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In this paper, we propose a simple yet effective modulation classification method for ML-based multi-user detection. We show that the proposed method, which is a simplification of Bayesian likelihood ratio test, can be implemented by modifying the sphere decoding algorithm. As a result, the modulation classification as well as the multi-user detection can be jointly achieved with minimal classification...
In this letter, we propose a simple yet effective modulation classification method for maximum likelihood multiuser detection. Our method is a modification of generalized likelihood ratio test (GLRT) that approximates the optimal classifier in the Bayesian sense. We show that the proposed method can be implemented by modifying the sphere decoding algorithm to support multimodulation. Simulation results...
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