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

TL;DR

This paper introduces CHOOSE, a shallow Transformer framework with autoregressive reasoning that enhances wireless symbol detection, achieving deep-model performance with significantly reduced computational and storage requirements.

Contribution

The paper proposes a novel CoT-enhanced shallow Transformer architecture for wireless detection, enabling high performance with minimal layers and resource efficiency.

Findings

Outperforms conventional shallow Transformers in detection accuracy.

Achieves performance comparable to deep Transformers.

Maintains low computational and storage costs.

Abstract

Transformers have shown potential in solving wireless communication problems, particularly via in-context learning (ICL), where models adapt to new tasks through prompts without requiring model updates. However, prior ICL-based Transformer models rely on deep architectures with many layers to achieve satisfactory performance, resulting in substantial storage and computational costs. In this work, we propose CHain Of thOught Symbol dEtection (CHOOSE), a CoT-enhanced shallow Transformer framework for wireless symbol detection. By introducing autoregressive latent reasoning steps within the hidden space, CHOOSE significantly improves the reasoning capacity of shallow models (1-2 layers) without increasing model depth. This design enables lightweight Transformers to achieve detection performance comparable to much deeper models, making them well-suited for deployment on resource-constrained mobile devices. Experimental results demonstrate that our approach outperforms conventional shallow Transformers and achieves performance comparable to that of deep Transformers, while maintaining storage and computational efficiency. This represents a promising direction for implementing Transformer-based algorithms in wireless receivers with limited computational resources.