<ul data-eligibleForWebStory="true"><li>Transformers have shown potential in wireless communication problem-solving through in-context learning, but current models require many layers for satisfactory performance, leading to high costs.</li><li>A new approach, CHOOSE, enhances shallow Transformers for wireless symbol detection by incorporating autoregressive reasoning steps in the hidden space.</li><li>CHOOSE significantly boosts the reasoning capacity of 1-2 layer models without increasing model depth, allowing for lightweight Transformers to achieve detection performance comparable to deeper models.</li><li>Experimental results indicate that CHOOSE outperforms traditional shallow Transformers, offering performance similar to deep models while maintaining storage and computational efficiency, making it suitable for resource-constrained mobile devices.</li></ul>

Chain-of-Thought Enhanced Shallow Transformers for Wireless Symbol Detection

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