The advent of the Internet-of-Things (IoT) and proliferation of wireless devices and systems have put stringent requirements on reliability and latency, in addition to the scarcity of energy and spectrum resources. More importantly, ultra-reliability and low-latency (URLL) combined with concepts of energyharvesting (EH) and cognitive-radio (CR) make the analysis of IoT networks much more complex. This paper analyzes the performance of uplink EH-CR-IoT networks with URLL requirements. Analytical expressions for IoT network metrics, namely, average packet latency, reliability, and energy-efficiency are derived, while incorporating diversity transmissions under the finite blocklength (FBL) regime. The effect of network parameters, such as number of resource blocks allocated to each IoT user equipment (UE), blocklength, and number of packet replicas is examined on the network metrics, and their tradeoffs are discussed. Finally, the derived expressions are utilized to maximize the energy-efficiency of the IoT UEs subject to energy-causality and URLL constraints. Cognitive-radio, energy-harvesting, finite blocklength, Internet-of-Things, low-latency, ultra-reliability. INDEX TERMS
With the rise of the Internet-of-Things (IoT), new requirements have been brought into communication networks to make them more efficient, sustainable, and self-sufficient. Requirements, such as availability and ultra-reliability combined with the solutions of energy-harvesting and dynamic spectrum access, make the analyses of such networks more complex, while imposing different performance trade-offs. This paper analyzes the performance of ultra-reliable energy-harvesting cognitive radio Internet-of-Things (UR-EH-CR-IoT) networks, and provides analytical derivations for different IoT network metrics, such as GoodPut, reliability, collision probability, availability, and stability, so as to investigate their trade-offs. A new metric for network availability is defined based on energy availability and spectrum accessibility for UR-EH-CR-IoT networks, while incorporating transmission diversity. The effect of IoT network parameters, such as sensing time, diversity transmission, and number of packets in a data frame, is examined on the IoT network performance metrics. Lastly, the derived expressions are utilized to optimize the GoodPut, subject to various practical constraints. INDEX TERMS Availability, cognitive radio, collision probability, diversity transmission, energyharvesting, Internet-of-Things, stability, ultra-reliability. 1 This can be seen by noting that diversity transmission is only based on transmitting a fixed number of packet replicas. To the best of our knowledge, there are no existing results on the implementation complexity of diversity transmission.
Internet-of-Things (IoT) networks have recently emerged to provide massive connectivity for many application scenarios and services. Additionally, developing spectrum-access strategies for a large number of nodes with sporadic data traffic behaviors in IoT networks has attracted much attention recently. However, developing such strategies becomes more challenging when ultra-reliable low-latency (URLL) transmissions are required. As IoT networks entail spectrum-efficient transmission schemes, non-orthogonal multiple-access (NOMA) has emerged as a key enabler for such networks. On the other hand, grant-free random-access (RA) techniques are particularly promising for high spectral-efficiency and massive connectivity, since they reduce signaling overhead, and packet latency. Therefore, in this paper, uplink RA-NOMA IoT networks with clustered IoT devices is studied, where short packet and diversity transmissions are adopted to meet the URLL requirements. To reduce the negative effect of diversity transmission on packet latency, multiple replicas of packets are accommodated within different resource blocks (RBs) in the same transmission time interval (TTI). The analytical expressions of network metrics, namely, average packet latency, reliability, and GoodPut are derived. Furthermore, the effect of the number of packet replicas, blocklength, and cluster size on the network metrics is evaluated. Finally, the analytical derivations are utilized to find the optimal values for the number of packet replicas, blocklength, and power control parameters, such that the network GoodPut is maximized, subject to URLL constraints.INDEX TERMS Internet-of-Things, low-latency, NOMA, random-access, ultra-reliability. I. INTRODUCTIONFifth generation (5G) cellular networks have ignited numerous research areas since its introduction. The fundamental difference between 5G and the previous generations is that 5G is the driver for implementing two generic types of communications, namely, ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC) [1]. The combination of these two communication types along with the explosive and exponential growth in the number of smart devices, applications, and services is the advent of massive Internet-of-Things (mIoT) networks. This is in addition to the extensive emerging mission-critical applications and use cases, such as tactile Internet (involving remote motion control, tele-surgery, etc.), factory automation, Industrial IoT (IIoT), and those under the The associate editor coordinating the review of this manuscript and approving it for publication was Noor Zaman .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.