deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deepunfoldedconjugategradient (CG) architecture for large-scale multiple-input multiple-output de...
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deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deepunfoldedconjugategradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugategradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions.
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