Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Sparse Code Transceiver Design for Unsourced Random Access with Analytical Power Division in Gaussian MAC

Zhentian Zhang, Mohammad Javad Ahmadi, Jian Dang, Kai-Kit Wong, Zaichen Zhang, Christos Masouros

TL;DR

This work tackles unsourced random access over a Gaussian MAC for massive machine-type connectivity by formulating URA as a coding problem using sparse codes. It introduces an analytically derived power-division (PD) strategy within a TIN-SIC framework and a slotted structure to reduce detector load and remove reliance on exhaustive power allocation. The proposed approach yields about a $2.8$ dB improvement in $E_b/N_0$ compared to identical-channel-code baselines and matches or closely approaches the performance of schemes with more advanced codes under optimal PD. While the gains are promising for sparse-code URA, reaching the theoretical limits will require further advances in channel code design and decoding techniques, especially under high user density.

Abstract

In this work, we discuss the problem of unsourced random access (URA) over a Gaussian multiple access channel (GMAC). To address the challenges posed by emerging massive machine-type connectivity, URA reframes multiple access as a coding-theoretic problem. The sparse code-oriented schemes are highly valued because they are widely used in existing protocols, making their implementation require only minimal changes to current networks. However, drawbacks such as the heavy reliance on extrinsic feedback from powerful channel codes and the lack of transmission robustness pose obstacles to the development of sparse codes. To address these drawbacks, a novel sparse code structure based on a universally applicable power division strategy is proposed. Comprehensive numerical results validate the effectiveness of the proposed scheme. Specifically, by employing the proposed power division method, which is derived analytically and does not require extensive simulations, a performance improvement of approximately 2.8 dB is achieved compared to schemes with identical channel code setups.

Sparse Code Transceiver Design for Unsourced Random Access with Analytical Power Division in Gaussian MAC

TL;DR

This work tackles unsourced random access over a Gaussian MAC for massive machine-type connectivity by formulating URA as a coding problem using sparse codes. It introduces an analytically derived power-division (PD) strategy within a TIN-SIC framework and a slotted structure to reduce detector load and remove reliance on exhaustive power allocation. The proposed approach yields about a dB improvement in compared to identical-channel-code baselines and matches or closely approaches the performance of schemes with more advanced codes under optimal PD. While the gains are promising for sparse-code URA, reaching the theoretical limits will require further advances in channel code design and decoding techniques, especially under high user density.

Abstract

In this work, we discuss the problem of unsourced random access (URA) over a Gaussian multiple access channel (GMAC). To address the challenges posed by emerging massive machine-type connectivity, URA reframes multiple access as a coding-theoretic problem. The sparse code-oriented schemes are highly valued because they are widely used in existing protocols, making their implementation require only minimal changes to current networks. However, drawbacks such as the heavy reliance on extrinsic feedback from powerful channel codes and the lack of transmission robustness pose obstacles to the development of sparse codes. To address these drawbacks, a novel sparse code structure based on a universally applicable power division strategy is proposed. Comprehensive numerical results validate the effectiveness of the proposed scheme. Specifically, by employing the proposed power division method, which is derived analytically and does not require extensive simulations, a performance improvement of approximately 2.8 dB is achieved compared to schemes with identical channel code setups.
Paper Structure (12 sections, 1 theorem, 14 equations, 4 figures)

This paper contains 12 sections, 1 theorem, 14 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Motivations
  4. Contributions
  5. Content Structures
  6. Transceiver Design
  7. Signal Model and Encoder Design
  8. Iterative Decoding
  9. TIN-SIC Power Division
  10. Numerical Results
  11. Conclusion
  12. Proof of Theorem \ref{['theorem1']}

Key Result

Theorem 1

Consider a URA system with $K_0$ users, each transmitting $B_0$ bits of information over $n_0$ channel uses. Let $\gamma$ be the ratio of each user's average per-channel-use power to the noise variance. An approximation of the PUPE for the TIN-SIC scheme is given by: where $\mathbb{P}(\xi_{n,k})=Q\left({(C-R_c)}/{\sqrt{V_{dis}/n_0}}\right)^{\bar{K}_k}$, $\bar{K}_k=K_0-k+1$, $C=0.5\log_2(1+\alpha_

Figures (4)

  • Figure 1: Illustrations of encoding procedures under GMAC.
  • Figure 2: Illustration of iterative decoding of the proposed GMAC scheme: The inner loop passes statistic informaiton to conduct joint data and pattern detection; The outer loop conducts SIC to reduce interference.
  • Figure 3: Comparison of the minimum-required $E_b/N_0$ (dB) between the proposed scheme with/without TIN-SIC PD power division (PD), and ODMA-based schemes using NR-LDPC and polar code ODMA_Polar, RA code ODMA_RA, and non-binary LDPC ODMA_NB_LDPC.
  • Figure 4: Performance of the minimum required energy-per-bit for a 0.05 PUPE. Chronologically, the benchmarks include achievability boundPolyanskiy, CCSCCS, RS-PolarRS-Polar, RS-LDPCRS-LDPC, RS+optimal power divisionPD_Mohamod, SKPSKP, CRC-BMSTCRC-BMST, ODMA+RAODMA_RA, ODMA+non-binary LDPCODMA_NB_LDPC, dynamic CCSdynamic_CCS.

Theorems & Definitions (2)

  • Theorem 1
  • proof