Search

Physics-based wireless propagation modeling and network protocol design have evolved over decades as orthogonal areas in communication systems research. This fragmented approach does not exploit available efficiencies when planning and deploying communication systems. In an attempt to integrate the two areas, we harness the understanding of the underlying physics of electromagnetic propagation to enhance the robustness of network protocol design by deriving physics-based network-level performance metrics. We use ray-tracing and parabolic equation models of 2.4 GHz propagation along tunnel and open-air sections of London Underground to evaluate the performance of a communications-based train control system. For comparison, we consider existing path loss models for tunnel environments and investigate whether they can provide sufficient accuracy to be used for network protocol design. We show that physics-based models lead to reliable predictions at the network level, similar in fidelity to using measured data and unlike using simplified channel models of the path loss exponent type. [ABSTRACT FROM AUTHOR]

This paper describes an extensive propagation channel measurement campaign in a warehouse environment for line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The measurement setup employs a vector network analyzer operating in the 2–8-GHz frequency band combined with an $8 \times 8$ virtual multiple-input multiple-output (MIMO) antenna array. We develop a comprehensive statistical propagation channel model based on high-resolution extraction of multipath components and subsequent spatiotemporal clustering analysis. The intracluster direction of departure (DoD), direction of arrival (DoA), and the time of arrival (ToA) are independent, both for the LOS and NLOS scenarios. The intracluster DoD and DoA can be approximated by the Laplace distribution, and the intracluster ToA can be approximated by an exponential mixture distribution. The intercluster analysis, however, shows a dependency between the cluster DoD, DoA, and ToA. To capture this dependency, we separately model the clusters caused by single and multiple bounce scattering along the aisles in the warehouse. The intercluster DoD distribution follows a Laplace distribution, while the cluster DoA conditioned on the DoD is approximated by a Gaussian mixture distribution. The model was validated using the capacity and delay-spread values. [ABSTRACT FROM AUTHOR]