Packet Structure and Receiver Design for Low Latency Wireless Communications with Ultra-Short Packets Byungju Lee ,Member, IEEE , Sunho Park ,Member, IEEE , David J

796 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 66, NO. 2, FEBRUARY 2018 Packet Structure and Receiver Design for Low Latency Wireless Communications With Ultra-Short Packets Byungju Lee ,Member, IEEE , Sunho Park ,Member, IEEE , David J. Love ,Fellow, IEEE , Hyoungju Ji, Member, IEEE , and Byonghyo Shim ,Senior Member, IEEE Abstract — Fifth generation wireless standards require much and low latency communication (uRLLC) as one of key use lower latency than what current wireless systems can guaran- cases for 5G wireless communications. 1 One direct way to tee. The main challenge in fulfilling these requirements is the meet the stringent low latency requirements is to use a short- development of short packet transmission, in contrast to most of the current standards, which use a long data packet structure. sized packet. This is in contrast to most current wireless Since the available training resources are limited by the packet systems whose sole purpose is to transmit long data packets size, reliable channel and interference covariance estimation with efficiently. Indeed, most current physical layer design relies reduced training overhead are crucial to any system using short heavily on long codes to approach Shannon capacity. Sensors data packets. In this paper, we propose an efficient receiver that and devices in an IoT networks transmit a very small amount exploits useful information available in the data transmission period to enhance the reliability of the short packet transmission. of information such as environmental data (e.g., temperature, In the proposed method, the receive filter (i.e., the sample humidity, and pollution density), locations, and emergency covariance matrix) is estimated using the received samples from alarm, and thus asymptotic capac ity-achieving principles are the data transmission without using an interference training not relevant to the transmission of short packets. In view period. A channel estimation algorithm to use the most reliable of this, 5G physical layer design (e.g., pilot transmission data symbols as virtual pilots is employed to improve quality of the channel estimate. Simulation results verify that the proposed strategy, coding scheme, hybrid automatic repeat request) receiver algorithms enhance the reception quality of the short needs to be reevaluated and potentially redesigned as a whole. packet transmission. Many papers have been dedicated to analyze performance Index Terms — 5G wireless communications, short data metrics that are relevant to short packet communication, packets, low latency, Internet of Things, energy harvesting, including the maximal achievabl e rate at finite packet length virtual pilots. and finite packet error probability instead of using two classic I. I NTRODUCTION information-theoretic metrics, the ergodic capacity and the IFTH generation (5G) communication networks will be outage capacity [8]–[11]. Finite block-length analysis has been Fa key enabler in realizing the Internet of Things (IoT) extended to spectrum sharing networks using rate adapta- era and hyper-connected society [2]–[4]. To support real- tion [12] and wireless energy and information transmission time applications with stringent delay requirements and mas- using feedback [13]. sive machine-type devices, communication systems supporting In this paper, we consider practical constraints that are ultra low latency are needed [5]–[7]. For this reason, Interna- encountered when implementing a short packet transmission tional telecommunication union (ITU) defined ultra reliable framework. First, massive and simultaneous communications among autonomous devices will induce inter-device interfer- Manuscript received December 3, 2016; revised May 5, 2017 and ence at the receiver in an IoT network. In order to control the August 4, 2017; accepted September 11, 2017. Date of publication September 21, 2017; date of current version February 14, 2018. This inter-device interference, a technique to use multiple receive work was supported in part by the National Science Foundation (NSF) antennas has been proposed by utilizing the spatial degrees grant CNS1642982 and the National Res earch Foundation of Korea (NRF) of freedom (DoF) provided by multiple receive antennas to grant funded by the Korean government (MSIP) (2014R1A5A1011478 and 2016K1A3A1A20006019). This paper was p resented at the GLOBECOM, balance interference suppression and desired signal power Washington, USA, December 4–8, 2016 [1]. The associate editor coordinating improvement [14]. To implement this approach, the receiver the review of this paper and approving it for publication was A. Tajer. needs