The complexity of the mode selection and resource allocation (MS&RA) problem has hampered the commercialization progress of Device-to-Device (D2D) communication in 5G networks. Furthermore, the combinatorial nature of MS&RA has forced the majority of existing proposals to focus on constrained scenarios or offline solutions to contain the size of the problem. Given the real-time constraints in actual deployments, a reduction in computational complexity is necessary. Adaptability is another key requirement for mobile networks that are exposed to constant changes such as channel quality fluctuations and mobility. In this article, we propose an online learning technique (i.e., CBMoS) which leverages combinatorial multi-armed bandits (CMAB) to tackle the combinatorial nature of MS&RA. Furthermore, our two-stage CMAB design results in a tight model, which eliminates the theoretically feasible but practicality invalid options from the solution space. We prototype the first SDR-based D2D testbed to verify the performance of CBMoS under real-world conditions. The simulations confirm that the fast learning speed of CBMoS leads to outperforming the benchmark schemes by up to 132%. In experiments, CBMoS exhibits even higher performance (up to 142%) than in the simulations. This stems from the adaptability/fast learning speed of CBMoS in presence of high channel dynamics which cannot be captured via statistical channel models used in the simulators.

Experimental setups for cellular communications have always been a rare commodity in academia. The advent of software-defined radios (SDRs) paved the way for researchers to prototype their ideas on real hardware. However, existing SDR platforms and their associated reference design codes mostly provide basic cellular functionality with limitations such as low numbers of users and computational capacity. In this demo, we demonstrate the first SDR-based testbed for inband D2D communications using LabVIEW Communications and the USRP hardware platform. Furthermore, we implement a light-weight quality-aware scheduler which adaptively switches communication links from D2D to cellular and vice versa.