Model-Driven Deep Learning-Based Signal Detector for CP-Free MIMO-OFDM Systems

Author(s):  
Xingyu Zhou ◽  
Jing Zhang ◽  
Chao-Kai Wen ◽  
Jun Zhang ◽  
Shi Jin
Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 599
Author(s):  
Seong-Joon Shim ◽  
Seung-Jin Choi ◽  
Hyoung-Kyu Song

For a low complexity signal detector to reduce the power consumption for multiple input and multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the depth-first sphere decoding (DFSD) detection scheme was proposed. However, the DFSD detection scheme still has high complexity in the hardware implementation. The complexity is especially high when the signal-to-noise ratio (SNR) is low. Therefore, this paper proposes an adaptive DFSD detection scheme. The proposed detection scheme arrays nodes, sorting by ascending order of squared Euclidean distance (ED) at the top layer of tree structure. Then, the proposed detection scheme uses the different number of nodes according to thresholds based on channel condition. In the simulation results, the proposed detection scheme has similar error performance and low complexity compared with the conventional DFSD detection scheme. Therefore, the proposed detection scheme reduces the power consumption in the signal detector.


2013 ◽  
Vol E96.B (3) ◽  
pp. 830-835 ◽  
Author(s):  
Wen ZHONG ◽  
Anan LU ◽  
Xiqi GAO
Keyword(s):  

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