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

5G will be supporting new services that have remarkably higher requirements than LTE 4G and Ultra-reliable and low-latency communication (URLLC) is one of those emerged categories. Although various techniques have been proposed to improve the data reliability, there has been a gap in how to improve the reliability of control/scheduling information pointing to the scheduled data. In this paper, we propose an intelligent combining of retransmissions of physical downlink control channel (PDCCH) and the physical downlink data channel (PDSCH). In the proposed scheme, the downlink control information (DCI) on PDCCH already indicates the location of a potential retransmission of the corresponding PDSCH. Moreover, the retransmitted DCI can be combined with the first transmission so that resource consumption and latency are reduced compared to the conventional scheme. Theoretical calculations and simulation results show a decrease of resource consumption.

Control and data channel combining in Ultra-Reliable Low-Latency Communication

For the fifth generation (5G) wireless network technology, the most important design goals were improved metrics of reliability and latency, in addition to network resilience and flexibility so as to best adapt the network operation for new applications such as augmented virtual reality, industrial automation and autonomous vehicles. That led to design efforts conducted under the subject of ultra-reliable low-latency communication (URLLC). The technologies integrated in Release 15 of the 3rd Generation Partnership Project (3GPP) finalized in December 2017 provided the foundation of physical layer design for URLLC. New features such as numerologies, new transmission schemes with sub-slot interval, configured grant resources, etc., were standardized to improve mainly the latency aspects. 
5G evolution in Release 16, PHY version finalized in December 2019, allows achieving improved metrics for latency and reliability by standardizing a number of new features. This article provides a detailed overview of the URLLC features from 5G Release 15 and Release 16, describing how these features permit URLLC operation in 5G networks.

An overview of physical layer design for Ultra-Reliable Low-Latency Communications in 3GPP Release 15 and Release 16.

In uplink (UL) transmission, the Ultra-Reliable Low-Latency Communication (URLLC) users (UEs) might be assigned the grant-free (GF)/configured-grant (CG) periodic resources to transmit data straightaway instead of sending scheduling request (SR) and receiving UL grant. However, when these resources are not in use by the URLLC traffic, the base station (called gNB) can dynamically schedule the Enhanced Mobile Broadband (eMBB) UEs to transmit in the GF resources to increase the resource efficiency. This may lead to potential collision and detrimental QoS for URLLC as some of the URLLC UEs may become active and try to use the same resource assigned to an eMBB UE. In this paper, a two-step strategy containing an overlap indication and explicit Hybrid automatic repeat request (HARQ) feedback is proposed to improve URLLC performance in multiplexing with eMBB. Besides the explicit HARQ feedback structure, a scheme with an additional SR is also presented. Simulation results show that these two schemes help achieving URLLC requirements by reducing error probability due to Demodulation Reference Signal (DMRS) miss-detection while allowing better resource efficiency.

Improving Ultra-Reliable Low-Latency Communication in multiplexing with Enhanced Mobile Broadband in grant-free resources

Ultra-reliable low-latency communication (URLLC) is one of the new service categories specified by The 3rd Generation Partnership Project (3GPP) that will be used for the applications with the strict reliability and latency requirements. To satisfy these requirements in Release 16, URLLC downlink (DL) transmission can be scheduled in the semi-persistent scheduling (SPS) resources to reduce control overhead and a retransmission if necessary is triggered by hybrid automatic repeat request (HARQ) feedback transmitted in uplink (UL) resources. However, in time division duplex (TDD) mode, HARQ feedback is dropped when there are no available UL resources for physical uplink control channel (PUCCH) in the indicated slot/sub-slot because that slot/sub-slot contains DL symbols in semi-static TDD configuration or flexible symbols updated to DL symbols in dynamic TDD configuration. Dropping the feedback results in a degradation of DL SPS quality of service (QoS) and may not be acceptable for URLLC based services and applications. This paper provides a scheme to guarantee the transmission of HARQ feedback and potential data retransmission when there is UL-DL slot/sub-slot conflict at the indicated feedback resource. The scheme comprises a dynamic indication of feedback resource without using an associated downlink control information (DCI). An acknowledgement (ACK)-only feedback protocol is proposed to best suit the scenario in question. The combination of dynamic indication of feedback resource and ACK-only feedback structure guarantees higher reliability and brings flexibility to the transmission of HARQ feedback as confirmed by the simulations.

Feedback Enhancements for Semi-Persistent Downlink Transmissions in Ultra-Reliable Low-Latency Communication

Ultra-reliable low-latency communication (URLLC) in 5G New Radio has been originally defined only for licensed spectrum. However, due to new use cases in the Industry 4.0 scenarios, URLLC operation is currently being extended to unlicensed spectrum in the ongoing Release 17 of the 3rd Generation Partnership Project. Although in such controlled environments we can guarantee the absence of any other technology sharing the channel on a long-term basis, the uncertainty of obtaining channel access through load based equipment (LBE) or frame based equipment (FBE) can impede with the latency requirements of URLLC. In FBE, the transmitters can be prioritized to support data with different requirements and have lower energy consumption and latency compared to LBE with a big contention window size. In this paper we analyze the performance of FBE in an unlicensed controlled environment through a Markov chain. Based on this analysis, we propose two schemes to improve the URLLC performance in FBE: The first scheme allows the transmitters to use multiple fixed frame period (FFP) configurations while the second scheme configures the FFP's starting point of each transmitter based on its priority. The simulations show the benefits of these schemes compared to the URLLC transmission of existing schemes.

Trung-Kien Le

Papers

Control and data channel combining in Ultra-Reliable Low-Latency Communication

An overview of physical layer design for Ultra-Reliable Low-Latency Communications in 3GPP Release 15 and Release 16.

Improving Ultra-Reliable Low-Latency Communication in multiplexing with Enhanced Mobile Broadband in grant-free resources

Feedback Enhancements for Semi-Persistent Downlink Transmissions in Ultra-Reliable Low-Latency Communication

Frame based equipment channel access enhancements in NR unlicensed spectrum for URLLC transmissions.