PROJECT TITLE :

Sparsity-Boosted Detection for Large MIMO Systems

ABSTRACT:

In this letter, we have a tendency to propose a unique low-complexity detector for massive MIMO systems, that is capable of achieving near-ML performance for low order constellation (like BPSK, 4-QAM). The main idea of our algorithm is to successively boost the detection by leveraging the hidden sparsity within the residual error of received signal. Specifically, since the symbol error rate (SER) of the MMSE detector is sometimes not high (say, but 10percent), the residual error, that is that the distinction between the original transmitted signal and therefore the recovered one, would exhibit important sparsity. Therefore, by locating the non-zero entries (i.e., the incorrectly detected symbols) via compressive sensing algorithms, we tend to can reduce the initial MIMO system to a new one, whose input dimension is a lot of but the output dimension. This implies that a linear detector can suffice for achieving close to-optimal performance, otherwise we have a tendency to can repeat the higher than procedures to iteratively boost the detection until satisfaction. Overall, our proposed algorithm will achieve performance close to the optimal ML detector, whereas its complexity is simply on the order of the linear detectors (say, MMSE detector).


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