Ultra-reliable Low-latency Communication Requirements Research Articles

Multi-Agent Deep Reinforcement Learning Based Resource Allocation for Ultra-Reliable Low-Latency Internet of Controllable Things

As a promising technology in the 5G era, the artificial intelligence (AI) enabled Internet of controllable things (IoCT) is expected to be an integral part of heterogeneous networks (HetNets) in the future. However, the realization of ultra-reliable low-latency communications (URLLC) in IoCT communications underlaid HetNet has stringent quality of service (QoS) requirements, resulting in unprecedented challenges for existing wireless resource allocation methods. In this paper, we first describe a cellular HetNet model with uplink IoCT communications, then formulate a dynamic mixed-integer nonlinear programming (MINLP) resource allocation problem for maximizing the long-term average energy efficiency under URLLC requirements including reliability, latency, and transmission rate. To solve the problem, we propose a decentralized MADRL-based resource allocation algorithm with a decentralized partially observable Markov decision process (dec-POMDP) and a mixed-centralized-decentralized (MCD) framework to address the partial observability and the scalability issues, respectively. In addition, we design a reward function featuring the objective decomposition, baseline-guided scaling, and QoS violation penalty so that the agents are coordinated. Extensive experiments demonstrate the convergence, scalability, and robustness of the proposed algorithm. Besides, the proposed algorithm substantially outperforms conventional resource allocation methods and different agent communication mechanisms in terms of maximizing energy efficiency.

Next-Generation URLLC With Massive Devices: A Unified Semi-Blind Detection Framework for Sourced and Unsourced Random Access

This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with a massive number of devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference information (RI) is embedded in the access signal to resolve the inherent ambiguities incurred by the unknown CSI. At the receiver, we further develop a successive interference cancellation-based semi-blind detection scheme, where a bilinear generalized approximate message passing algorithm is utilized for joint channel and signal estimation (JCSE), while the embedded RI is exploited for ambiguity elimination. Particularly, a rank selection approach and a RI-aided initialization strategy are incorporated to reduce the algorithmic computational complexity and to enhance the JCSE reliability, respectively. Besides, four enabling techniques are integrated to satisfy the stringent latency and reliability requirements of massive URLLC. Numerical results demonstrate that the proposed semi-blind detection framework offers a better scalability-latency-reliability tradeoff than the state-of-the-art detection schemes dedicated to sourced or unsourced RA.

Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC: Centralized and Federated DRL Approaches

The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.

Efficient Decoders for Short Block Length Codes in 6G URLLC

This article reviews the potential channel decoding techniques for ultra-reliable low-latency communications (URLLC). URLLC is renowned for its stringent requirements including ultra-reliability, low end-to-end transmission latency, and packet-size flexibility. These requirements exacerbate the difficulty of the physical-layer design, particularly for the channel coding and decoding schemes. To satisfy the requirements of URLLC, decoders must exhibit superior error-rate performance and low decoding complexity. Also, it is desired that decoders be universal to accommo-date various coding schemes. This article provides a comprehensive review and comparison of different candidate decoding techniques for URLLC in terms of their error-rate performance and computational complexity for structured and random short codes. We further make recommendations of the decoder selections and suggest several potential research directions.

Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

Ultra-reliable low-latency communication (URLLC) has been introduced in the 5th Generation (5G) radios for mission-critical applications that demand strict reliability and latency traffic to guarantee the rapid delivery of short packets (up to 1 ms) with a success probability rate of 99.999%. The challenging reliability and latency requirements of URLLC have significant impact on the air-interface design, especially on the Hybrid Automatic Repeat reQuest (HARQ) mechanism. This study focuses on satisfying link latency requirements by reducing the delay that arises in the presence of the HARQ operation. To this end, we propose a Swift HARQ protocol empowered by machine learning techniques to estimate the decodability of a packet early enough within its maximum number of allowable retransmission attempts. This can allow the transmitter to react faster by dropping the non-decodable packets, or activating the repetition mode where parts of the HARQ feedback can be omitted. As shown through system-level simulations, the proposed model achieves a delay reduction of more than 50% compared to the traditional HARQ, and increases the system throughput by up to 40% when multiple HARQ retransmissions are required.

Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches

Massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) are two key services in fifth-generation (5G) mobile wireless networks. These networks have been developed with extremely high service quality requirements: scalability for mMTC and reliability with low latency for URLLC. Fifth-generation network slicing will play a key role in supporting the distinct requirements of various services. Software-defined networking (SDN), a promising technology for network softwarization, physically separates the network control plane from the data plane by centrally controlling switches with an SDN controller. However, control channel bottleneck and processing delays due to this centralized control may reduce the scalability, reliability, and security of SDN. This paper proposes an SDN framework with programming protocol–independent packet processor (P4) switches (SDNPS), and defines a packet format containing in-band network telemetry data to simultaneously support heavy Internet of Things and URLLC traffic in 5G network slices. The method both satisfies the requirements of mMTC and URLLC and alleviates the load on the SDN controller. P4 is an advanced switch interface technology that provides enhanced stateful forwarding and reveals a persistent state on the SDN data plane. To demonstrate the superiority of SDNPS, simulations are performed on conventional SDNs and SDNPS. SDNPS outperforms the other schemes in terms of average throughput, packet loss ratio, and packet delay for both the mMTC and URLLC network slices.

A Hybrid Scheme of MCS Selection and Spectrum Allocation for URLLC Traffic under Delay and Reliability Constraints.

The Ultra-Reliable Low-Latency Communication (URLLC) is expected to be an important feature of 5G and beyond networks. Supporting URLLC in a resource-efficient manner demands optimal Modulation and Coding Scheme (MCS) selection and spectrum allocation. This paper presents a study on MCS selection and spectrum allocation to support URLLC. The essential idea is to establish an analytical connection between the delay and reliability requirements of URLLC data transmission and the underlying MCS selection and spectrum allocation. In particular, the connection factors in fundamental aspects of wireless data communication include channel quality, coding and modulation, spectrum allocation and data traffic characteristics. With this connection, MCS selection and spectrum allocation can be efficiently performed based on the delay and reliability requirements of URLLC. Theoretical results in the scenario of a 5G New Radio system are presented, where the Signal-to-Noise Ratio (SNR) thresholds for adaptive MCS selection, data-transmission rate and delay, as well as spectrum allocation under different configurations, including data duplication, are discussed. Simulation results are also obtained and compared with the theoretical results, which validate the analysis and its efficiency.

Self-Adaptive Ordered Statistics Decoder for Finite Block Length Raptor Codes Toward URLLC

Rateless codes can adapt to the wireless channel conditions without accurate channel state information (CSI) at the transmitter side, avoiding CSI feedback and retransmission, and thus are a promising channel coding approach to meet the stringent requirements of ultrareliable low-latency communications (uRLLCs). This article investigates a self-adaptive ordered statistics decoder (S-OSD) scheme for finite block length nonbinary Raptor code (NBRC). Aiming at minimizing the decoding complexity, some strategies for our S-OSD scheme are designed, including the segmentation rules of most reliable basis, the generating and discarding rules of test error patterns, and the stop criteria, respectively. In addition, an upper bound of block error rates (BLERs) for the NBRC under OSD is derived, which can be used to estimate the number of NBRC symbols required to successfully decode the input information via the S-OSD. Simulation results show that the complexity of our S-OSD scheme is greatly reduced comparing to the existing OSD schemes, while achieving very low BLER in the short block length regime.

Enabling URLLC Applications Through Reconfigurable Intelligent Surfaces: Challenges and Potential

Next-generation communication systems, including the sixth generation (6G) cellular systems, are expected to support a wide range of new ultra-reliable low-latency communications (URLLC). These emerging applications strictly demand high data rates and/or massive connectivity besides their strict reliability and latency requirements. To enable such URLLC applications alongside the enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC), it is imperative to develop spectrally efficient methods, and non-conventional technologies and networking architectures. To this end, reconfigurable intelligent surfaces (RISs) have recently emerged as a key promising technology to enhance the capabilities of 6G wireless networks. The RIS technology offers a wide range of advantages, including offering a low-cost and energy-efficient solution for controlling the propagation environments. We study the potential of deploying RIS in 6G networks with an emphasis on accommodating the coexistence of URLLC and eMBB traffic. Specifically, we demonstrate through several case studies the potential of integrating RIS into 6G networks with the objective of meeting the requirements of URLLC without compromising the performance of eMBB users. Motivated by this and the fact that the RIS technology has not been fully investigated, we propose a number of relevant research directions and highlight what kind of challenges such directions may entail. We also elaborate on how such challenges, once tackled, may lead to realizing the full potential of integrating RIS into future wireless networks.

