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Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication

Yongjeong Oh, Jaehong Jo, Byonghyo Shim, Yo-Seb Jeon

TL;DR

A novel approach for joint activity detection, channel estimation, and data detection in uplink grant-free nonorthogonal multiple access (NOMA) systems, enhanced with deep learning to jointly tackle the AD, CE, and DD problems.

Abstract

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.

Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication

TL;DR

A novel approach for joint activity detection, channel estimation, and data detection in uplink grant-free nonorthogonal multiple access (NOMA) systems, enhanced with deep learning to jointly tackle the AD, CE, and DD problems.

Abstract

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.
Paper Structure (30 sections, 36 equations, 9 figures, 2 tables)

This paper contains 30 sections, 36 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Prior Works
  3. Contributions
  4. System Model
  5. Uplink Grant-Free NOMA System
  6. Key Challenges in Grant-Free NOMA
  7. Proposed Pilot-Only PIC Framework
  8. Overall Structure
  9. Input and Output
  10. Stage ($K$ CE modules and $K$ IC modules)
  11. AD Module
  12. Final Decision
  13. Training Procedure
  14. Extension of the Pilot-Only PIC
  15. Data-Aided PIC
  16. ...and 15 more sections

Figures (9)

  • Figure 1: Illustration of the uplink grant-free NOMA system considered in our work.
  • Figure 2: Overall diagram of two communication schemes in grant-free NOMA: (i) coherent scheme and (ii) non-coherent scheme.
  • Figure 3: Illustration of the pilot-only PIC framework with $T$ stages and $K$ AD modules.
  • Figure 4: Illustration of the $t$th stage in the data-aided PIC framework, consisting of $K$ CE modules, $K$ IC modules, and $K$ DD modules.
  • Figure 5: Illustration of the $t$th stage in the non-coherent PIC framework, consisting of $K$ CE modules, $K$ IC modules, and $K$ DD modules.
  • ...and 4 more figures