Optimal Resource Allocation in URLLC for Real-Time Wireless Control Systems

In real-time wireless control systems, ultra-reliable and low-latency communication (URLLC) is critical for the connection between the remote controller and its control objective. Since both transmission delay and packet loss can lead to control performance loss, our goal is to optimize control performance by jointly considering control and URLLC constraints in this paper. To achieve this goal, we formulate an optimal problem to minimize control cost by optimizing the packet drop and wireless resource allocation. To solve the problem, we analyze the relationship between communication and control. Then, based on the relationship, we decompose the original problem into two subproblems: 1) an optimal packet-drop problem to minimize control cost and 2) an optimal resource allocation problem to minimize communication packet error. Finally, the corresponding solutions for each subproblem can be obtained. Compared with the traditional method only considering the communication aspect, the proposed packet-drop and resource allocation method shows remarkable performance gain in terms of control cost.

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB sum rate.

In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.

The coexistence of Ultra-Reliable and Low-Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) brings significant challenges for service pairing and resource allocation in beyond fifth-generation (B5G) wireless networks. To meet the reliability requirement of URLLC services and improve the fairness of eMBB services, we first develop a supervised learning-based resource allocation policy for eMBB services. Then, a two-phase resource allocation framework is proposed for URLLC services: 1) eMBB/URLLC service pairing, and 2) URLLC resource allocation. In the first phase, matching theory pairs eMBB and URLLC services for better fairness. In the second phase, URLLC resource allocation policy is optimized by a constrained unsupervised learning algorithm. Simulation results show that our proposed framework can achieve better trade-offs among fairness, throughput, and reliability compared with two existing baselines. For example, a dynamic proportional fairness algorithm can meet the reliability requirement of URLLC when its average packet arrival rate is below 0.7 packets/mini-slot. The proposed algorithm can support URLLC services with an average packet arrival rate of 1.6 packets/mini-slot

Carrier frequency offset (CFO) and channel equalization are two critical problems for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems in ultra-reliable and low latency communication (URLLC). In this paper, we propose a semi-blind precoding aided structure that includes two CFO estimation approaches and an independent component analysis (ICA) based equalization scheme for MIMO OFDM systems in URLLC, requiring no pilots. We design a non-redundant balanced precoding strategy, killing two birds with one stone, where reference signals are superimposed into source signals to simultaneously allow CFO estimation and ambiguity elimination in the ICA-equalized signals. The proposed precoding-aided CFO estimation approach performs by maximizing a cost function formulated via the cross-correlations between the reference signal and the received signal. We further propose a low-complexity closed-form CFO estimation approach, by transforming the formulated cost function into a new expression. To maximize bit error rate (BER) performance, particle swarm optimization (PSO) is employed to perform the joint optimization of precoding constant and the number of OFDM blocks for CFO estimation, while avoiding exhaustive search. The proposed semi-blind precoding-aided structure provides a trade-off between performance, complexity and spectral efficiency for MIMO OFDM systems in URLLC.

5G is envisaged to support diverse use cases and applications. Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC) have been identified as two main communication scenarios in 5G. Unlike eMBB service which demands high data rate, URLLC has stringent latency and reliability requirement. Due to the sporadic and stochastic characteristics of URLLC traffic, an URLLC transmission may suddenly occurs during the transmission of eMBB data. It is sometimes necessary to transmit URLLC data on the resources occupied by eMBB. Thus in this paper, we investigate the dynamic downlink multiplexing for eMBB and URLLC data on the same time/frequency resource. Specifically, we present a dynamical puncturing scheme, which allows URLLC data occupy part of the radio resource that has been scheduled to ongoing eMBB transmission. The puncturing will degrade the performance of the eMBB traffic, therefore we introduce the signal space diversity (SSD) scheme into the proposed dynamic puncturing scheme. The decoding performance of eMBB data over the fading channel is thus improved through the component interleaving and rotation modulation. Furthermore, we derive the optimal rotation angel that can achieve the optimal puncturing performance by maximizing the average mutual information (AMI). The link-level simulation results show that the proposed scheme outperforms existing strategy, and the performance gain is around 1dB when the code rate is set as $2/3$. This observation also validates the theoretical analysis based on the AMI.

