Toward Realistic SDR-Based Emulation of Ray-Traced Millimeter-Wave Indoor Channels for Next-Generation Wireless Systems

The convergence of advancements in antenna technology with Machine Learning (ML) is envisioned to enhance coverage and capacity for wireless communication systems in complex and dynamic millimeter-wave (mmWave) indoor environments. These environments often experience significant performance variability due to user movement and obstacles. Our study highlights the potential benefits of combining reconfigurable antenna (RA) systems with ML to address mmWave propagation challenges in indoor environments. However, rigorous verification and validation are essential to ensure accurate modeling of mmWave propagation, which is inherently complex and challenging to evaluate experimentally. To circumvent costly, time-intensive, and non-repeatable real-world measurements, we introduce a hardware emulation framework. It enables realistic evaluation of non-stationary, ray-traced channel models with a large number of propagation paths. This framework integrates realistic channel coefficients from site-specific 3D raytracing scenarios with RA-equipped access points (APs) and user mobility features. It incorporates them into a softwaredefined radio (SDR)-based full-mesh wireless channel emulation system, enabling the coexistence of virtual and real nodes. We present experimental results from transceiver hardware-in-theloop testing in this testbed. These results feature repeatable and controllable path loss and delays between communicating nodes. Experimental evaluations confirmed that intelligent state selection algorithms, particularly Thompson Sampling and UCB1-Tuned, significantly enhance system performance in terms of throughput and packet error rate, outperforming traditional omni-directional antenna configurations.