static over a time-frequency interval ${\tau_L}_c \gg \tau_c$. As shown in the following subsections, these channel characteristics can be leveraged to simplify both the channel estimation and the precoding/combining processes. In particular, and transmissions between and are organized in a half-duplex operation whereby each coherence interval is split into three phases, namely, the training phase, the payload data transmission phase and the payload data transmission phase, and every *large-scale coherence interval* ${\tau_L}_c$ the system performs an estimation of the large-scale parameters of the channel (see Fig. \[fig:TDD\_frame\]). In the training phase, all transmit training orthogonal pilots allowing the to estimate the propagation channels to every in the network[^2]. Subsequently, these channel estimates are used to detect the signals transmitted from the in the payload data transmission phase and to compute the precoding filters governing the payload data transmission. Not shown are guard intervals between and transmissions.

Channel Model {#subsec:