In 6G networks, integrated sensing and communication (ISAC) is envisioned as a key technology that enables wireless systems to perform joint sensing and communication using shared hardware, antenna(s) and spectrum. ISAC designs facilitate emerging applications such as digital twins, smart cities and autonomous driving. Such applications also demand ultra-reliable and low-latency communication (URLLC), a feature that was first introduced in 5G and is expected to be further enhanced in 6G. Thus, an ISAC-enabled URLLC system can prioritize critical and time-sensitive targets and ensure information delivery under strict latency and reliability constraints. We propose a bi-static multiple-input multiple-output (MIMO) ISAC system to detect the arrival of URLLC messages and prioritize their delivery. In this system, a dual-function base station (BS) communicates with a user equipment (UE) and a sensing receiver (SR) is deployed to collect echo signals reflected from a target of interest. The BS regularly transmits messages of enhanced mobile broadband (eMBB) services to the UE. During each eMBB transmission, if the SR senses the presence of a target of interest, it immediately triggers the transmission of an additional URLLC message. To reinforce URLLC transmissions, we propose a dirty-paper coding (DPC)-based technique that mitigates the interference of both eMBB and sensing signals. To decode the eMBB message, we consider two approaches for handling the URLLC interference: treating interference as noise (TIN) and successive interference cancellation (SIC). For this system, we formulate the rate-reliability-detection trade-off in the finite blocklength (FBL) regime by evaluating the communication rate of the eMBB transmissions, the reliability of the URLLC transmissions and the probability of the target detection. Our numerical analysis show that our proposed DPC-based ISAC scheme significantly outperforms power-sharing based ISAC and traditional time-sharing schemes. In particular, it achieves higher eMBB transmission rate while satisfying both URLLC and sensing constraints.

The ultra-reliable and low-latency communication (URLLC) is the key driver of the current 5G new radio standardization. URLLC encompasses sporadic and small-payload transmissions that should be delivered within extremely tight radio latency and reliability bounds, i.e., a radio latency of 1 ms with 99.999% success probability. However, such URLLC targets are further challenging in the 5G dynamic time division duplexing (TDD) systems, due to the switching between the uplink and downlink transmission opportunities and the additional inter-cell cross-link interference (CLI). This paper presents a system level analysis of the URLLC outage performance within the 5G new radio flexible TDD systems. Specifically, we study the feasibility of the URLLC outage targets compared to the case with the 5G frequency division duplexing (FDD), and with numerous 5G design variants. The presented results therefore offer valuable observations on the URLLC outage performance in such deployments, and hence, introducing the state-of-the-art flexible-FDD technology.

Ultra-reliable and low-latency communication (URLLC) is one of the key applications in fifth generation (5G) cellular networks, which requires extremely high reliability (~99.9999%) and low latency (<; 1 ms). In this paper, the energy efficiency (EE) of multi-user multicarrier amplify-and-forward (AF) networks is maximized under short packet transmission. Accordingly, we formulate an energy-efficient resource allocation problem to jointly optimize the transmit power, subcarrier pairing and allocation, and error probability with finite block-length codes subject to the constraints of the decoding error probability of each user pair, subcarrier pairing and allocation and total transmission power. The formulated problem is non-convex and hence difficult to solve. We analyze the structure of the problem and hence convert it into a convex problem which is approximately equivalent to the original one. An efficient algorithm is also proposed which is capable of producing a near-optimal solution. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC, by showing the impact of various system parameters on EE.

Efficient resource allocation is important to realize ultra-reliability and low latency in the ultra-reliable and low-latency communication (URLLC) system with massive users. In this paper, resource allocation in a multi-user URLLC system is studied to improve the system reliability. A latency-constrained packet dropping rate (LCPDR) which jointly captures packet losses induced by transmission errors, timeout violations and buffer overflows during packet delivery, is used to comprehensively measure the end-to-end reliability. A joint optimization problem of subchannel, time slot and power allocation is formulated to minimize the LCPDR with the maximum delay constraint. To reduce complexity and resource consumption when there are a large number of users, user grouping is proposed and the problem is reformulated as two subproblems on two timescales, i.e., user grouping on a long-timescale and resource allocation within each group on a short-timescale. To solve the subproblems, a three-tier hierarchical deep reinforcement learning (HDRL) architecture is proposed, in which a novel event-triggered dynamic regrouping mechanism is embedded into the long-timescale user grouping and short-timescale intra-group resource allocation tiers to dynamically migrate abnormal users between groups. A distance-aware proximal policy optimization (PPO) algorithm named H-PPO-IL based on HDRL and imitation learning (IL) is proposed, in which IL is used to pretrain the PPO agents and accelerate the convergence. Simulation results show that the proposed algorithm has a lower LCPDR and a faster convergence speed, which is more effective compared with benchmark algorithms.

