Channel Selection for Network-Assisted D2D Communication via No-Regret Bandit Learning With Calibrated Forecasting
Network-Assisted D2D (Device-to-Device) communication is a technology that allows direct communication between nearby mobile devices, without going through the base station or any other infrastructure. In this technology, the mobile devices can use the available radio spectrum more efficiently, reduce latency, and save energy.
One of the challenges in Network-Assisted D2D communication is to choose the appropriate channel for communication, considering the dynamic and uncertain nature of the wireless environment. The channel selection problem can be formulated as a multi-armed bandit problem, where the mobile devices need to choose the best channel among multiple options to maximize their rewards.
"No-Regret Bandit Learning" is a framework for solving the multi-armed bandit problem, where the goal is to minimize the "regret," which is the difference between the cumulative reward obtained by the algorithm and the cumulative reward obtained by the best channel selection strategy. This framework provides a guarantee that the algorithm will not perform significantly worse than the best channel selection strategy over a long period.
"Calibrated Forecasting" is a technique used to predict the future reward for each channel accurately. In this technique, the algorithm learns from the past performance of each channel and uses this information to predict the expected reward for each channel in the future. The prediction is calibrated in the sense that the expected reward is accurate and reflects the uncertainty in the wireless environment.
Therefore, "Channel Selection for Network-Assisted D2D Communication via No-Regret Bandit Learning With Calibrated Forecasting" is a technique that combines the No-Regret Bandit Learning framework with Calibrated Forecasting to solve the channel selection problem in Network-Assisted D2D communication. The technique allows mobile devices to choose the appropriate channel for communication by predicting the future reward accurately and minimizing the regret over a long period.
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