Modelling and Analysis of Non-Stationary Mobile Fading Channels Using Brownian Random Trajectory Models
Doctoral thesis, Peer reviewed
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The demanding mobility features of communication technologies call for the need to advance channel models (among other needs), in which non-stationary aspects of the channel are carefully taken into consideration. Owing to the mathematical complexity imposed by mobility features of the mobile station (MS), the number of non-stationary channel models proposed in the literature is very limited. The absence of a robust trajectory model for capturing the mobility features of the MS also adds to the depth of this gap. Not only statistically non-stationary channels, but also physically non-stationary channels, such as vehicle-to-vehicle channels in the presence of moving scatterers, have been rarely investigated. In the literature, there exist two fundamental channel modelling approaches, namely deterministic and stochastic approaches. Deterministic approaches, such as measurement-based channel modelling, are known to be accurate, but site-specific and economically expensive. The stochastic approaches, such as geometry-based channel modelling, are known to be economically inexpensive, computationally fair, but not as accurate as the deterministic approach. Among these approaches, the geometry-based stochastic approach is the best to capture the non-stationary aspects of the channel. In this dissertation, we employ the geometry-based stochastic approach for the development of three types of channel models, namely stationary, physically nonstationary, and statistically non-stationary channel models. We geometrically track the plane waves emitted from the transmitter over the local scatterers up to the receiver, which is assumed to be in motion. Under the assumptions that the scatterers are fixed and the observation time is short enough, we develop the stationary channel models. In this regard, we propose a unified disk scattering model (UDSM), which unifies several well-established geometry-based channel models into one robust channel model. We show that the UDSM is highly flexible and outperforms several other geometric models in the sense of matching empirical data. In addition, we provide a new approach to develop stationary channel models based on delay-angle joint distribution functions. Under the assumption that the scatterers are in motion and the observation time is again short enough, we develop a physically non-stationary channel model. In this connection, we model vehicle-to-vehicle (V2V) channels in the presence of moving scatterers. Proper distributions for explaining the speed of relatively fast and relatively slow moving scatterers are provided. The statistical properties of the proposed channel model are also derived and validated by measured channels. It is shown that relatively fast moving scatterers have a major impact on both V2V and fixed-to-fixed (F2F) communication links, as they are significant sources of the Doppler spread. However, relatively slow moving scatterers can be neglected in V2V channels, but not in F2F channels. Under the assumption that the scatterers are fixed and the observation time is not necessarily short anymore, we develop the statistically non-stationary channel models. To this aim, we first introduce a new approach for generating fully spatial random trajectories, which are supposed to capture the mobility features of the MS. By means of this approach, we develop a highly flexible trajectory model based on the primitives of Brownian fields (BFs). We show that the flexibility of the proposed trajectory is threefold: 1) its numerous configurations; 2) its smoothness control mechanism; and 3) its adaptivity to different speed scenarios. The statistical properties of the trajectory model are also derived and validated by data collected from empirical studies. We then introduce a new approach to develop stochastic non-stationary channel models, the randomness of which originates from a random trajectory of the MS, rather than from the scattering area. Based on the new approach, we develop and analyze a non-stationary channel model using the aforementioned Brownian random trajectory model. We show that the channel models developed by this approach are very robust with respect to the number of scatterers, such that highly reported statistical properties can be obtained even if the propagation area is sparsely seeded with scatterers. We also show that the proposed non-stationary channel model superimposes large-scale fading and small-scale fading. Moreover, we show that the proposed model captures the path loss effect. More traditionally, we develop and analyze two non-stationary channel models, the randomness of which originates from the position of scatterers, but not from the trajectory of the MS. Nevertheless, the travelling path of the MS is still determined by a sample function of a Brownian random trajectory. It is shown that the proposed channel models result in a twisted version of the Jakes power spectral density (PSD) that varies in time. Accordingly, it is demonstrated that non-stationarity in time is not in line with the common isotropic propagation assumption on the channel.
Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 2014
PublisherUniversitet i Agder / University of Agder
SeriesDoctoral dissertations at University of Agder;