Ultra-reliable low-latency communication (URLLC) has been introduced in 5G new radio for new applications that have strict reliability and latency requirements such as augmented/virtual reality, industrial automation and autonomous vehicles. The first full set of the physical layer design of 5G release, Release 15, was finalized in December 2017. It provided a foundation for URLLC with new features such as flexible sub-carrier spacing, a sub-slot-based transmission scheme, new channel quality indicator, new modulation and coding scheme tables, and configured-grant transmission with automatic repetitions. The second 5G release, Release 16, was finalized in December 2019 and allows achieving improved metrics for latency and reliability to support new use cases of URLLC. A number of new features such as enhanced physical downlink (DL) control channel monitoring capability, new DL control information format, sub-slot physical uplink (UL) control channel transmission, sub-slot-based physical UL shared channel repetition, enhanced mobile broadband and URLLC inter-user-equipment multiplexing with cancellation indication and enhanced power control were standardized. This article provides a detailed overview of the URLLC features from 5G Release 15 to Release 16 by describing how these features allow meeting URLLC target requirements in 5G networks. The ongoing Release 17 targets further enhanced URLLC operation by improving mechanisms such as feedback, intra-user-equipment multiplexing and prioritization of traffic with different priority, support of time synchronization and new quality of service related parameters. In addition, a fundamental feature targeted in URLLC Release 17 is to enable URLLC operation over shared unlicensed spectrum. The potential directions of URLLC research in unlicensed spectrum in Release 17 are presented to serve as a bridge from URLLC in licensed spectrum in Release 16 to URLLC in unlicensed spectrum in Release 17.

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An Overview of Physical Layer Design for Ultra-Reliable Low-Latency Communications in 3GPP Releases 15, 16, and 17

A 5G campus network is a 5G network for the users affiliated with the campus organization, e.g., an industrial campus, covering a prescribed geographical area. A 5G campus network can operate as a so-called 5G non-standalone (NSA) network (which requires 4G Long-Term Evolution (LTE) spectrum access) or as a 5G standalone (SA) network (without 4G LTE spectrum access). 5G campus networks are envisioned to enable new use cases, which require cyclic delay-sensitive industrial communication, such as robot control. We design a rigorous testbed for measuring the one-way packet delays between a 5G end device via a radio access network (RAN) to a packet core with sub-microsecond precision as well as for measuring the packet core delay with nanosecond precision. With our testbed design, we conduct detailed measurements of the one-way download (downstream, i.e., core to end device) as well as one-way upload (upstream, i.e., end device to core) packet delays and losses for both 5G SA and 5G NSA hardware and network operation. We also measure the corresponding 5G SA and 5G NSA packet core processing delays for download and upload. We find that typically 95% of the SA download packet delays are in the range from 4–10 ms, indicating a fairly wide spread of the packet delays. Also, existing packet core implementations regularly incur packet processing latencies up to 0.4 ms, with outliers above one millisecond. Our measurement results inform the further development and refinement of 5G SA and 5G NSA campus networks for industrial use cases. We make the measurement data traces publicly available as the IEEE DataPort 5G Campus Networks: Measurement Traces dataset (DOI 10.21227/xe3c-e968).

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5G Campus Networks: A First Measurement Study

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective $5^{th}$ generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The $3^{rd}$ generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

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URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-to-end behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.

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A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

Enabling ultra-reliable low-latency communication (URLLC) with stringent requirements for transmitting data packets (e.g., 99.999% reliability and 1 millisecond latency) presents considerable challenges in uplink transmissions. For each packet transmission over dynamically allocated network radio resources, the conventional random access protocols are based on a request-grant scheme. This induces excessive latency and necessitates reliable control signalling, resulting overhead. To address these problems, grant-free (GF) solutions are proposed in the fifth-generation (5G) new radio (NR). In this paper, an overview and vision of the state-of-the-art in enabling GF URLLC are presented. In particular, we first provide a comprehensive review of NR specifications and techniques for URLLC, discuss underlying principles, and highlight impeding issues of enabling GF URLLC. Furthermore, we briefly explain two key phenomena of massive multiple-input multiple-output (mMIMO) (i.e., channel hardening and favorable propagation) and build several deep insights into how celebrated mMIMO features can be exploited to enhance the performance of GF URLLC. Moving further ahead, we examine the potential of cell-free (CF) mMIMO and analyze its distinctive features and benefits over mMIMO to resolve GF URLLC issues. Finally, we identify future research directions and challenges in enabling GF URLLC with CF mMIMO.A new version of the paper has been updated on 21/08/2021

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Enabling Grant-Free URLLC: An Overview of Principle and Enhancements by Massive MIMO

To meet the strict requirements of Ultra-Reliable Low-Latency Communication in the uplink, grant-free uplink transmission has been specified, allowing the UE to transmit data in a random-access fashion without first transmitting a scheduling request and then waiting for a uplink grant from the gNB. To further increase the reliability, these grant-free uplink transmissions can be repeated without waiting for HARQ feedback from the gNB. However, these repetitions have to happen within a certain interval to avoid a confusion in HARQ IDs of different HARQ processes. When a UE starts transmitting late in the interval, it, therefore, can not exploit all the possible repetitions and thus reliability and latency decrease. In this paper, a scheme based on reserved resources is proposed to ensure the number of repetitions in a specific period. The size of each reserved resource is optimized depending on its position so as to reduce resource consumption. The scheme evaluated by theoretical analysis and numerical results shows its benefits to system performance.