This paper presents novel ultrareliable and low-latency communication (URLLC) techniques for URLLC services, such as Tactile Internet services. Among typical use cases of URLLC services are teleoperation, immersive virtual reality, cooperative automated driving, and so on. In such URLLC services, new kinds of traffic such as haptic information including kinesthetic information and tactile information need to be delivered in addition to high-quality video and audio traffic in traditional multimedia services. Furthermore, such a variety of traffic has various characteristics in terms of packet sizes and data rates with a variety of requirements of latency and reliability. Furthermore, some traffic may occur in a sporadic manner but requires reliable delivery of packets of medium to large sizes within a low latency, which is not supported by current state-of-the-art wireless communication systems and is very challenging for future wireless communication systems. Thus, to meet such a variety of tight traffic requirements in a wireless communication system, novel technologies from the physical layer to the network layer need to be devised. In this paper, some novel physical layer technologies such as waveform multiplexing, multiple-access scheme, channel code design, synchronization, and full-duplex transmission for spectrally efficient URLLC are introduced. In addition, a novel performance evaluation approach, which combines a ray-tracing tool and system-level simulation, is suggested for evaluating the performance of the proposed schemes. Simulation results show the feasibility of the proposed schemes providing realistic URLLC services in realistic geographical environments, which encourages further efforts to substantiate the proposed work.

Inspired by the new and dominating traffic services - ultra-reliable and low latency communications (URLLC), finite blocklength coding (FBC) has been developed to support delay and error-rate bounded quality-of-services (QoS) provisioning for time-sensitive wireless applications by using short-packet data communications. On the other hand, the age of information (AoI) has recently emerged as a new dimension of QoS performance metric in terms of the freshness of updated information. Since the status updates normally consist only of a small number of information bits but warrant ultra-low latency, exploring AoI in the finite blocklength regime creates another promising solution for supporting URLLC services. However, how to efficiently integrate and implement the above new techniques for statistical delay and error-rate bounded QoS provisioning in the finite blocklength regime has neither been well understood nor thoroughly studied. To overcome these challenges, we propose the AoI-driven statistical delay and error-rate bounded QoS provisioning schemes which leverage the AoI technique as a key QoS performance metric to efficiently support URLLC in the finite blocklength regime. First, we build up the AoI-metric based modeling frameworks in the finite blocklength regime. Second, we apply the stochastic network calculus (SNC) to characterize the upper-bounded peak AoI violation probability. Third, we jointly optimize the peak AoI violation probability and ∊-effective capacity and characterize their tradeoff in supporting statistical delay and error-rate bounded QoS provisioning for URLLC. Finally, we conduct the extensive simulations to validate and evaluate our developed schemes in the finite blocklength regime.

Future mobile communication systems need to support wide variety of new services and applications. In order to support these, ITU-R introduced new types of use cases. One such use case, called ultra reliable and low latency communication (URLLC), concerns the reduction of latency down to a millisecond level while ensuring the reliability of the transmission. In case of uplink transmission, supporting the stringent latency requirement of URLLC is quite challenging due to a time-consuming and complicated handshaking process. In the time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is directed to the downlink. In this paper, we propose a new grant signaling scheme, referred to as channel-aware sparse signaling (CASS), to achieve a low latency access in the TDD-based URLLC systems. Key idea of CASS is to map the grant information into a small number of subcarriers and then decode it using a small number of early received samples. From the numerical evaluations, we demonstrate that the proposed CASS scheme achieves significant reduction in access latency over the conventional LTE-TDD systems.

5G mobile networks are envisioned to support ultra-reliable and low latency communications (URLLC), which is targeted for a packet transmission with 99.999% reliability and a user plane latency down to 0.5 ms. In this paper, a reliability model and a latency model for URLLC traffic are presented, along with some deterministic analysises providing the reliability requirements for each physical channels and the feasible nu-merologies for URLLC transmission. Meanwhile, system-level simulation is conducted with two metrics including the 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile downlink and uplink Signal-to-Interference-and-Noise Ratio (SINR) values and the percentage of users satisfying the reliability and latency requirements. Furthermore, a resource selection based structure for physical uplink control channel (PUCCH) that satisfies the deterministic analysis is proposed and evaluated with link-level simulation, which shows the reliability can be met at the 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile uplink SINR value provided by system-level simulation.

The coexistence of human type communications (HTC) with an ever-increasing number of machine type communications (MTC) is a challenge that the forthcoming Fifth Generation (5G) network is claimed to tackle. Ultra-Reliable and Low Latency Communications (URLLC) are among the most demanding services that 5G network will support. Also referred to as mission-critical services, URLLC often requires group communications. The evolved Multimedia Broadcast and Multicast Services (eMBMS) architecture, currently specified by 3GPP to support group-based services in cellular networks, needs a revision to deliver future mission-critical services. In this paper, we analyze the performance of different machine-type multicast service (MtMS) scenarios and we compare three subgroup based approaches for multicast data delivery to MTC devices with limited channel bandwidth. Achieved simulation results provide useful guidelines for the design of multicasting schemes able to support delay-sensitive applications in 5G networks.