Performance Analysis and Energy Conservation of Cellular Mobile Networks

無線蜂巢網絡的性能分析和節能

Student thesis: Doctoral Thesis

View graph of relations

Author(s)

  • Jingjin Timothy WU

Related Research Unit(s)

Detail(s)

Awarding Institution
Supervisors/Advisors
Award date8 Mar 2016

Abstract

We are now in a mobile age where mobile communication devices are penetrating rapidly into every aspect of our life. Mobile cellular networks have thus become a popular research area for both academia and industry recently. This thesis focuses on two important problems in cellular networks, namely performance evaluation and energy conservation.
Energy conservation techniques in cellular networks, or "Green Cellular Net-working" topics, have drawn a lot of attention due to global climate change as well as economic concern of network operators. While energy saving can be achieved by adopting renewable energy resources or improving design of certain hardware (e.g., power amplifiers) to make it more energy-efficient, the cost of purchasing, replacing and installing new equipment (including manpower, transportation, disruption to normal operation, as well as associated energy and direct cost) is often prohibitive. By comparison, approaches that work on the operating protocols of the system do not require changes to current network architecture, making them far less costly and easier for testing and implementation.
One of such approaches is base station (BS) sleeping technique, which takes advantage of changing traffic patterns on daily or weekly basis, and selectively switches some lightly loaded base stations to the sleep mode (a special mode consuming minimal amount of energy as compared to normal operating mode). As base stations account for a large proportion of energy consumed in cellular network, this approach has the potential to save significant amount of energy as shown in various studies. However, it is noticed that certain simplifying assumptions made in the published papers introduce inaccuracies in performance evaluation. This thesis will discuss these assumptions, particularly, an assumption that ignores the effect of traffic-load-dependent factors on energy consumption by a comparative simulation study. We show here that considering this effect may lead to noticeably lower benefit than in models that ignore this effect.
On the other hand, turning BSs to sleep mode reduces total capacity of the network. Therefore, it is important for the network operators to maintain Quality of Service (QoS) with less number of active BSs in order to maintain user experience. This thesis proposes a novel method to evaluate call blocking probability, one of the QoS measures, in cellular networks with different BS sleeping patterns. While most existing work uses computer simulation to obtain blocking probabilities in cellular networks with or without BS sleeping, it is not scalable as large amount of computing power and time is required for systems of practical sizes. This thesis evaluates blocking probabilities of cellular networks with BS sleeping techniques analytically by proposing a robust and computationally efficient analytical approximation technique under the recently established Information Exchange Surrogate Approximation (IESA) framework. The basic idea is to model the network as an overflow loss system, where calls arriving at a sleeping or fully-occupied BS are allowed to overflow to nearby servers (BSs) for service. A new surrogate model under the IESA framework is proposed to fit the features distinguishing cellular networks from other overflow loss systems, including the locality feature that the calls are only allowed to overflow to physically proximate BSs, and the user mobility feature which could cause the set of available BSs to overflow for a call to change during its service period.
By considering the mutual overflow effect between BSs and the unique features of cellular networks, the proposed approximation method, Information Exchange Surrogate Approximation for Cellular Networks (IESA-CN), is demonstrated in a wide range of scenarios to significantly improve the approximation accuracy as compared to the classical Fixed-Point Approximation (FPA).