km/h and 80-120 km/h for outdoor and vehicular, respectively. The proportions of users are 60% and 40% for outdoor and vehicular, respectively. Mobility Model The mobility model will follow outdoor and vehicular patterns allowing appropriate handover between macrocells and microcells for all users. Figure 2.9 Mixed physical environment and proposed deployment model. 2.2.6.1 Long-term Fading Decorrelation Length We characterize the log-normal fading in the logarithmic scale around the mean path loss L (dB) by a Gaussian distribution with zero mean and standard deviation. In this context, due to the slow fading process versus distance Ax, adjacent fading values corre- late. Then, the normalized auto-correlation function R(Ax) can be described with suffi- cient accuracy by an exponential function [17]. R(Ax) = exp ( d Ax In2 (2.49) where the decorrelation length dcor depends on the environment. From this principle, we may assume 20 m for the decorrelation in the vehicular, and 5 m for the outdoor to indoor pedestrian environment. For the latter the evaluation of decorrelation length may not be fully valid. 2.2.7 Channel Impulse Response The environments described in the preceding sections have a channel impulse response based on a tapped-delay line model. The number of taps, the time delay relative to the System Analysis Fundamentals first tap, the average power relative to the strongest tap, and the Doppler spectrum of each tap characterize the model. Most of the time delay spreads are relatively small, but occasionally, there are worst case multi-path characteristics that lead to larger delay spreads. Here we consider the worst case. Two multi-path channels capture this delay spread better than a single tapped delay line. The reference simulation channel model can use a discrete wide sense stationary uncorrelated scattering (WSSUS) channel model, where the sum of delay replicas represents the received signal of the input signal weighted by independent zero-mean complex Gaussian time variant processes. Hence, if z(