Incremental Learning-based MIMO Relay Selection

The forthcoming 6G wireless networks are expected to be much more machine-intelligent in resource allocation, including relay selections to serve ever-increasing users and the internet of things with extended coverage. Selecting an optimal multiple-input multiple-output (MIMO) relay using conventional methods becomes challenging due to dependency on perfect channel information, which exponentially increases feedback overhead. In this paper, we propose a novel incremental learning-based online MIMO relay selection algorithm, with only imperfect channel gain information available at the relay nodes in the framework of MIMO two-way amplify-and-forward (TWAF) relay networks. In particular, we develop naive Bayes, logistic regression, and support vector-based incremental learning classifiers for the near-optimal online relay selection. Using simulated results, we show that the proposed online relay selection approaches outperform the best conventional Gram-Schmidt algorithm while reducing the feedback overhead up to a factor of eight.