HARQ Optimization for PDCP Duplication-Based 5G URLLC Dual Connectivity

Packet duplication (PD) with dual connectivity (DC) was newly introduced in the 5G New Radio (NR) specifications to meet the stringent ultra reliable low latency communication (URLLC) requirements. PD technology uses duplicated packets in the packet data convergence protocol (PDCP) layer that are transmitted via two different access nodes (ANs) to the user equipment (UE) in order to enhance the reliability performance. However, PD can result in unnecessary retransmissions in the lower layers since the hybrid automatic retransmission request (HARQ) operation is unaware of the transmission success achieved through the alternate DC link to the UE. To overcome this issue, in this paper, a novel duplication-aware retransmission optimization (DRO) scheme is proposed to reduce the resource usage induced by unnecessary HARQ retransmissions. The proposed DRO scheme can minimize the average channel use while satisfying the URLLC requirements. The proposed DRO scheme derives the optimal HARQ retransmission attempts for different ANs by solving a nonlinear integer programming (NLIP) problem. The performance of the proposed DRO scheme was evaluated using MATLAB simulation and is compared to the existing 5G HARQ support schemes. The simulations results show that the proposed DRO scheme can provide a 14.71% and 15.11% reduced average channel use gain compared to the selective data duplication upon failure (SDUF) scheme and latency-aware dynamic multi-connectivity algorithm (LADMA) scheme, respectively, which are the existing 5G PD schemes that use HARQ.

Optimization of ultra-reliable low-latency communication systems

The increasingly ubiquitous applications of Ultra-Reliable Low-Latency Communications (URLLC) require innovative solutions that can only be achieved through a flexible communication system such as the The Fifth Generation (5G) New Radio (NR). Though a number of Grant-Free (GF) and Grant-based (GB) transmission schemes have been proposed to meet URLLC requirements there is a lack of work on the design of these systems. In this paper, we develop an analytical model that enables optimization of the performance of the URLLC systems with heterogeneous traffic. We consider uplink communications and assume that multiple copies of the same packet will be transmitted, utilizing both dedicated and shared resources. The network considered in this study consists of users with both periodic and sporadic traffic. Users in the network are grouped into classes according to their packet generation probabilities. While all users access shared resources through GF transmission, access to dedicated resources is done in two different ways depending on the user's packet generation rate, namely, Periodic Scheduling (PS) and GB scheduling. Although recent studies were disinclined towards the GB scheme due to its high latency, we show that the exploitation of 5G NR's new scalable numerology results in significant reductions to GB's latency, making it suitable for the URLLC use case. Following this latency examination, we present probabilistic expressions representing the reliability of our proposed solution. Then, we formulate an optimization problem in terms of minimizing the required bandwidth or maximizing the traffic capacity while satisfying reliability requirements. The performance of the system is maximized through optimal allocation of the resources among the transmission schemes and classification of the user classes as PS or GB based.

URLLC Key Technologies and Standardization for 6G Power Internet of Things

The 6G Power Internet of Things (PIoT) is considered to be the key enabler for smart grid. In 6G PIoT, ultra-reliable low-latency communications (URLLC) can provide deterministic service guarantee for applications with stringent latency and reliability requirements. In recent years, the concept, use cases, and key technologies of URLLC have been thoroughly investigated by 3GPP, while the characteristics of PIoT are not considered in the standardization yet. Confronted with such a problem, this article analyzes the application scenarios and service requirements of URLLC for 6G PIoT, summarizes the key technologies and standardization of 5G/B5G URLLC and end-to-end URLLC guarantee technologies, and outlines the possible evolution directions of URLLC in 6G.

A Novel Joint Index Modulation and Physical Layer Network Coding Mechanism for Beyond 5G

In beyond 5G communications, besides energy efficiency (EE) and spectral efficiency (SE), latency and reliability are among the main metrics that extreme ultra-reliable low-latency communications (URLLC) applications must fulfill. Although new techniques are sought after to meet the crunching requirements of URLLC, combining existing phys-ical-layer techniques have become a compelling, attractive, and cost saving approach to achieving the same goal. In this article, we describe a novel mechanism combining physical layer network coding (PNC) and index modulation (IM) to achieve a balance between SE and EE for URLLC applications beyond 5G. PNC has the potential to increase SE because it leverages on interference from many transmissions occurring at the same time. Although fewer resources are required for IM the capacity gain is the same as if all transmission resources are used, and as a result, both EE and SE can increase simultaneously. Our simulation results show the feasibility of combining these two key physical-lay-er techniques, affirming the complementary role this approach will play in meeting the performance KPIs of URLLC beyond 5G.

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

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.

