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 a...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.展开更多
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 utili...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.展开更多
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 per...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.展开更多
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 b...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.展开更多
文摘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.
基金National Nature Science Foundation of China(61971080).
文摘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.
基金supported by the National Natural Science Foundation of China(60472104)
文摘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.
文摘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.
