NEW CONCATENATION SCHEMES BASED ON THE MULTITHRESHOLD DECODERS OF CONVOLUTIONAL SELF-ORTHOGONAL CODES FOR GAUSSIAN CHANNELS

New serial concatenation schemes based on the multithreshold decoders and di- vergent principle for the convolutional self-orthogonal codes under Gaussian channels are proposed. Using both binary and symbolic decoders on the second decoding stage of the convolutional codes are considered. Simulation results are indicated the higher performance characteristics of the proposed cascade schemes on majority decoders in comparison with clas- sical schemes based on the Viterbi algorithm and Reed-Solomon codes. A moderate increase in decoding delay during concatenation is revealed. It is determined by the absence of the need to use traditional two-dimensional concatenated structures.

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.

Rate and Performance Enhancement of LDPC Codes Using Collection of Punctured Codes Decoding (CPCD)

Background: Low-density parity-check (LDPC) codes have received significant interest in a variety of communication systems due to their superior performance and reasonable decoding complexity. Methods: A novel collection of punctured codes decoding (CPCD) technique that considers a code as a collection of its punctured codes is proposed. Two forms of CPCD, serial CPCD that decodes each punctured code serially and parallel CPCD that decodes each punctured code in parallel, are discussed. Results: It is demonstrated that both serial and parallel CPCD have about the same decoding complexity compared with standard sum product algorithm (SPA) decoding. It is also demonstrated that while serial CPCD has about the same decoding delay compared with standard SPA decoding, parallel CPCD can decrease the decoding delay, however, at the expense of processing power. Conclusion: Numerical results demonstrate that CPCD can significantly improve the performance, or significantly increase the code rate of low-density parity-check (LDPC) codes.

Reinforcement Learning for Bit-Flipping Decoding of Polar Codes

A traditional successive cancellation (SC) decoding algorithm produces error propagation in the decoding process. In order to improve the SC decoding performance, it is important to solve the error propagation. In this paper, we propose a new algorithm combining reinforcement learning and SC flip (SCF) decoding of polar codes, which is called a Q-learning-assisted SCF (QLSCF) decoding algorithm. The proposed QLSCF decoding algorithm uses reinforcement learning technology to select candidate bits for the SC flipping decoding. We establish a reinforcement learning model for selecting candidate bits, and the agent selects candidate bits to decode the information sequence. In our scheme, the decoding delay caused by the metric ordering can be removed during the decoding process. Simulation results demonstrate that the decoding delay of the proposed algorithm is reduced compared with the SCF decoding algorithm, based on critical set without loss of performance.

Tradeoff between compression ratio and decoding delay of distributed source coding for uplink transmissions in machine-type communication

Rapid growth of machine-type communications devices challenges the future network with a significant aggregated data traffic. Distributed source coding is a promising technique that compresses data sources and decreases required aggregated data transmission rate. In this article, we discuss the merits and demerits of deploying distributed source coding in machine-type communications uplink transmissions. We analyze how the decoding delay and storage consumption scale with the number of users and prove that the average decoding delay grows linearly with the user number under some assumptions. A machine-type communications uplink transmission scheme adopting clustered distributed source coding is proposed to balance the compression ratio and decoding delay of distributed source coding where users are divided into independently encoded and decoded clusters. We evaluate three clustering algorithms, grid dividing, Weighted Pair Group Method with Arithmetic Mean, and K-medoids in our system model. The grid dividing algorithm clusters users based on their locations, while Weighted Pair Group Method with Arithmetic Mean and K-medoids cluster users using the correlation intensity between them. Our simulation results show that Weighted Pair Group Method with Arithmetic Mean and K-medoids outperform grid dividing on compression ratio and K-medoids and grid dividing have a more balanced delay distribution among different clusters than Weighted Pair Group Method with Arithmetic Mean.