scholarly journals Reinforcement Learning for Bit-Flipping Decoding of Polar Codes

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 171
Author(s):  
Xiumin Wang ◽  
Jinlong He ◽  
Jun Li ◽  
Liang Shan
Keyword(s):  
Reinforcement Learning ◽  
Error Propagation ◽  
Polar Codes ◽  
Learning Technology ◽  
Decoding Algorithm ◽  
Critical Set ◽  
Q Learning ◽  
Successive Cancellation ◽  
Information Sequence ◽  
Decoding Delay

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.

scholarly journals The Effect of Error Propagation on the Performance of Polar Codes Utilizing Successive Cancellation Decoding Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 114-120
Author(s):  
O. Gazi ◽  
A. A. Andi
Keyword(s):  
Error Propagation ◽  
Polar Codes ◽  
Decoding Algorithm ◽  
Wrong Decision ◽  
Successive Cancellation ◽  
Successive Cancellation Decoding

 In this paper, we discuss and analyze the effect of error propagation on the performance polar codes decoded using the successive cancellation algorithm. We show that error propagation due to erroneous bit decision is a catastrophic issue for the successive cancellation decoding of polar codes. Even a wrong decision on a single bit may cause an abundance of successor bits to be wrongly decoded. Furthermore, we observe that the performance of polar codes is significantly improved if even single bit errors are detected and corrected before the decoding of successor bits.

scholarly journals Improved Adaptive Successive Cancellation List Decoding of Polar Codes

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 899
Author(s):  
Xiumin Wang ◽  
Jinlong He ◽  
Jun Li ◽  
Zhuoting Wu ◽  
Liang Shan ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Likelihood Ratio ◽  
Polar Codes ◽  
Correct Result ◽  
List Decoding ◽  
Successive Cancellation ◽  
Burst Error ◽  
Log Likelihood ◽  
Information Sequence ◽  
Simulation Results

Although the adaptive successive cancellation list (AD-SCL) algorithm and the segmented-CRC adaptive successive cancellation list (SCAD-SCL) algorithm based on the cyclic redundancy check (CRC) can greatly reduce the computational complexity of the successive cancellation list (SCL) algorithm, these two algorithms discard the previous decoding result and re-decode by increasing L, where L is the size of list. When CRC fails, these two algorithms waste useful information from the previous decoding. In this paper, a simplified adaptive successive cancellation list (SAD-SCL) is proposed. Before the re-decoding of updating value L each time, SAD-SCL uses the existing log likelihood ratio (LLR) information to locate the range of burst error bits, and then re-decoding starts at the incorrect bit with the smallest index in this range. Moreover, when the segmented information sequence cannot get the correct result of decoding, the SAD-SCL algorithm uses SC decoding to complete the decoding of the subsequent segmentation information sequence. Furthermore, its decoding performance is almost the same as that of the subsequent segmentation information sequence using the AD-SCL algorithm. The simulation results show that the SAD-SCL algorithm has lower computational complexity than AD-SCL and SCAD-SCL with negligible loss of performance.

scholarly journals Bidirectional stack decoding of polar codes

2021 ◽  
Vol 69 (2) ◽  
pp. 405-415
Author(s):  
Aleksandar Minja ◽  
Dušan Dobromirov ◽  
Vojin Šenk
Keyword(s):  
Error Correction ◽  
High Speed ◽  
Parallel Implementation ◽  
Convolutional Codes ◽  
Low Complexity ◽  
Low Latency ◽  
Polar Codes ◽  
Decoding Algorithm ◽  
Decoding Algorithms ◽  
Successive Cancellation

Introduction/purpose: The paper introduces a reduced latency stack decoding algorithm of polar codes, inspired by the bidirectional stack decoding of convolutional codes and based on the folding technique. Methods: The stack decoding algorithm (also known as stack search) that is useful for decoding tree codes, the list decoding technique introduced by Peter Elias and the folding technique for polar codes which is used to reduce the latency of the decoding algorithm. The simulation was done using the Monte Carlo procedure. Results: A new polar code decoding algorithm, suitable for parallel implementation, is developed and the simulation results are presented. Conclusions: Polar codes are a class of capacity achieving codes that have been adopted as the main coding scheme for control channels in 5G New Radio. The main decoding algorithm for polar codes is the successive cancellation decoder. This algorithm performs well at large blocklengths with a low complexity, but has very low reliability at short and medium blocklengths. Several decoding algorithms have been proposed in order to improve the error correcting performance of polar codes. The successive cancellation list decoder, in conjunction with a cyclic redundancy check, provides very good error-correction performance, but at the cost of a high implementation complexity. The successive cancellation stack decoder provides similar error-correction performance at a lower complexity. Future machine-type and ultra reliable low latency communication applications require high-speed low latency decoding algorithms with good error correcting performance. In this paper, we propose a novel decoding algorithm, inspired by the bidirectional stack decoding of classical convolutional codes, with reduced latency that achieves similar performance as the classical successive cancellation list and successive cancellation stack decoding algorithms. The results are presented analytically and verified by simulation.

An Adaptive Fusion Successive Cancellation List Decoder for Polar Codes with Cyclic Redundancy Check

2020 ◽  
Vol E103.B (1) ◽  
pp. 43-51 ◽  
Author(s):  
Yuhuan WANG ◽  
Hang YIN ◽  
Zhanxin YANG ◽  
Yansong LV ◽  
Lu SI ◽  
...  
Keyword(s):  
Polar Codes ◽  
Cyclic Redundancy Check ◽  
Successive Cancellation ◽  
Adaptive Fusion ◽  
List Decoder

scholarly journals Personalized project recommendations: using reinforcement learning

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Faxin Qi ◽  
Xiangrong Tong ◽  
Lei Yu ◽  
Yingjie Wang
Keyword(s):  
Reinforcement Learning ◽  
User Behavior ◽  
Collaborative Work ◽  
Recursive Least Squares ◽  
The Internet ◽  
Dynamic Impact ◽  
Rls Algorithm ◽  
Trust Value ◽  
Q Learning ◽  
Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

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