Abstract-This paper presents the analysed results from a dynamic spatial-temporal measurement campaign conducted at 5.2 GHz in a corridor environment within a modern building. The Single-Input-Multiple-Output (SIMO) measurement was performed by moving the transmitting antenna with a specialised trolley along a predefined route, while the receiving linear array was fixed at one location. Subsequent post-processing was conducted using the 2-D Unitary ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) super-resolution algorithm to extract the direction-of-arrival (DoA) and time-delay-of-arrival (TDoA) of the multipath components. The corridor was then characterised in terms of delay spread, azimuth spread, K-factor and coherence bandwidth. It was found that the corridor favours a wave-guiding effect whereas locations adjacent to the corridor enjoy the leakage of energy from the waves propagating along the corridor. The dynamic power delay spectrums are shown. Finally, the correlations between the delay spread, azimuth spread and coherence bandwidth are assessed.

Abstract-A novel stochastic wideband dynamic spatiotemporal indoor channel model which incorporates both the spatial and temporal domain properties as well as the dynamic evolution of paths when the mobile moves is proposed based on the concept of a Markov process. The derived model is based on dynamic measurement data collected at a carrier frequency of 5.2 GHz in typical indoor environments. Multipath components are estimated using the super-resolution frequency domain spacealternating generalized expectation maximization algorithm prior to identification of path "birth" and "death" using a new data analysis method. Analysis shows that multiple births and deaths are possible at any instant of time. Furthermore, correlation exists between the number of births and deaths. Thus, an M -step 4-state Markov channel model (MCM) is proposed in order to account for these two effects. The spatio-temporal variations of paths within their lifespans are taken into consideration by the spatio-temporal vector which was found to be well modeled by a Gaussian probability density function while the power variation can be modeled by a simple low-pass filter. In addition, the methodology used to extract the MCM parameters from the measurement data is also presented. Due to the distinction in the birth-death statistics, the model is generalized through segmentation of the measurement runs and can be completely parameterized by several sets of Markov parameters associated with the type of environment and scenario under consideration. The implementation of the model is also detailed and, finally, the model is evaluated by comparing key statistics of the simulation results with the measurement results.

Generalized Expectation maximization (FD-SAGE) algorithm and clusters are identified in the spatio-temporal domain by a nonparametric density estimation procedure. The description of the clustering observed within the channel relies on two classes of parameters, namely, intercluster and intracluster parameters which characterize the cluster and MPC, respectively. All parameters are described by a set of empirical probability density functions (pdfs) derived from the measured data. The correlation properties are incorporated in two joint pdfs for cluster and MPC positions, respectively. The clustering effect also gives rise to two classes of channel power density spectra (PDS)-intercluster and intracluster PDS which are shown to exhibit exponential and Laplacian functions in the delay and angular domains, respectively. Finally, the model validity is confirmed by comparison with two existing models reported in the literature.