Thompson Sampling for Device-to-Device Relay Selection Under a 3GPP-Aligned mmWave Channel Model: A Bandit-Theoretic Study with Bernoulli and Gaussian Variants

Device-to-Device (D2D) communication is a key technology for 5G-Advanced and 6G networks, enabling nearby User Equipment (UE) to communicate directly or via a relay when the direct link is blocked or unreliable. At Millimeter-Wave (mmWave) frequencies, frequent blockage and intermittent connectivity make relay selection a sequential decision problem under uncertainty, naturally formulated as a Multi-Armed Bandit (MAB). This paper develops and evaluates two Thompson Sampling (TS) approaches for D2D relay selection using a 3GPP TR 38.901 UMi-Street Canyon channel model. The first TS approach uses a Beta-Bernoulli model to maximize link-success probability, whereas the second TS uses a Gaussian-conjugate model to maximize continuous spectral efficiency. Over T=2000 rounds with N=10 candidate relays and shared channel realizations, both approaches identify the best relay within about 100–150 rounds and direct more than 94% of selections to it. Compared with random selection, they raise the mean spectral efficiency by ≈3×, raise the link-success rate to above 99%, and cut the outage probability to 0.9% (TS-Bernoulli) and 0.4% (TS-Gaussian). These results demonstrate the effectiveness of TS for relay selection under realistic mmWave propagation conditions and highlight the importance of aligning the learning objective with the target performance metric.