to acquire the desired CSI and the interference plus (Corresponding author: Byonghyo Shim.) B. Lee and D. J. Love are with the School of Electrical and noise covariance. While the desired CSI can be estimated Computer Engineering, Purdue Univ ersity, West Lafayette, IN 47907 USA using pilot signals, direct estimation of the interfering CSI (e-mail: [email protected] du; [email protected]). is difficult due to the large number of interfering devices in S. Park, H. Ji, and B. Shim are with the Institute of New Media and Communications and the School of Electrical and Computer Engi- the IoT network. One way to tackle this problem is to use an neering, Seoul National University , Seoul 08826, South Korea (e-mail: [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available 1Three key use cases include enhanc e mobile broadband (eMBB), massive online at http://ieeexplore.ieee.org. machine-type communications (mMTC), and ultra reliable and low latency Digital Object Identifier 10.1109/TCOMM.2017.2755012 communication (uRLLC) [6]. 0090-6778 © 2017 I EEE. Personal u se is perm itted, but republication/redistri bution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. LEE et al. : PACKET STRUCTURE AND RECEIVER DESIGN FOR LOW LATENCY WIRELESS COMMUNICATIONS 797 interference training period in which the interference (plus noise) covariance matrix can be estimated by listening to interference-only transmissions [15]. The main drawback of this approach is that it incurs a severe transmission rate loss since the target transmit device should remain silent during the interference training period. Also, the interference training period would not be large enough in obtaining the reliable estimate of the interference covariance for this short-sized packets, resulting in severe degradation in performance. Moreover, since current systems are designed to carry long data packets, the pilot transmission period can be made relatively small even though the actual period of pilot signals is large. However, the portion of time used for pilot signals is unduly large in a short-sized packet framework if the Fig. 1. A paired AP receives the desired signal from a target device and is training period is not reduced. On the other hand, if the pilot subject to interference (dashed lines) from neighboring devices. length is shortened, there would be a significant degradation in performance. Since there is a trade-off between the duration of the pilot training period and the data transmission period, technique suited to the short packet transmission. the main challenge is to perform a reliable channel estimation In Section IV, we describe the proposed virtual pilot while affecting the minimal impact on the duration of data based channel estimation technique. In Section V, we present transmission period. simulation results to verify the performance of the proposed An aim of this paper is to propose an efficient receiver scheme. We conclude the paper in Section VI. technique that exploits information obtained during the data We briefly summarize notation used in this paper. We employ uppercase boldface letters for matrices and lower- transmission period to improve the reception quality of the H T system in the short packet transmission. Intuitively, when time case boldface letters for vectors. The superscripts (·) and (·) resources are limited, we need to use all received data to denote the conjugate transpose and transpose, respectively. optimize the receiver performance. With this goal in mind, C denotes the field of complex numbers. · p indicates we propose a receive filtering algorithm that estimates the the p-norm. IN is the N× N identity matrix. E[·] is the interference covariance matrix using the received signal from expectation operator. ⊗ is the Kronecker product operator. CN (m,σ 2)denotes a complex Gaussian random variable with the normal data transmission period in the IoT network. In 2 doing so, the interference training period becomes unnecessary mean m and variance σ. and the transmission rate loss caused by the interference II. S YSTEM DESCRIPTION training period can be avoided. We show from analysis and numerical experiments that the proposed method achieves a A. System Model and Packet Structure linear scaling of SINR in the number of receive antennas. We consider uplink communication in an IoT networks Next, we propose a low latency frame structure adequate such as wireless sensor networks, ad hoc network, and energy for the one-shot random access where pilot and data are harvesting network, with an example setup shown in Fig. 1. transmitted simultaneously. We propose a strategy to exploit We assume that the target transmit device has a single antenna the data symbols received from the target transmit device to and

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