# How Critical is Site-Specific RAN Optimization? 5G Open-RAN Uplink Air Interface Performance Test and Optimization from Macro-Cell CIR Data

arXiv: https://arxiv.org/abs/2410.19565 · HTML: https://mlsanity.com/paper/2410.19565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Communication Algorithms via Deep Learning](https://mlsanity.com/paper/1805.09317/) (2018)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks](https://mlsanity.com/paper/1805.06350/) (2018)
- [WGAN-based Autoencoder Training Over-the-air](https://mlsanity.com/paper/2003.02744/) (2020)
- [Neural Network-based OFDM Receiver for Resource Constrained IoT Devices](https://mlsanity.com/paper/2205.06159/) (2022)
- [GAN-based Massive MIMO Channel Model Trained on Measured Data](https://mlsanity.com/paper/2403.05321/) (2024)
- [Realistic Channel Models Pre-training](https://mlsanity.com/paper/1907.09117/) (2019)
- [AERIQ: SDR-Based LTE I/Q Measurement and Analysis Framework for Air-to-Ground Propagation Modeling](https://mlsanity.com/paper/2210.07433/) (2022)

## Abstract (arXiv)

In this paper, we consider the importance of channel measurement data from specific sites and its impact on air interface optimization and test. Currently, a range of statistical channel models including 3GPP 38.901 tapped delay line (TDL), clustered delay line (CDL), urban microcells (UMi) and urban macrocells (UMa) type channels are widely used for air interface performance testing and simulation. However, there remains a gap in the realism of these models for air interface testing and optimization when compared with real world measurement based channels. To address this gap, we compare the performance impacts of training neural receivers with 1) statistical 3GPP TDL models, and 2) measured macro-cell channel impulse response (CIR) data. We leverage our OmniPHY-5G neural receiver for NR PUSCH uplink simulation, with a training procedure that uses statistical TDL channel models for pre-training, and fine-tuning based on measured site specific MIMO CIR data. The proposed fine-tuning method achieves a 10% block error rate (BLER) at a 1.85 dB lower signal-to-noise ratio (SNR) compared to pre-training only on simulated TDL channels, illustrating a rough magnitude of the gap that can be closed by site-specific training, and gives the first answer to the question "how much can fine-tuning the RAN for site-specific channels help?"