Sparse Vector Coding for Ultra-Reliable and Low Latency Communications

TL;DR

This paper introduces Sparse Vector Coding (SVC), a novel transmission scheme designed for ultra-reliable, low-latency 5G communications, leveraging compressed sensing principles to support short packet transmission with high reliability.

Contribution

The paper proposes SVC, a new short packet transmission method that transforms information into sparse vectors and applies compressed sensing, outperforming LTE schemes for URLLC.

Findings

SVC achieves ultra-reliable transmission with 10^-5 error rate within 1 ms.

Numerical evaluations show SVC outperforms LTE and LTE-Advanced schemes.

SVC effectively supports short packet URLLC requirements.

Abstract

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10^-5 packet error rate within 1 ms transmission period. Since the current wireless transmission scheme designed to maximize the coding gain by transmitting capacity achieving long codeblock is not relevant for this purpose, a new transmission scheme to support URLLC is required. In this paper, we propose a new approach to support the short packet transmission, called sparse vector coding (SVC). Key idea behind the proposed SVC technique is to transmit the information after the sparse vector transformation. By mapping the information into the position of nonzero elements and then transmitting it after the random spreading, we obtain an underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations and performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced scheme.