Evaluation of Outage Probability in Presence of Interference and Noise with Application to Dual-hop Wireless Systems

In a wireless network, the main objective of designers is to manage the available resources to maintain good quality of service for all users. As a result, engineers put extra effort in evaluating and improving the performance of wireless systems. Geometric modeling of wireless networks is drawing significant attention with regards to analytically evaluating the performance. In this dissertation, stochastic modeling of users is used to emulate their distribution in the wireless network. The wireless network is analyzed as a single-hop network where there is only one direct link connecting the base station to each user in the cell. Next, probability density function (PDF) of signal to interference ratio (SIR), signal to noise ratio (SNR) and signal to interference plus noise ratio (SINR) are evaluated. Given that outage probability is one of the important factors in studying the performance of a wireless network, outage probability is evaluated based on path loss, SIR, SNR and SINR. Simulation is performed in order to validate the analytical results. To show the application of the work done related to outage probability, a dual-hop wireless system is considered. Wireless networks face some challenges, such as the presence of users in severe shadowing regions, or obstacles which may diminish the link quality. To overcome these limitations, dual-hop relaying system is considered. Dual-hop system is used as a cost effective solution in improving the performance of the wireless system. In such a solution, a number of relays is deployed over the cell to help transmission of signals from the base station to mobile stations. In the dual-hop system, a low-quality long-distance link is broken into two better quality links, achieving the required outage probability and enhancing the system performance. The number of deployed relays and their location over the studied area affect the performance of a dual-hop wireless system. Therefore, the required number of relays and their placement over the studied area are investigated, and a desired outage probability is achieved. Uniform distribution of users cannot describe the real distribution for all cases. Gaussian distribution has malleability, as clustering tendency of users can be controlled in the network using the standard deviation of the distribution. Consequently, Gaussian distribution is considered for users in the studied area to evaluate PDF of SIR and the outage probability

On the Fundamentals of Stochastic Spatial Modeling and Analysis of Wireless Networks and its Impact to Channel Losses

With the rapid evolution of wireless networking, it becomes vital to ensure transmission reliability, enhanced connectivity, and efficient resource utilization. One possible pathway for gaining insight into these critical requirements would be to explore the spatial geometry of the network. However, tractably characterizing the actual position of nodes for large wireless networks (LWNs) is technically unfeasible. Thus, stochastical spatial modeling is commonly considered for emulating the random pattern of mobile users. As a result, the concept of random geometry is gaining attention in the field of cellular systems in order to analytically extract hidden features and properties useful for assessing the performance of networks. Meanwhile, the large-scale fading between interacting nodes is the most fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel losses in general, and the inevitability of random networks in real-life situations, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the large-scale fading. Therefore, in exact closed-form notation, we generically derived the large-scale fading distributions between a reference base-station and an arbitrary node for uni-cellular (UCN), multi-cellular (MCN), and Gaussian random network models. In fact, we for the first time provided explicit formulations that considered at once: the lattice profile, the users’ random geometry, the spatial intensity, the effect of the far-field phenomenon, the path-loss behavior, and the stochastic impact of channel scatters. Overall, the results can be useful for analyzing and designing LWNs through the evaluation of performance indicators. Moreover, we conceptualized a straightforward and flexible approach for random spatial inhomogeneity by proposing the area-specific deployment (ASD) principle, which takes into account the clustering tendency of users. In fact, the ASD method has the advantage of achieving a more realistic deployment based on limited planning inputs, while still preserving the stochastic character of users’ position. We then applied this inhomogeneous technique to different circumstances, and thus developed three spatial-level network simulator algorithms for: controlled/uncontrolled UCN, and MCN deployments