New Joint-Thompson Sampling algorithm enhances communication link adaptation

Researchers have introduced Joint-Thompson Sampling (Joint-TS), a novel algorithm for link adaptation in communication systems. This method models the problem as a multi-armed bandit, where each modulation and coding scheme (MCS) represents an arm. Unlike traditional Thompson Sampling, Joint-TS leverages the ordered nature of MCS success probabilities by employing a multivariate ordered Beta distribution as its prior, thus preserving inherent monotonicity. Simulation results indicate that Joint-TS offers robust and consistent performance across various scenarios, outperforming existing algorithms in specific situations. AI

IMPACT This algorithm could improve the efficiency and reliability of wireless communication systems by optimizing modulation and coding schemes.

RANK_REASON The cluster contains a research paper detailing a new algorithm for a specific technical problem. [lever_c_demoted from research: ic=1 ai=0.4]

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• arXiv
• beta distribution
• Joint-Thompson Sampling
• multi-armed bandit
• multivariate ordered Beta distribution
• Thompson Sampling
• Upper Confidence Bound

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