NOMA-Based Resource Allocation and Mobility Enhancement Framework for IoT in Next Generation Cellular Networks

Abstract

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the Internet, forming what is called the Internet of Things (IoT). The IoT devices with heterogeneous characteristics and the quality of experience (QoE) requirements may engage in the dynamic spectrum market due to the scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of this paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions enhance the radio resource utilization efficiency. The results show a substantial reduction in the number of sub-carriers required when compared with conventional OMA and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has fewer users or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve the required quality of service with a guaranteed bit rate (GBR) and non-GBR.

User Selection for NOMA-Based MIMO With Physical-Layer Network Coding in Internet of Things Applications

“…The IoT devices are uniformly placed in the cell area which is between 30m and 250m from the BS. The noise power spectrum density is -159dBm/Hz, and the maximum transmit power and the bandwidth of each IoT device are set to 11dBm and 200kHz respectively [34]. The path loss is determined by L = 128.1 + 37.6 log 10 (d n,j (km)) [35] where d n,j is the distance between IoT device n in the set j and the BS.…”

Cooperative Resource Management for Cognitive Satellite-Aerial-Terrestrial Integrated Networks Towards IoT

“…In terrestrial cellular networks, to improve the resource utilization as well as minimizing the energy consumption of IoT system, many resource allocation schemes have been proposed, such as bandwidth allocation [15], joint optimization of sub-carriers and transmit power [16], [17], and joint allocation of resource blocks and transmit power [18].…”

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

“…Hence, researchers have proposed many methods for separating the two signals, such as time switching, power splitting, antenna switching, and separate receiver. Abozariba et al 38 proposed a NOMA-based resource allocation and mobility enhancement framework for IoT networks. 29 NOMA exploits the power domain for multiple access, which leads to excellent performance, in terms of spectrum and energy efficiency.…”

“…Duan et al 37 discussed an optimal resource allocation method for multiple unmanned aerial vehicles (UAVs). Abozariba et al 38 proposed a NOMA-based resource allocation and mobility enhancement framework for IoT networks. We notice that optimizing energy consumption in self-powered IoT networks with NOMA technology is still an open and challenging problem.…”