Candidate: Hesham Moussa
Title: Channel Access Management in Massive Cellular IoT Communications
Date: January 29, 2020
Time: 11:00 AM
Place: EIT 3145
Supervisor(s): Zhuang, Weihua
Abstract:
As part of the steps taken towards improving the quality of life, many of everyday life activities as well as technological advancements are relying more and more on smart devices. In the future, it is expected that every electric device will be a smart device that can be connected to the internet. This gave rise to the new network paradigm known as the massive cellular IoT, where a large number of simple battery powered heterogeneous devices are collectively working for the betterment of humanity in all aspects. However, different from the traditional communication networks, IoT applications produce uplink-heavy data traffic that is composed of a large number of small data packets with different quality of service requirements. These unique characteristics pose as a challenge to the current cellular channel access process, and hence, new and revolutionary access mechanism are much needed. These access mechanisms need to be cost-effective, enable the support of massive number of devices, scalable, practical, and energy and resource efficient. To do so, we recognize that in cellular networks, before devices transmit their data, they use a contention-based association protocol, known as 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 and guide the process of developing potential cost-effective solutions. Consequently, in the first part, we develop a novel mathematical framework based on stochastic geometry to analyze the RACH protocol and identify its limitations. Our analysis and results highlight the shortcomings of the RACH protocol and give insights into the potentials brought on by employing power control techniques as cheap methods for improving the overall network performance. Based on our analysis, we determine that, as the number of devices increases, the contention over the limited network radio resources increases, leading to network congestion. Further, although power control has some potential, it is hard to implement, and the improvement comes at the cost of increase energy consumption. Consequently, we propose to use node clustering and data aggregation to provide cheap and cost-effective massive access. However, as the number of devices increase, optimizing node clustering and data aggregation processes becomes critical as many trade-offs arise among different network performance metrics. To tackle this issue, we explore the possibility of combining data aggregation and NOMA where we propose a novel two-hop NOMA-enabled network architecture. Concepts from queuing theory and stochastic geometry are jointly exploited to derive tractable models for different network performance metrics 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 metrics, and hence facilitate the design of two-stage transmission architecture. Numerical results demonstrate that the proposed solution improves the overall access delay and energy efficiency as compared to traditional OMA-based clustered networks.