Name
Abstract | ||
|---|---|---|
In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environment with state, action, and reward is defined in the context of polar coding. Specifically, a state represents the construction of an (N, K) polar code, an action specifies its reduction to an (N, K - 1) subcode, and the reward is the decoding performance. A neural network architecture consisting of both policy and value networks is proposed to generate actions based on the observed states, aiming at maximizing the overall rewards. A loss function is defined to trade off between exploitation and exploration. To further improve learning efficiency and quality, an "integrated learning" paradigm is proposed. It first employs a genetic algorithm to generate a population of (sub-)optimal polar codes for each (N, K), and then uses them as prior knowledge to refine the policy of RL. Such a paradigm is shown to accelerate the training process, and converge at better performances. Simulation results show that the proposed learning-based polar constructions achieve comparable, or even better, performances than the state of the art under successive cancellation list (SCL) decoders, and meanwhile satisfies the nested property. Last but not least, the learning process does not exploit explicit expert knowledge from polar coding theory. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GLOBECOM38437.2019.9014004 | IEEE Global Communications Conference |
Keywords | Field | DocType |
Polar codes,Nested polar code construction,Markov decision process,Reinforcement learning,Integrated learning | Population,Mathematical optimization,Markov decision process,Coding (social sciences),Theoretical computer science,Coding theory,Polar code,Decoding methods,Mathematics,Genetic algorithm,Reinforcement learning | Journal |
Volume | ISSN | Citations |
abs/1904.07511 | 2334-0983 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingchen Huang | 1 | 0 | 0.34 |
| Huazi Zhang | 2 | 289 | 24.91 |
| Rong Li | 3 | 31 | 17.93 |
| Yiqun Ge | 4 | 10 | 4.64 |
| Jun Wang | 5 | 9228 | 736.82 |