As 5G wireless networks evolve to 6G, the necessity for precise channel prediction intensifies, but approaches to date have lacked generalizability in unseen frequency bands, locations, or dynamic environments. To overcome these challenges, we propose RadioTwin, a high-fidelity, physically interpretable digital twin of the real-world radio environment. In contrast with prior deep learning approaches, we model ray-object interactions in the ambient environment using physics-guided models and train a neural network to infer the intrinsic material radio parameters of the environment. We incorporate this model into a differentiable ray tracing framework to characterize how wireless signals reflect, refract, diffract, and scatter when bouncing off different objects. This integration empowers us to predict wireless channels across different links and frequency bands, even in dynamic environments. We optimize the training and inference computational efficiency of RadioTwin and fully integrate it into Sionna ray-tracing framework. Our evaluation shows that RadioTwin achieves consistently higher channel prediction accuracy than SOTA systems: over 5.5dB, 4dB, and 4.7dB median EVM improvement on cross-band, cross-link, and the more challenging cross-band and cross-link channel prediction task, respectively.

Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that the quantum-inspired computing approach based on Coherent Ising Machines~(CIMs) is a promising candidate for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the direct approach adopted in the existing literature on MIMO detection using Quantum Annealing. We evaluate our methods using a simplified, quantum-inspired model and show that our methods can achieve a near-optimal performance for several Large MIMO systems, like 16x16, 20x20, and 24x24 MIMO with BPSK modulation.