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Eigenvalue Based Active User Enumeration for Grant-Free Access Under Carrier Frequency Offsets

Takanori Hara

TL;DR

This letter investigates a grant-free non-orthogonal multiple access (GF-NOMA) system in the presence of carrier frequency offsets and proposes two schemes for enumerating active users in such a GF-NOMA system, which is equivalent to estimating the sparsity level.

Abstract

This paper investigates a grant-free non-orthogonal multiple access (GF-NOMA) system in the presence of carrier frequency offsets. We propose two schemes for enumerating active users in such a GF-NOMA system, which is equivalent to estimating the sparsity level. Both schemes utilize a short common pilot and the eigenvalues of the sample covariance matrix of the received signal. The two schemes differ in their treatment of noise variance: one exploits known variance information, while the other is designed to function without this knowledge. Simulation results demonstrate the effectiveness of the proposed schemes in terms of the normalized root-mean-squared error.

Eigenvalue Based Active User Enumeration for Grant-Free Access Under Carrier Frequency Offsets

TL;DR

This letter investigates a grant-free non-orthogonal multiple access (GF-NOMA) system in the presence of carrier frequency offsets and proposes two schemes for enumerating active users in such a GF-NOMA system, which is equivalent to estimating the sparsity level.

Abstract

This paper investigates a grant-free non-orthogonal multiple access (GF-NOMA) system in the presence of carrier frequency offsets. We propose two schemes for enumerating active users in such a GF-NOMA system, which is equivalent to estimating the sparsity level. Both schemes utilize a short common pilot and the eigenvalues of the sample covariance matrix of the received signal. The two schemes differ in their treatment of noise variance: one exploits known variance information, while the other is designed to function without this knowledge. Simulation results demonstrate the effectiveness of the proposed schemes in terms of the normalized root-mean-squared error.
Paper Structure (10 sections, 23 equations, 4 figures, 2 tables)

This paper contains 10 sections, 23 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Conventional AUE Schemes
  4. Proposed AUE Schemes
  5. Preliminaries
  6. Eig-sum: Proposed AUE With Knowledge of $\sigma^{2}_{\mathrm{z}}$
  7. Eig-diff: Proposed AUE Without Knowledge of $\sigma^{2}_{\mathrm{z}}$
  8. Complexity Comparison
  9. Numerical Results
  10. Conclusion

Figures (4)

  • Figure 1: performance versus $\epsilon_{\max}$.
  • Figure 2: performance versus $M$.
  • Figure 3: performance versus .
  • Figure 4: performance versus $K$.