Reinforcement Learning-Based SCMA Codebook Design for Uplink Rayleigh Fading Channels

Sparse Code Multiple Access (SCMA) is a promising technique for next generation mobile communication systems. In this letter, the problems surrounding the design of an SCMA codebook problem are confronted through the use of Artificial Intelligence (AI) techniques. The suggested algorithm is based on...

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Bibliographic Details
Published in:IEEE wireless communications letters 2021-08, Vol.10 (8), p.1717-1721
Main Authors: Chen, Yen-Ming, Gonzalez, Carlos D. Sagastume, Wang, Pao-Hung, Chen, Kai-Ping
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
artificial intelligence (AI)
codebook design
Complexity
Constellations
Design parameters
Machine learning
Measurement
Mobile communication systems
NOMA
Reinforcement learning
reinforcement learning (RL)
Sparse code multiple access (SCMA)
Unsupervised learning
Uplink
Upper bound
Wireless communication
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Summary:Sparse Code Multiple Access (SCMA) is a promising technique for next generation mobile communication systems. In this letter, the problems surrounding the design of an SCMA codebook problem are confronted through the use of Artificial Intelligence (AI) techniques. The suggested algorithm is based on Reinforcement Learning (RL). The design parameters include a set of actions, a set of states, and a reward function. It is shown that the proposed algorithm is capable of generating codebooks that include superior metric values and optimized signal constellations based on low levels of searching complexity. The low-complexity feature in the RL-based construction algorithm ensures that it is more suitable to applications that rely on large-scale SCMA schemes.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2021.3077986