Considering that the data symbols or channel parameters are unknown, we adopt a generalized likelihood ratio test (GLRT), which is known to be optimal with unknown parameters based on finite samples.

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This paper introduces a generalized framework for time-varying batch-averaged Q-learning, termed sample-averaged Q-learning (SA-QL), which extends traditional single-sample Q-learning by aggregating ...