Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free
  NOMA in Machine-Type Communication
Yongjeong Oh Jaehong Jo Byonghyo Shim Yo-Seb Jeon
In this paper, we present a novel approach for joint activity detection (AD),
channel estimation (CE), and data detection (DD) in uplink grant-free
non-orthogonal multiple access (NOMA) systems. Our approach employs an
iterative and parallel interference removal strategy inspired by parallel
interference cancellation (PIC), enhanced with deep learning to jointly tackle
the AD, CE, and DD problems. Based on this approach, we develop three PIC
frameworks, each of which is designed for either coherent or non-coherence
schemes. The first framework performs joint AD and CE using received pilot
signals in the coherent scheme. Building upon this framework, the second
framework utilizes both the received pilot and data signals for CE, further
enhancing the performances of AD, CE, and DD in the coherent scheme. The third
framework is designed to accommodate the non-coherent scheme involving a small
number of data bits, which simultaneously performs AD and DD. Through joint
loss functions and interference cancellation modules, our approach supports
end-to-end training, contributing to enhanced performances of AD, CE, and DD
for both coherent and non-coherent schemes. Simulation results demonstrate the
superiority of our approach over traditional techniques, exhibiting enhanced
performances of AD, CE, and DD while maintaining lower computational
complexity.