Latency (engineering)

About: Latency (engineering) is a research topic. Over the lifetime, 3729 publications have been published within this topic receiving 39210 citations. The topic is also known as: lag.

Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service

Abstract: To satisfy the expected plethora of demanding services, the future generation of wireless networks (6G) has been mandated as a revolutionary paradigm to carry forward the capacities of enhanced broadband, massive access, and ultrareliable and lowlatency service in 5G wireless networks to a more powerful and intelligent level. Recently, the structure of 6G networks has tended to be extremely heterogeneous, densely deployed, and dynamic. Combined with tight quality of service (QoS), such complex architecture will result in the untenability of legacy network operation routines. In response, artificial intelligence (AI), especially machine learning (ML), is emerging as a fundamental solution to realize fully intelligent network orchestration and management. By learning from uncertain and dynamic environments, AI-/ML-enabled channel estimation and spectrum management will open up opportunities for bringing the excellent performance of ultrabroadband techniques, such as terahertz communications, into full play. Additionally, challenges brought by ultramassive access with respect to energy and security can be mitigated by applying AI-/ML-based approaches. Moreover, intelligent mobility management and resource allocation will guarantee the ultrareliability and low latency of services. Concerning these issues, this article introduces and surveys some state-of-the-art techniques based on AI/ML and their applications in 6G to support ultrabroadband, ultramassive access, and ultrareliable and lowlatency services.

IoT Enabled UAV: Network Architecture and Routing Algorithm

Abstract: Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.

Ultra-Reliable Low Latency Communication Using Interface Diversity

Abstract: An important ingredient of the future 5G systems will be ultra-reliable low-latency communication (URLLC). A way to offer URLLC without intervention in the baseband/PHY layer design is to use interface diversity and integrate multiple communication interfaces, each interface based on a different technology. In this paper, we propose to use coding to seamlessly distribute coded payload and redundancy data across multiple available communication interfaces. We formulate an optimization problem to find the payload allocation weights that maximize the reliability at specific target latency values. In order to estimate the performance in terms of latency and reliability of such an integrated communication system, we propose an analysis framework that combines traditional reliability models with technology-specific latency probability distributions. Our model is capable to account for failure correlation among interfaces/technologies. By considering different scenarios, we find that the optimized strategies can in some cases significantly outperform strategies based on $k$ -out-of- $n$ erasure codes, where the latter do not account for the characteristics of the different interfaces. The model has been validated through simulation and is supported by experimental results.

5G Ultra-Reliable Low-Latency Communication Implementation Challenges and Operational Issues with IoT Devices

Abstract: To meet the diverse industrial and market demands, the International Telecommunication Union (ITU) has classified the fifth-generation (5G) into ultra-reliable low latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). Researchers conducted studies to achieve the implementation of the mentioned distributions efficiently, within the available spectrum. This paper aims to highlight the importance of URLLC in accordance with the approaching era of technology and industry requirements. While highlighting a few implementation issues of URLLC, concerns for the Internet of things (IoT) devices that depend on the low latency and reliable communications of URLLC are also addressed. In this paper, the recent progress of 3rd Generation Partnership Project (3GPP) standardization and the implementation of URLLC are included. Finally, the research areas that are open for further investigation in URLLC implementation are highlighted, and efficient implementation of URLLC is discussed.

Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario

Abstract: Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10−9 packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon’s capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while the NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.