Analysis of Local Quasi-Stationarity Regions in an Urban Macrocell Scenario

A Novel Non-Stationary Channel Model Utilizing Brownian Random Paths

“…In addition, as can be observed in Figures 4 and 5, the lowest probability of receiving signals belongs to the average direction of the motion, i.e., the angle corresponds to the slope of the drift. Figures 7-9 exhibit the local PSD S µµ ( f ; l) presented in (9). The classical Jakes PSD (resembling a U-shape) can be observed for the stationary case (l = 0) in all these three figures.…”

“…Many empirical and analytical investigations, e.g., [9][10][11], however, reveal that the stationarity of the channel is only valid for extremely short travelling distances [12]. The potential suitability of geometric channel models for explaining non-stationary environments [1][2][3] on the one hand, and the results of reallife measurement campaigns [9][10][11] on the other hand, encourage us to study geometric channel models under non-stationary conditions.…”

“…The potential suitability of geometric channel models for explaining non-stationary environments [1][2][3] on the one hand, and the results of reallife measurement campaigns [9][10][11] on the other hand, encourage us to study geometric channel models under non-stationary conditions.…”

On Non-Stationary Urban Macrocell Channels in a Cooperative Downlink Beamforming Scenario

“…The BSs use local statistical knowledge about the channel, i.e., each BS has only statistical channel knowledge about its own link to the MT, to perform transmit beamforming. Furthermore, we assume that transmission is taking place over a random, time-varying, and flat-fading 1 MISO channel with N a antennas at each BS and a single antenna at the MT. We assume to have perfect timing synchronization from the BSs to the MT.…”

“…However, the wireless channel is known to be inherently non-stationary [1]. As several algorithms in digital communications rely on the stationarity of the channel, a practical approach is to define local quasistationarity regions [2], i.e., local regions in which a stochastic process is approximated as stationary.…”