Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness

arXiv (Cornell University)

Abstract

Building on recent advances in representation learning for wireless channels, this work investigates the cost-benefit trade-offs of high-dimensional channel embeddings in practical systems. We benchmark multiple wireless representations: high-dimensional learned embeddings from a wireless foundation model, compact autoencoder-based representations with significantly lower dimensionality, and raw data baselines, evaluating their performance across diverse downstream tasks. We then systematically analyze data efficiency, noise robustness, and computational complexity, explicitly characterizing the resource overhead associated with high-dimensional embeddings. Beyond standard tasks such as line-of-sight/non-line-of-sight (LoS/NLoS) classification and beam selection, we introduce power allocation as a new downstream task. Our results reveal clear trade-offs: while high-dimensional embeddings can perform well in few-shot regimes for certain tasks, they incur substantial latency and parameter overhead. In contrast, compressed latent representations learned by autoencoders demonstrate improved noise robustness and more stable performance across tasks, while significantly reducing computational and transmission costs.

Authors 6

1. Murilo Batista Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   Chair of Information Theory and Data Analytics (INDA) , RWTH Aachen University , Aachen , Germany

2. Shirin Salehi Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   Chair of Information Theory and Data Analytics (INDA) , RWTH Aachen University , Aachen , Germany

3. Saeed Mashdour

   Pontifícia Universidade Católica do Rio de Janeiro

   Affiliation as printed

   Centre for Telecommunications Studies , Pontifical Catholic University of Rio de Janeiro , Rio de Janeiro 22541-041 , Brazil

4. Paul Zheng Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   Chair of Information Theory and Data Analytics (INDA) , RWTH Aachen University , Aachen , Germany

5. Rodrigo C. de Lamare

   University of York · Pontifícia Universidade Católica do Rio de Janeiro

   Affiliation as printed

   Centre for Telecommunications Studies , Pontifical Catholic University of Rio de Janeiro , Rio de Janeiro 22541-041 , Brazil

   School of Physics, Engineering and Technology , University of York , United Kingdom

6. Anke Schmeink Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   Chair of Information Theory and Data Analytics (INDA) , RWTH Aachen University , Aachen , Germany