Skip to main content
QUICK REVIEW

[Paper Review] Uplink Grant-Free Random Access Solutions for URLLC services in 5G New Radio

Nurul Huda Mahmood, Renato Abreu|arXiv (Cornell University)|Apr 11, 2019
Advanced Wireless Communication Technologies11 references4 citations
TL;DR

This paper proposes two advanced grant-free random access schemes for URLLC in 5G NR to enhance reliability and resource efficiency. The first uses dedicated initial transmission followed by blind retransmissions over shared resources; the second combines grant-free access with non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) at the receiver. Both schemes significantly improve performance, with NOMA enabling a normalized load of over 1.5 users per slot at a 10⁻⁵ packet loss rate.

ABSTRACT

The newly introduced ultra-reliable low latency communication service class in 5G New Radio depends on innovative low latency radio resource management solutions that can guarantee high reliability. Grant-free random access, where channel resources are accessed without undergoing assignment through a handshake process, is proposed in 5G New Radio as an important latency reducing solution. However, this comes at an increased likelihood of collisions resulting from uncontrolled channel access, when the same resources are preallocated to a group of users. Novel reliability enhancement techniques are therefore needed. This article provides an overview of grant-free random access in 5G New Radio focusing on the ultra-reliable low latency communication service class, and presents two reliability-enhancing solutions. The first proposes retransmissions over shared resources, whereas the second proposal incorporates grant-free transmission with non-orthogonal multiple access with overlapping transmissions being resolved through the use of advanced receivers. Both proposed solutions result in significant performance gains, in terms of reliability as well as resource efficiency. For example, the proposed non-orthogonal multiple access scheme can support a normalized load of more than 1.5 users/slot at packet loss rates of ~10^{-5} - a significant improvement over the maximum supported load with conventional grant-free schemes like slotted-ALOHA.

Motivation & Objective

  • Address the reliability and latency challenges in URLLC services within 5G New Radio (NR) by improving grant-free (GF) random access.
  • Overcome the collision risk inherent in shared-resource GF access, which compromises reliability in high-load scenarios.
  • Enhance resource efficiency and reduce transmission latency compared to conventional GF schemes like slotted-ALOHA and K-repetition.
  • Enable support for high-load URLLC traffic, particularly in industrial automation and V2X applications, through novel transmission and receiver design.
  • Explore the feasibility of advanced receiver techniques, such as SIC and joint decoding, to resolve interference from overlapping GF transmissions.

Proposed method

  • Proposes a hybrid GF scheme where the initial transmission uses dedicated resources, followed by blind retransmissions over shared radio resources to improve reliability.
  • Introduces a GF-NOMA scheme that allows multiple users to transmit simultaneously over the same time-frequency resources using power-domain multiplexing.
  • Employs successive interference cancellation (SIC) at the base station receiver to decode overlapping transmissions, treating interference as decodable rather than noise.
  • Applies low-rate channel coding across multiple transmission blocks to improve diversity gain and reliability in the presence of interference.
  • Uses selection combining and Chase combining as diversity combining techniques to enhance performance in the presence of fading and interference.
  • Evaluates system performance under varying traffic loads, focusing on outage probability, latency, and spectral efficiency.

Experimental results

Research questions

  • RQ1Can a hybrid grant-free transmission scheme combining dedicated initial access with shared retransmissions significantly reduce latency while maintaining high reliability?
  • RQ2To what extent can non-orthogonal multiple access (NOMA) with advanced receivers improve the supported load in grant-free random access for URLLC?
  • RQ3How does the use of successive interference cancellation (SIC) and joint decoding improve reliability in overloaded GF-NOMA systems compared to conventional GF schemes?
  • RQ4What is the maximum normalized user load (users per slot) achievable with GF-NOMA at a target outage probability of 10⁻⁵?
  • RQ5How do different combining techniques (e.g., selection combining, Chase combining) affect the reliability and latency performance of GF-NOMA systems?

Key findings

  • The proposed GF-NOMA scheme with SIC and joint decoding supports a normalized load of over 1.5 users per slot at a packet loss rate of approximately 10⁻⁵, a significant improvement over conventional GF schemes.
  • The hybrid retransmission scheme reduces transmission latency by approximately 60% compared to traditional feedback-based retransmission schemes, achieving a 23% gain in spectral efficiency over robust single-shot transmission.
  • Successive interference cancellation (SIC) enables reliable detection of overlapping GF transmissions, allowing the system to operate reliably even under high load conditions.
  • Chase combining and selection combining provide measurable performance gains, with joint decoding further improving outage performance in the NOMA-based scheme.
  • The combination of dedicated initial transmission and shared retransmissions reduces the latency tail compared to reactive GF schemes, improving end-to-end latency for URLLC applications.
  • The proposed schemes outperform standard 3GPP Release-15 GF solutions (e.g., K-repetition and reactive GF) in both reliability and resource efficiency, especially under high traffic loads.

Better researchstarts right now

From reading papers to final review, dramatically reduce your research time.

No credit card · Free plan available

This review was created by AI and reviewed by human editors.