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Asynchronous MIMO-OFDM Massive Unsourced Random Access with Codeword Collisions

Tianya Li, Yongpeng Wu, Junyuan Gao, Wenjun Zhang, Xiang-Gen Xia, Derrick Wing Kwan Ng, Chengshan Xiao

Abstract

This paper investigates asynchronous multiple-input multiple-output (MIMO) massive unsourced random access (URA) in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framework segregates the data into two components, namely, preamble and coding parts, with the former being tree-coded and the latter LDPC-coded. By leveraging the dual sparsity of the equivalent channel across both codeword and delay domains (CD and DD), we develop a message-passing-based sparse Bayesian learning algorithm, combined with belief propagation and mean field, to iteratively estimate DD channel responses, TO, and delay profiles. Furthermore, by jointly leveraging the observations among multiple slots, we establish a novel graph-based algorithm to iteratively separate the superimposed channels and compensate for the phase rotations. Additionally, the proposed algorithm is applied to the flat fading scenario to estimate both TO and CFO, where the channel and offset estimation is enhanced by leveraging the geometric characteristics of the signal constellation. Extensive simulations reveal that the proposed algorithm achieves superior performance and substantial complexity reduction in both channel and offset estimation compared to the codebook enlarging-based counterparts, and enhanced data recovery performances compared to state-of-the-art URA schemes.

Asynchronous MIMO-OFDM Massive Unsourced Random Access with Codeword Collisions

Abstract

This paper investigates asynchronous multiple-input multiple-output (MIMO) massive unsourced random access (URA) in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framework segregates the data into two components, namely, preamble and coding parts, with the former being tree-coded and the latter LDPC-coded. By leveraging the dual sparsity of the equivalent channel across both codeword and delay domains (CD and DD), we develop a message-passing-based sparse Bayesian learning algorithm, combined with belief propagation and mean field, to iteratively estimate DD channel responses, TO, and delay profiles. Furthermore, by jointly leveraging the observations among multiple slots, we establish a novel graph-based algorithm to iteratively separate the superimposed channels and compensate for the phase rotations. Additionally, the proposed algorithm is applied to the flat fading scenario to estimate both TO and CFO, where the channel and offset estimation is enhanced by leveraging the geometric characteristics of the signal constellation. Extensive simulations reveal that the proposed algorithm achieves superior performance and substantial complexity reduction in both channel and offset estimation compared to the codebook enlarging-based counterparts, and enhanced data recovery performances compared to state-of-the-art URA schemes.
Paper Structure (19 sections, 1 theorem, 60 equations, 14 figures, 1 table, 2 algorithms)

This paper contains 19 sections, 1 theorem, 60 equations, 14 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Proposed Scheme
  4. Encoding Scheme
  5. JADCE-MP-SBL Algorithm
  6. Forward Message Passing
  7. Backward Message Passing
  8. GB-CR$^2$ Algorithm
  9. Application To Flat Fading Channels
  10. Performance Analysis
  11. Analysis of CE Performance
  12. Analysis of GB-CR$^2$ Algorithm
  13. Computational Complexity Analysis
  14. Numerical Results
  15. Flat Fading Channel Scenario
  16. ...and 4 more sections

Key Result

Proposition 1

Given a path $\hat{p}_k$ restored from Eq. equ-33, for its arbitrary nodes ${v}_{i,n_i}$ and ${v}_{j,n_j}$, we consider $\hat{p}_k$ is valid if where $\widehat{\underline{\mathbf{X}}}_i \triangleq \widehat{\mathbf{X}}^i_{{v}_{i,n_i}}\slash q^i(\hat{\epsilon}_k)$ and $\widehat{\underline{\mathbf{X}}}_j \triangleq \widehat{\mathbf{X}}^j_{{v}_{j,n_j}}\slash q^j(\hat{\epsilon}_k)$. Based on this c

Figures (14)

  • Figure 1: A realization of the overall encoding scheme and the proposed receiver design.
  • Figure 2: Demonstration of the sparsity of the matrix $\mathbf{X}^t$.
  • Figure 3: FG representation of Eq. \ref{['equ-11']}.
  • Figure 4: An example of the proposed graph with $K_a=6$ and four stages, with both valid (solid blue) and invalid (dotted orange) edges. The overlapped circles correspond to the collision cases.
  • Figure 5: An example of the implementation process of GB-CR$^2$ algorithm with four stages and five paths.
  • ...and 9 more figures

Theorems & Definitions (4)

  • Proposition 1
  • Remark 1
  • Remark 2
  • Remark 3