoT) Systems

This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement. Therefore, the existing LTE turbo codes are not recommended in NB-IoT systems and, hence, new channel coding algorithms need to be employed for LPWA specifications. First, LTE-based turbo decoding and frequency-domain turbo equalization algorithms are proposed, modifying the simplified maximum a posteriori probability (MAP) decoder and minimum mean square error (MMSE) Turbo equalization algorithms were appended to different Narrowband Physical Uplink Shared Channel (NPUSCH) subcarriers for interference cancellation. These proposed methods aim to minimize the complexity of realizing the traditional MAP turbo decoder and MMSE estimators in the newly NB-IoT PHY layer features. We compare the system performance in terms of block error rate (BLER) and computational complexity.