Performance Analysis of Grant-Free Random-Access NOMA in URLL IoT Networks

Abstract

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 .

A Cross-Layer Survey on Secure and Low-Latency Communications in Next-Generation IoT

“…A further alternative solution for massive access in IoT networks is given by NOMA, where a user receives a superimposition of the signals of all the other users and then applies proper interference cancellation strategies, thus allowing for increased throughout. In [54], the authors investigate the combined use of NOMA and short packet transmissions to enable URLL services. A similar target is pursued by the authors of [55], who combine NOMA with UAVs to establish a high-capacity IoT uplink network suitable for URLLC applications.…”

Enabling transmission status detection in grant‐free power domain non‐orthogonal multiple access for massive Internet of Things

“…Motivated by this, the principal contributions of this work are summarized as follows. • Different from the related works, [46][47][48][49][50][51][52] that propose different grant-free PD-NOMA protocols from the access throughput point of view, this work focuses specifically on the design of mechanisms to facilitate efficient transmission status (especially collisions) detection in grant-free PD-NOMA.…”

“…As grant‐free access is prone to collisions, another work proposed a distributed layered grant‐free PD‐NOMA model by dividing the cell into different layers based on predetermined inter‐layer received power differences to reduce collisions, and analyzed the conditional access throughput 50 . The performance of orthogonal preambles aided grant‐free PD‐NOMA for ultra‐reliable low‐latency networks was also recently investigated, 51 where devices were initially clustered, and each active device in a cluster transmitted multiple copies of its data packet over different RBs to reduce the latency; these multiple copies may affect the signaling overhead. Lastly, a transmit power pool design to maximize the achievable data rate for grant‐free PD‐NOMA using deep reinforcement learning was proposed to tackle the absence of closed‐loop power control in grant‐free access 52 .…”

“…Motivated by this, the principal contributions of this work are summarized as follows. Different from the related works, 46‐52 that propose different grant‐free PD‐NOMA protocols from the access throughput point of view, this work focuses specifically on the design of mechanisms to facilitate efficient transmission status (especially collisions) detection in grant‐free PD‐NOMA. To this end, a transmission status detection technique is proposed, which requires the active devices to send minimal dummy/pilot data to the BS, but still facilitating efficient transmission status detection. The technique relies on special activity indicator symbols (AIS) transmitted by each active device at the start of its transmission to show its activity/presence, which are exploited by the BS to identify collisions and successful transmissions.…”

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

“…It serves as the data collection entrance for the new power system [1]. However, with the digitalization of the new power system, various new power services emerging in the power grid have brought massive data and diverse demands [2], imposing higher requirements on the service quality and processing capabilities of the network [3]. The traditional approach of centralized processing through the IoT management platform not only results in significant processing delays, making it difficult to meet business needs, but also puts tremendous pressure on the backbone communication network's carrying capacity and the cloud server's computing capabilities due to the transmission of massive data [4].…”