Site-Specific Finetuning of Neural Receivers with Real-World 5G NR Measurements
This work addresses the need for efficient wireless communication in real-world 5G deployments, though it is incremental as it extends prior findings from synthetic data to real-world benchmarks.
The paper tackled the problem of improving wireless receiver performance by finetuning neural receivers to specific deployment scenarios using real-world 5G NR measurements, confirming substantial error-rate improvements that generalize across hardware and environments.
Finetuning wireless receivers to a specific deployment scenario can yield significant error-rate performance improvements without increasing processing complexity. However, site-specific finetuning has so far only been demonstrated on synthetic channel data and lacks real-world benchmarks. In this work, we empirically study site-specific finetuning of neural receivers using real-world 5G NR physical uplink shared channel (PUSCH) data collected with an over-the-air testbed at ETH Zurich across three scenarios: (i) a small laboratory, (ii) a large office floor, and (iii) a high-mobility outdoor environment. Our results confirm substantial error-rate performance improvements from site-specific finetuning, consistent with earlier findings based on synthetic channel data. Moreover, we demonstrate that these improvements generalize across different user-equipment hardware and deployment scenarios.