Optimal reserved resources to ensure the repetitions in Ultra-Reliable Low-Latency Communication Uplink Grant-free transmission

The 3rd Generation Partnership Project (3GPP) has defined Ultra-Reliable Low-Latency Communication (URLLC) as one of the main objectives of 5G development to satisfy the applications with stringent requirements of latency and reliability. Uplink (UL) configured-grant (CG) transmission where the user equipment (UE) transmits a packet without scheduling request (SR) and UL grant is standardized by 3GPP Release 15 to reduce latency. The UE also transmits automatically a configured number of repetitions without feedback from the base station (gNB) to increase reliability.Nevertheless, the repetitions are not allowed to transmit outside the hybrid automatic repeat request (HARQ) process containing the first repetition. It might cause a smaller number of transmitted repetitions than configuration that is harmful to the performance of URLLC.This paper uses the reserved resources where the UEs can transmit the repetitions outside the original HARQ process until the configured number is reached. The scheme is developed further when the gNB is equipped with a successive interference cancellation (SIC) receiver so it can decode multiple repetitions of the different UEs in the same reserved resource. The use of reserved resources ensures the performance of UL CG transmission while SIC receiver minimizes the reserved resource’s consumption. The numerical results show a higher transmission reliability and lower reserved resource consumption of the proposed scheme compared to the related works.

Enhancing URLLC Uplink Configured-grant Transmissions

Ultra-reliable low-latency communication (URLLC) is one of the main services in 5G New Radio (NR) to serve the applications with the strict requirements of latency and reliability. Increase of mobile traffic and bandwidth requirements for new applications have resulted in a shortage of licensed spectrum so that even URLLC services are deemed to be running over the unlicensed spectrum. This may require a significant re-design of channel access, transmission and reception procedures over the unlicensed spectrum and is currently being investigated in The 3rd Generation Partnership Project (3GPP) Release 17.This paper focuses on the channel access mechanism for URLLC services over the unlicensed spectrum. It provides an analytical analysis of current channel access procedures, listen before talk (LBT). LBT states in random duration channel access are modeled through Markov chains which help characterize the closed form expressions for average channel access time. These expressions are evaluated using the parameters from currently standardized channel access priority classes. The evaluation shows the total inability of several current channel access classes to support URLLC services over the unlicensed spectrum even under low load conditions.The insights gained from this analysis lead the proposal of new channel access priority classes where new special classes are introduced for URLLC services. The results are provided demonstrating the improved performance of URLLC services in the unlicensed spectrum with the proposed changes in the channel access procedures.

Channel Access Enhancements in unlicensed spectrum for NR URLLC transmissions

In Ultra-Reliable Low-Latency Communication (URLLC), the user (UE) can be configured to transmit in grant-free/configured-grant (GF/CG) resources for uplink (UL) transmission that does not require the UE to transmit scheduling request (SR) and receive UL grant to reduce latency. In addition, the UE is also configured to transmit automatically a specific number of repetitions without waiting feedback. However, these repetitions are only allowed to carry out in an interval with period P to avoid identity (ID) confusion in a Hybrid automatic repeat request (HARQ) process. Thereby, there is a chance that the UE cannot transmit all repetitions as configured if data arrives late and it leads to a drop of reliability. Two approaches are proposed in this paper to cope with this problem. This first approach requires an usage of the explicit HARQ feedback structure and the second one is related to an additional SR transmitted by the UE in parallel with data. The numerical results show the benefit of these two methods in increasing system performance in case of less configured repetitions made when they help the system to avoid or reduce packet loss due to Demodulation Reference Signal (DMRS) miss-detection.

Trung-Kien Le

Papers

An Overview of Physical Layer Design for Ultra-Reliable Low-Latency Communications in 3GPP Releases 15, 16, and 17

Optimal reserved resources to ensure the repetitions in Ultra-Reliable Low-Latency Communication Uplink Grant-free transmission

Enhancing URLLC Uplink Configured-grant Transmissions

Channel Access Enhancements in unlicensed spectrum for NR URLLC transmissions

Strategies to meet the configured repetitions in URLLC Uplink Grant-Free transmission