This paper explores the usage of low-voltage Power-Line Communication (PLC) links for Enhanced Common Public Radio Interface (eCPRI)-based front-hauling in 5G Internet of Things (IoT) indoor mobile coverage environments, using a split Centralized Radio Access Network (C-RAN) architecture. This research aims to analyze how parameters such as wireless IoT device count, bandwidth, and transmission technology affect the delay performance of the proposed system. To achieve this goal, we develop detailed mathematical models that draw insights from queuing theory, stochastic geometry, and Markov models. Extensive system-level simulations verify these models’ accuracy, and the analytical results cover radio and access delay performance. We validate the system’s efficiency in supporting IoT indoor cellular applications and assess the feasibility of the proposed PLC-based front-hauling system, considering the strict delay requirements of the eCPRI standard.

Providing energy-efficient and delay-aware channel access in cellular networks is essential to many anticipated massive Internet of Things (IoT) applications. However, as the number of devices increases, the contention over the limited network radio resources increases, leading to network congestion. The congestion increases the channel access delay and energy consumption of IoT devices, and reduces the number of supported devices. Node clustering and data aggregation are potential approaches to support the massive number of devices while meeting the various service quality requirements of diverse applications. As the number of devices increases, optimizing the node clustering and data aggregation process becomes critical as many tradeoffs arise among different network performance metrics. In this article, we present a novel nonorthogonal multiple access-enabled two-stage transmission architecture to enable massive cellular IoT communications. Concepts from queuing theory and stochastic geometry are jointly exploited to derive tractable models for different network performance parameters, such as coverage probability, two-hop access delay, and the number of served devices per transmission frame. The established models characterize relations among various network parameters, and hence facilitate the design of two-stage transmission architecture. The numerical results demonstrate that the proposed solution improves the overall access delay and energy efficiency as compared to traditional-orthogonal-multiple-access-based clustered networks. They also highlight that, for the scenario considered, there is an optimal number of aggregators at which the tradeoffs among the different network performance measures are optimized.

Node clustering and data aggregation help extend the coverage of cellular networks and increase the number of supported devices, while meeting the various service quality requirements and reducing energy consumption, making them suitable for enabling the future massive cellular Internet-of-Things (IoT) applications. Consequently, we propose to overlay the cellular network with a layer of data aggregators (DAs) to act as relays. DAs use cellular backhauling, and thus, the network provides both single- and two-hop routes; however, DAs share radio resources with single-hop devices, creating a dependency between the two routes. Thus, the proper design of the DA-enabled network becomes critical for cost effectiveness and efficient radio resource utilization. In this article, we formulate a joint access point association, resources utilization, and energy-efficient communication optimization problem that takes into account various networking factors, such as the number of devices, the number of DAs, the number of available resource units, interference, the transmission power limitation of the devices, DA transmission performance, and channel conditions. The objective is to show the usefulness of data aggregation and shed light on the importance of network design when the number of devices is massive. We propose a coalition game theory-based algorithm PAUSE to transform the optimization problem into a simpler form that can be successfully solved in polynomial time. Different network scenarios are simulated to showcase the effectiveness of PAUSE and to draw observations on cost-effective network design with DAs.

Providing energy efficient and delay-aware access is essential to many anticipated cellular Internet of Things (IoT) applications. In cellular networks, before devices transmit their data, they use a contention-based association protocol, known as random access channel (RACH), which introduces extensive access delays and energy wastage as the number of contending devices increases. Modeling the performance of the RACH protocol is a challenging task due to the complexity of uplink transmission that exhibits a wide range of interference components; nonetheless, it is an essential process that will help determine the applicability of cellular IoT communication paradigm. This paper presents a novel mathematical framework based on stochastic geometry to analyze the RACH protocol and identify its limitations in the context of cellular IoT applications with a massive number of devices. To do so, we study the traditional cellular association process and derive a mathematical model for its association success probability. The derived model accounts for device density, spatial characteristics of the network, power control employed, and mutual interference among the devices. Our analysis and results highlight the shortcomings of the RACH protocol and give insights into the potentials brought on by employing power control techniques. The developed framework can be applied to evaluate the performance of other contention-based access schemes by incorporating their unique operational principles.