How Critical is Site-Specific RAN Optimization? 5G Open-RAN Uplink Air Interface Performance Test and Optimization from Macro-Cell CIR Data
This addresses the need for more realistic channel models in 5G RAN optimization, though it is incremental as it builds on existing neural receiver methods with site-specific data.
The paper tackled the problem of unrealistic statistical channel models for 5G air interface optimization by comparing neural receivers trained on simulated 3GPP TDL models versus measured macro-cell CIR data, achieving a 10% block error rate at a 1.85 dB lower SNR with site-specific fine-tuning.
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?"