"Towards Reliable, High Throughput and Low Power Wireless Communication Systems"

无线通信系统的发展日新月异，人们对无线通信系统的吞吐量、可靠性和低功耗的要求也越来越高。这些基本要求可以通过查看无线节点的空间位置来研究。对于一个庞大而密集的网络来说，获取节点的实际位置并不是一项微不足道的任务。因此，在分析系统需求和模拟手持移动用户实际行为的参数时，应该考虑网络的随机空间建模。因此，随机网络的概念在无线通信中得到了广泛的支持。 通过考虑信道损耗、干扰和移动性之间的相互作用，该建议集中在随机网络的方向上。到目前为止，集中式网络的分析结果是在假设无线节点是静态的基础上确定的。在这项研究中，我们感兴趣的是通过量化基于各种随机网络的大范围衰落和干扰的整体中断概率来扩展我们对网络的理解。此外，还考虑了移动性模型对无线网络各种系统参数的影响。在某种程度上，这项工作的挑战是发现行为良好的移动模型，这些模型反映了城市和郊区移动小区的用户模式。此外，我们还希望分析和实现一个随机无线传感器网络，并评估其检测能力。 这一建议所产生的总体研究将作为一种基本的分析工具，用于得出预测模型，这些模型能够预测信道损失的影响。这些模型有望帮助系统分析师和部署工程师规划智能网络。此外，结果还可用于创建部署子例程，这些子例程可以在不同渠道环境的设计过程中帮助行业专业人员。

Wireless communication systems are progressing very rapidly and there is always requirements on higher throughput, better reliability and lower power consumption. These essential requirements can be investigated by looking at the spatial position of wireless nodes. Obtaining the actual position of nodes is not a trivial task for a large and dense network. Therefore, stochastic spatial modelling of the network should be considered for the analysis of system requirements and parameters which emulates the actual behavior of handheld mobile users. Consequently, the concept of random network is gaining wide traction in wireless communications. This proposal is concentrated in the direction of random networks by considering interplay between channel loss, interference and mobility. To date, analytical results have been determined for centralized network over the assumption that wireless nodes are static. In this research, we are interested in expanding our understanding of the network by quantifying the overall outage probability based on large scale fading and interference for various random networks. Furthermore, the impact of mobility models are considered on various system parameters for wireless networks. In part, the challenge in this endeavor is to discover well behaved mobility models that are reflective of user patterns for urban and suburban mobile cells. Additionally, we want to analyze and implement a random wireless sensor network and assess its detection capability. The overall research resulting from this proposal will serve as an essential analytical tool for deriving predictive models that have the ability to forecast the impact of the channel loss. These models are anticipated to help system analysts and deployment engineers in planning smart networks. Also, the outcome can be used for creating deployment subroutines that can assist industry professionals during the design process over diverse channel environments.