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Hierarchical Sparse Vector Transmission for Ultra Reliable and Low Latency Communications

Yanfeng Zhang, Xi'an Fan, Jinkai Zheng, Xiaoye Jing, Weiwei Yang, Xu Zhu

TL;DR

The paper tackles URLLC's stringent latency and reliability by introducing Hierarchical Sparse Vector Coding (HSVC), which maps all users' common information into the indices of non-zero sections and embeds private user data into dedicated non-zero blocks with distinct lengths. The encoding uses a two-layer structure with random spreading, enabling simultaneous transmission of common and private streams, while decoding leverages a two-stage pipeline: common information recovered via BOMP and private information recovered via MBOMP with SIC and circular-shift exploitation. The approach yields substantial BLER improvements over state-of-the-art SVT schemes and reduces transmission latency by avoiding redundant common-information transmission, with demonstrated gains in simulations under Rayleigh fading. This work offers a practical path to more reliable and lower-latency multi-user downlink URLLC and suggests avenues for extension to massive MIMO regimes.

Abstract

Sparse vector transmission (SVT) is a promising candidate technology for achieving ultra-reliable low-latency communication (URLLC). In this paper, a hierarchical SVT scheme is proposed for multi-user URLLC scenarios. The hierarchical SVT scheme partitions the transmitted bits into common and private parts. The common information is conveyed by the indices of non-zero sections in a sparse vector, while each user's private information is embedded into non-zero blocks with specific block lengths. At the receiver, the common bits are first recovered from the detected non-zero sections, followed by user-specific private bits decoding based on the corresponding non-zero block indices. Simulation results show the proposed scheme outperforms state-of-the-art SVT schemes in terms of block error rate.

Hierarchical Sparse Vector Transmission for Ultra Reliable and Low Latency Communications

TL;DR

The paper tackles URLLC's stringent latency and reliability by introducing Hierarchical Sparse Vector Coding (HSVC), which maps all users' common information into the indices of non-zero sections and embeds private user data into dedicated non-zero blocks with distinct lengths. The encoding uses a two-layer structure with random spreading, enabling simultaneous transmission of common and private streams, while decoding leverages a two-stage pipeline: common information recovered via BOMP and private information recovered via MBOMP with SIC and circular-shift exploitation. The approach yields substantial BLER improvements over state-of-the-art SVT schemes and reduces transmission latency by avoiding redundant common-information transmission, with demonstrated gains in simulations under Rayleigh fading. This work offers a practical path to more reliable and lower-latency multi-user downlink URLLC and suggests avenues for extension to massive MIMO regimes.

Abstract

Sparse vector transmission (SVT) is a promising candidate technology for achieving ultra-reliable low-latency communication (URLLC). In this paper, a hierarchical SVT scheme is proposed for multi-user URLLC scenarios. The hierarchical SVT scheme partitions the transmitted bits into common and private parts. The common information is conveyed by the indices of non-zero sections in a sparse vector, while each user's private information is embedded into non-zero blocks with specific block lengths. At the receiver, the common bits are first recovered from the detected non-zero sections, followed by user-specific private bits decoding based on the corresponding non-zero block indices. Simulation results show the proposed scheme outperforms state-of-the-art SVT schemes in terms of block error rate.
Paper Structure (9 sections, 16 equations, 3 figures)

This paper contains 9 sections, 16 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Proposed HSVC Scheme
  3. HSVC Encoding
  4. Random Spreading of HSVC
  5. HSVC Decoding
  6. Common Information Decoding
  7. Private Information Decoding
  8. Simulation Results
  9. Conclusions

Figures (3)

  • Figure 1: Diagram of HSVC encoding process with $N=36$, $U=2$, $S=4$, $D=9$, $L_{1}=4$ and $L_{2}=2$.
  • Figure 2: BLER performance of different schemes for (a) 2 users, and (b) 4 users.
  • Figure 3: BLER performance: (a) as a function of subcarrier number $M$ with $\text{SNR}=2$ dB, and (b) comparison of different schemes for various modulation orders.