Visualizing Wireless Network Performance Metrics in Space–Time

To evaluate the performance of emerging mobile wireless networks with multihop coverage extensions, spatiotemporal network models are required. These models should include the directional characteristics of network information flows to exploit the spatial domain due to the deployment of advanced antenna systems. Furthermore, the models should be capable of handling nonstationary scenarios with dynamic evolution of mobile nodes. In an attempt to solve these two problems, which have not been fully addressed in the existing literature, and motivated by the useful analogy between classical propagation channels and wireless networks, we propose a novel network modeling framework where the relevant figure of merit (FOM) may be the received signal power, the event detection error exponent, the channel capacity, etc., depending on the network's type. We first formulate the general description methods of the framework, including the double-directional FOM impulse response, the angular spectrum, and the angular dispersion. Subsequently, we propose the ray approaches, including the geometry-based stochastic channel models (GSCMs) and the deterministic ray-tracing techniques, to support and enrich the description methods. The given analytical tools facilitate visualization of wireless network performance metrics in space–time. Due to their stochastic nature, the proposed methodology would be most useful for the design and analysis of networks featured by decentralized, randomized, and dense placement of nodes.