Polar Codes for Soft Decode-and-Forward in HalfDuplex Relay Channels

Soft decode-and-forward(DF) can combine the advantages of both amplify-and-forward and hard DF in relay channels. In this paper, we propose a low-complexity soft DF scheme based on polar codes, which features two key techniques: a low-complexity cyclic redundancy check(CRC) aided list successive cancellation(CALSC) decoder and a soft information calculation method. At the relay node, a low-complexity CALSC decoder is designed to reduce the computational complexity by adjusting the list size according to the reliabilities of decoded bits. Based on the path probability metric of the CALSC decoder, we propose a method to compute the soft information of the decoded bits in CALSC. Simulation results show that our proposed scheme outperforms the soft DF based on low-density parity-check codes and the soft DF with belief propagation or soft cancellation decoder, especially in the case when the source-relay channel is at the high signal-to-ratio region.

Soft Decoding Scheme of Convolution Code Combined with Huffman Coding

This paper proposes a modification of the soft output Viterbi decoding algorithm (SOVA) which combines convolution code with Huffman coding. The idea is to extract the bit probability information from the Huffman coding and use it to compute the a priori source information which can be used when the channel environment is bad. The suggested scheme does not require changes on the transmitter side. Compared with separate decoding systems, the gain in signal to noise ratio is about 0 5-1.0 dB with a limi...

Improved Soft Cancellation Decoding of Polar Codes

The soft cancellation decoding of polar codes achieves a better performance than the belief propagation decoding with lower computational time and space complexities.However,because the soft cancellation decoding is based on the successive cancellation decoding,the decoding efficiency and performance with finite-length blocks can be further improved.Exploiting the idea of the successive cancellation list decoding,the soft cancellation decoding can be improved in two aspects:one is by adding branch decoding to the error-prone information bits to increase the accuracy of the soft information,and the other is through using partial iterative decoding to reduce the time and computational complexities.Compared with the original method,the improved soft cancellation decoding makes progress in the error correction performance,increasing the decoding efficiency and reducing the computational complexity,at the cost of a small increase of space complexity.

Power and rate adaption in wireless communication systems with energy harvesting-based on soft decision decoding

In this paper,the online power control and rate adaptation for a wireless communication system with energy harvesting(EH)are investigated,in which soft decision decoding is adopted by the receiver.To efficiently utilize the harvested energy and maximize the actual achievable transmission rate under the constraints of the available channel codes and modulation schemes,the transmit power,code rate and modulation order are jointly optimized.The Lyapunov framework is used to transform the long-term optimization problem into a per time slot optimization problem.Since there is no theoretical formula for the error rate of soft decision decoding,the optimization problem cannot be solved analytically.A table to find the optimal modulation order and code rate under the different values of signal-to-noise ratio(SNR)is built first,and then a numerical algorithm to find the solution to the optimization problem is given.The feasibility and performance of the proposed algorithm are demonstrated by simulation.The simulation results show that compared with the algorithms to maximize the theoretical channel capacity,the proposed algorithm can achieve a higher actual transmission rate.

Approximate Iteration Detection and Precoding in Massive MIMO

Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.

Low-Complexity Detection and Decoding Scheme for LDPC-Coded MLC NAND Flash Memory

With the development of manufacture technology, the multi-level cell(MLC)technique dramatically increases the storage density of NAND flash memory. As the result,cell-to-cell interference(CCI) becomes more serious and hence causes an increase in the raw bit error rate of data stored in the cells.Recently, low-density parity-check(LDPC)codes have appeared to be a promising solution to combat the interference of MLC NAND flash memory. However, the decoding complexity of the sum-product algorithm(SPA) is extremely high. In this paper, to improve the accuracy of the log likelihood ratio(LLR) information of each bit in each NAND flash memory cell, we adopt a non-uniform detection(N-UD) which uses the average maximum mutual information to determine the value of the soft-decision reference voltages.Furthermore, with an aim to reduce the decoding complexity and improve the decoding performance, we propose a modified soft reliabilitybased iterative majority-logic decoding(MSRBI-MLGD) algorithm, which uses a non-uniform quantizer based on power function to decode LDPC codes. Simulation results show that our design can offer a desirable trade-off between the performance and complexity for high-column-weight LDPC-coded MLC NAND flash memory.

New concatenated soft decoding of Reed-Solomon codes with lower complexities

To improve error-correcting performance, an iterative concatenated soft decoding algorithm for Reed-Solomon （RS） codes is presented in this article. This algorithm brings both complexity as well as advantages in performance over presently popular sot~ decoding algorithms. The proposed algorithm consists of two powerful soft decoding techniques, adaptive belief propagation （ABP） and box and match algorithm （BMA）, which are serially concatenated by the accumulated log-likelihood ratio （ALLR）. Simulation results show that, compared with ABP and ABP-BMA algorithms, the proposed algorithm can bring more decoding gains and a better tradeoff between the decoding performance and complexity.