Short-Packet Communications: Transmission Strategies and Power Control Policies Design
Creators: name: Li, Chunhui
Publisher: name: The Australian National University
Year: 2021

description: Ultra-reliable and low-latency communications (URLLC) has been envisaged as the enabling paradigm to support real-time communications with stringent requirements on latency and reliability. The realization of URLLC will bring life-changing applications, e.g., smart manufacturing for Industrial 4.0, autonomous networked vehicles, and remote surgery, to human society. Notably, these applications typically require a target decoding error probability to be less than $10^{-7}$ within a latency being lower than 1 ms. Such strictly low latency imposes an unprecedented restriction on the size of packets. As such, short-packet communications (SPC) has been proposed as the fundamental method to reduce the latency for URLLC. This thesis aims to gain a comprehensive understanding of SPC for URLLC. Specifically, this thesis investigates and addresses the following issues: 1) how to design SPC with limited channel estimation overhead in SPC (Chapters 2 and 3), 2) how to improve the design of SPC to reduce the communication latency for URLLC (Chapter 4), and 3) how to design secure SPC for URLLC under statistical quality-of-service (QoS) constraints (Chapter 5). The contributions made in this thesis are summarized as follows: First, we investigate two different channel training strategies for SPC in Chapter 2. We study the requirement on channel reciprocity to activate uplink channel training, instead of downlink channel training, to achieve a higher data rate for the downlink transmission from a multi-antenna base station to a single-antenna user. We show the necessity and benefits of activating uplink channel training for SPC with multiple transmit antennas. Then, we further study the optimal SPC strategy in a multiple-input single-output system in Chapter 3. To maximize the average achievable data rate, 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. Second, to reduce communication latency, in Chapter 4, we propose to use channel inversion power control (CIPC) with channel reciprocity to eliminate the overhead of channel state information (CSI) feedback, as well as achieve one-way URLLC where only the transmission in one direction requires ultra reliability and low latency. Based on channel reciprocity, the proposed CIPC schemes guarantee that the power of the received signal used to decode the information is a constant value \emph{Q}, by varying the transmit signal and power, which relaxes the assumption of knowing CSI at the user. We derive new analytical expressions for the packet loss probability of the proposed CIPC schemes, based on which we determine a closed interval and a convex set for optimizing \emph{Q} in CIPC with imperfect and perfect channel reciprocities, respectively. Finally, we study how to realize secure SPC subject to a statistical QoS requirement and an average power constraint in Chapter 5. We compare the secure transmission rates of short packets in different scenarios (i.e., with/without eavesdropper's instantaneous CSI and with/without channel estimation errors). To find the optimal power control policy that maximizes the effective secrecy throughout under QoS and power constraints, we apply an unsupervised deep learning method with low complexity to address constrained functional optimization problems, which do not have a closed-form solution in general. To provide more insights and demonstrate the effectiveness of unsupervised deep learning, we derive the closed-form expression for the optimal policy in a special case. This thesis advances our understanding of the fundamental performance of SPC for URLLC. It also provides guidelines to assist URLLC designers to solve important problems on how to fully explore the advantages of SPC in practical wireless URLLC systems.