Dynamic Wireless QoS Analysis for Real-Time Control in URLLC

One of the major goals of ultra-reliable and low-latency communication (URLLC) is to enable real-time wireless control systems. However, it is challenging to use URLLC throughout the control process since a huge amount of wireless resource is needed to maintain the rigorous quality-of-service (QoS) in URLLC, i.e, ultra reliability and low latency. In this paper, our goal is to discuss that whether the extreme high QoS in URLLC leads to better control performance than low QoS during the control process. This is expected to provide a guideline on the usage of the URLLC throughout the control process dynamically. Specifically, we first investigate the relationship between the URLLC QoS and control performance. Then, we discuss the effect of different communication QoS on the control performance. Our results show that the rigorous QoS in URLLC and a low QoS can be used dynamically throughout the control process with high system performance

Dynamic Wireless QoS Analysis for Real-Time Control in URLLC

One of the major goals of ultra-reliable and low-latency communication (URLLC) is to enable real-time wireless control systems. However, it is challenging to use URLLC throughout the control process since a huge amount of wireless resource is needed to maintain the rigorous quality-of-service (QoS) in URLLC, i.e, ultra reliability and low latency. In this paper, our goal is to discuss that whether the extreme high QoS in URLLC leads to better control performance than low QoS during the control process. This is expected to provide a guideline on the usage of the URLLC throughout the control process dynamically. Specifically, we first investigate the relationship between the URLLC QoS and control performance. Then, we discuss the effect of different communication QoS on the control performance. Our results show that the rigorous QoS in URLLC and a low QoS can be used dynamically throughout the control process with high system performance

Optimal Transmission of Short-Packet Communications in Multiple-Input Single-Output Systems

We design the optimal transmission strategy, which maximizes the average achievable data rate of the multiple-input single-output system that adopts short-packet communications. In this system, the N A -antenna access point (AP) transmits to the single-antenna user with finite blocklength T after estimating the AP-user channel via downlink training and uplink feedback. For this system, we determine the optimal allocation of the finite resource (e.g., the total transmit power and a finite number of symbol periods) for downlink training, uplink feedback, and data transmission to maximize the average data rate. Specifically, we derive an approximate closed-form lower bound on the average data rate, an explicit result for the optimal number of symbol periods for downlink training, an easy-to-implement method to find the optimal number of symbol periods for uplink feedback, and a simple expression for the optimal power allocation between data transmission and downlink training. By using numerical results, we demonstrate the effectiveness of our analytical solutions and examine the impact of system parameters, e.g., N A and T, on the optimal strategy.This work was supported by the Australian Research Council under Discovery Project Grant DP180104062

Robust URLLC Packet Scheduling of OFDM Systems

In this paper, we consider the power minimization problem of joint physical resource block (PRB) assignment and transmit power allocation under specified delay and reliability requirements for ultra-reliable and low-latency communication (URLLC) in downlink cellular orthogonal frequency-division multiple-access (OFDMA) system. To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS). The formulated problem is a combinatorial and mixed-integer nonconvex problem and is difficult to tackle. Through techniques of slack variables introduction, the first-order Taylor approximation and reweighted $\ell_1$-norm, we approximate it by a convex problem and the successive convex approximation (SCA) based iterative algorithm is proposed to yield sub-optimal solutions. Numerical results provide some insights into the impact of channel estimation error, user number, the allowable maximum delay and packet error probability on the required system sum power