Multi-Gigabit CO-OFDM System over SMF and MMF Links for 5G URLLC Backhaul Network

The 5G cellular network aims at providing three major services: Massive machine-type communication (mMTC), ultra-reliable low-latency communications (URLLC), and enhanced-mobile-broadband (eMBB). Among these services, the URLLC and eMBB require strict end-to-end latency of 1 ms while maintaining 99.999% reliability, and availability of extremely high data rates for the users, respectively. One of the critical challenges in meeting these requirements is to upgrade the existing optical fiber backhaul network interconnecting the base stations with a multigigabit capacity, low latency and very high reliability system. To address this issue, we have numerically analyzed 100 Gbit/s coherent optical orthogonal frequency division multiplexing (CO-OFDM) transmission performance over 400 km single-mode fiber (SMF) and 100 km of multi-mode fiber (MMF) links. The system is simulated over optically repeated and non-repeated SMF and MMF links. Coherent transmission is used, and the system is analyzed in a linear and non-linear regime. The system performance is quantified by bit error ratio (BER). Spectrally efficient and optimal transmission performance is achieved for 400 km SMF and 100 km MMF link. The results designate that MMF links can be employed beyond short reach applications by using them in the existing SMF infrastructure for long haul transmission. In particular, the proposed CO-OFDM system can be efficiently employed in 5G backhaul network. The multi-gigabit capacity and lower BER of the proposed system makes it a suitable candidate especially for the eMBB and URLLC requirements for 5G backhaul network.

Impact of Correlated Fading on Multi-Connectivity

Multi-connectivity (MC) is regarded as one of the key features that meet the requirements of ultra-reliable low-latency communications for 5th generation networks, as it provides multiple diversity branches. Recently, we evaluated the decoding reliability of various MC setups and different combining algorithms by analyzing the resulting outage probabilities. In this work, we are interested in how much the performance is affected by correlated fading. Specifically, we analyze the outage probability of MC systems with joint decoding reception operating over correlated quasi-static Rayleigh and Nakagami-m fading channels. Our main contributions are as follows: (i) deriving the exact outage probability in integral form and the asymptotic outage probability at high signal-to-noise ratio (SNR) in closed form; (ii) deriving the correlation loss, which quantifies the extra SNR required under correlated fading as compared with the independent scenario; and (iii) evaluating frame-error rates of quasi-cyclic low-density parity-check codes by Monte-Carlo simulations. Our results show that the correlation loss is marginal for a low to moderate level of correlation, whereas the diversity gain is not affected by correlation whatsoever. Furthermore, we find that the correlation loss is independent of the degrees of freedom encapsulated by the Nakagami-m fading parameter.

Performance Analysis of NOMA Enabled Fog Radio Access Networks

In the fifth generation (5G) era, mobile networks will provide diversified services such as enhanced Mobile BroadBand (eMBB) and ultra-reliable low-latency communication (URLLC). Furthermore, the 5G architecture will be fog-like, enabling a functional split of network functionalities between cloud and edge nodes. In this article, we propose a non-orthogonal multiple access (NOMA)-enabled fog-cloud structure in a novel density-aware fog-based radio access networks (F-RAN) to tackle different aspects of high and low-density regions, in order to meet the heterogeneous requirements of eMBB and URLLC traffic. A framework of two different problem formulations is considered to cater the high throughput and low-latency requirements in a high and low-density mode respectively. In the first problem, we study the joint caching placement and association strategy aiming at minimizing the average delay. To deal with the first problem, we apply McCormick envelopes and Lagrange partial relaxation method to transform it into three convex sub-problems, which is then solved by proposing a distributed algorithm. The second problem is to jointly optimize transmission mode selection, subchannel assignment and power allocation to maximize the sum data rate of all fog user equipments (F-UEs) while satisfying fronthaul capacity and fog-computing access point (F-AP) power constraints. Moreover, for given transmission mode selection and subchannel assignment, the optimal power allocation is derived in a closed-form expression. Simulation results are provided to validate the effectiveness of the proposed NOMA-enabled F-RAN framework and reveal that the ultra-low latency and high throughput can be achieved by properly utilizing the available resources.

Analyzing Grant-Free Access for URLLC Service

5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grant-based (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this article. Specifically, we present a spatio-temporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.

Design and performance evaluation of contention-based transmission schemes for URLLC services

We investigate in this paper uplink transmission schemes for 5G Ultra-Reliable Low Latency Communications (URLLC) traffic. The URLLC class of services has been defined for applications requiring extremely stringent latency and reliability. We show that, in systems with episodic traffic and many users compared with the number of transmission resources, randomly transmitting multiple copies of a packet allows to meet the URLLC requirements. We first develop an analytical model for the packet loss rate for a contention based multiple transmission scheme where replica positions are assigned randomly and show that it allows to meet the performance requirements of URLLC for small enough user activity rate. We then propose an advanced replication scheme where positions for the different replicas are pre-allocated to users so that users have pairwise-distinct positions. We show that this advanced scheme achieves very high reliability with low resource consumption.

Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems

This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.