Abstract
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.
Highlights
When the code length goes to infinity, the channels are divided into noisy subchannels and noiseless subchannels, which are known as “channel polarization”
We propose a Q-learning-assisted SC flip (SCF) (QLSCF) algorithm which uses the reinforcement learning to reduce decoding delay of the SCF decoding
For the proposed QLSCF algorithm, the order of candidate bits is already obtained in the reinforcement learning
Summary
The fifth generation (5G) mobile wireless communication system has begun to be commercially used. A neural SC (NSC) decoder based on the portioning technique decreases the complexity by learning information bits in each subblock of polar codes, and it reduces the decoding delay of the SC algorithm [21]. For further optimizing the structure of error correction codes (ECC), Huang et al studied an artificial intelligence (AI)-driven method to design the ECC [24] They proposed a construction-evaluation framework, in which the construction framework can be implemented by various AI algorithms, such as reinforcement learning and genetic algorithm, and the evaluation framework can provide the performance metrics of the ECC. In order to accurately locate the candidate bits, Wang et al proposed a deep learning-assisted SCF decoding algorithm, using a long short-term memory (LSTM) network and reinforcement learning to find the error bits [26].
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