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Pilot-Free Unsourced Random Access Via Dictionary Learning and Error-Correcting Codes

Zhentian Zhang, Jian Dang, Zaichen Zhang, Liang Wu, Bingcheng Zhu, Lei Wang

TL;DR

The paper tackles unsourced random access for massive device populations by eliminating pilot signals and leveraging a shared sparse codebook. It proposes a dictionary-learning plus error-correcting-code (DL-ECC) receiver that jointly detects active codewords and restores information within a sparsity-driven frame, all at a multi-antenna BS. Key contributions include a sparse-frame URA protocol, a collision-resolution mechanism, and an unknown-$K_a$ estimator, with numerical validation under practical multi-user MIMO settings. The approach offers energy-efficient, low-latency access for mMTC without pilot overhead, providing practical guidance on atom-number selection and active-user estimation for system design.

Abstract

Massive machine-type communications (mMTC) or massive access is a critical scenario in the fifth generation (5G) and the future cellular network. With the surging density of devices from millions to billions, unique pilot allocation becomes inapplicable in the user ID-incorporated grant-free random access protocol. Unsourced random access (URA) manifests itself by focusing only on unwrapping the received signals via a common codebook. In this paper, we propose a URA protocol for a massive access cellular system equipped with multiple antennas at the base station. The proposed scheme encompasses a codebook enabling construction of sparse transmission frame, a receiver equipped with dictionary learning and error-correcting codes and a collision resolution strategy for the collided codeword. Discrepant to the existing schemes with necessary overhead for preamble signals, no overhead or pre-defined pilot sequences are needed in the proposed scheme, which is favorable for energy-efficient transmission and latency reduction. Numerical results verify the viability of the proposed scheme in practical massive access scenario.

Pilot-Free Unsourced Random Access Via Dictionary Learning and Error-Correcting Codes

TL;DR

The paper tackles unsourced random access for massive device populations by eliminating pilot signals and leveraging a shared sparse codebook. It proposes a dictionary-learning plus error-correcting-code (DL-ECC) receiver that jointly detects active codewords and restores information within a sparsity-driven frame, all at a multi-antenna BS. Key contributions include a sparse-frame URA protocol, a collision-resolution mechanism, and an unknown- estimator, with numerical validation under practical multi-user MIMO settings. The approach offers energy-efficient, low-latency access for mMTC without pilot overhead, providing practical guidance on atom-number selection and active-user estimation for system design.

Abstract

Massive machine-type communications (mMTC) or massive access is a critical scenario in the fifth generation (5G) and the future cellular network. With the surging density of devices from millions to billions, unique pilot allocation becomes inapplicable in the user ID-incorporated grant-free random access protocol. Unsourced random access (URA) manifests itself by focusing only on unwrapping the received signals via a common codebook. In this paper, we propose a URA protocol for a massive access cellular system equipped with multiple antennas at the base station. The proposed scheme encompasses a codebook enabling construction of sparse transmission frame, a receiver equipped with dictionary learning and error-correcting codes and a collision resolution strategy for the collided codeword. Discrepant to the existing schemes with necessary overhead for preamble signals, no overhead or pre-defined pilot sequences are needed in the proposed scheme, which is favorable for energy-efficient transmission and latency reduction. Numerical results verify the viability of the proposed scheme in practical massive access scenario.
Paper Structure (20 sections, 15 equations, 10 figures, 4 algorithms)

This paper contains 20 sections, 15 equations, 10 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contributions and Organization
  4. System model
  5. Uplink Transmission Model
  6. Dictionary Learning
  7. DL-ECC-based receiver design
  8. Frame Structure and Sparsity Construction
  9. Joint Active Device And information Detection
  10. Collision Resolution
  11. Atom Number Optimization
  12. Resolving The Unknown $K_{a}$
  13. NUMERICAL RESULTS
  14. Active Codeword Identification
  15. Choices of Atom Number
  16. ...and 5 more sections

Figures (10)

  • Figure 1: Illustration of sporadic uplink transmission in mMTC system with a MIMO base station.
  • Figure 2: All coding procedures of the proposed scheme before transmission are illustrated, incorporating ECC encoding, modulation mapping and codeword-oriented sparse spreading.
  • Figure 3: Illustration of the procedures at the receiver via DL and ECC methods.
  • Figure 4: The impact of atom number selection on activity detection and computational complexity. (a) Ratio of successful active codeword detection versus atom number with $K_{tot}$=1000, $K_{a}$=100, $M$=64, $L$=1600, $S$=$L$/40, SNR=10dB. (b) Computational complexity versus atom number with $K_{tot}$=1000, $M$=64, $L$=1600, $S$=$L$/40.
  • Figure 5: (a) Ratio of successful active codeword detection versus frame sparsity with $K_{tot}$=1000, $K_{a}$=100, $L$=1600, SNR=10dB under different antennas number at BS . (b) Ratio of successful active codeword detection versus frame sparsity with $K_{tot}$=1000, $M$=64, $L$=1600, SNR=10dB under different atom number selection.
  • ...and 5 more figures