High-dimensional embeddings excel in few-shot regimes for some wireless tasks but carry high latency and parameter costs, whereas compressed autoencoder representations provide better noise robustness, stability, and efficiency.
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Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness
High-dimensional embeddings excel in few-shot regimes for some wireless tasks but carry high latency and parameter costs, whereas compressed autoencoder representations provide better noise robustness, stability, and efficiency.