Intelligent MIMO Detection With Momentum-Induced Unfolded Layers

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Abstract
In this letter, we present a novel deep-learning-based network for MIMO symbol detection, referred to as a momentum-induced detection network, MomentNet. Inspired by the projected gradient descent algorithm, our proposed architecture integrates an additional momentum component to the previous deep-learning detectors. By eliminating redundant values, we have successfully reduced the number of training parameters by half from the baseline network, compensating for the increased computational burden introduced by the incorporation of momentum parameters. Simulation results reveal that the proposed network achieves near-Maximum Likelihood symbol error rate performance with low computational complexity.
Funding Information
  • Korea Research Institute for Defense Technology Planning and Advancement
  • Defense Acquisition Program Administration (KRIT-CT-22-078)
  • National Research Foundation of Korea
  • Korea government (2022R1A2C2092521)
  • Grand Information Technology Research Center support program (IITP-2022-2020-0-01612)

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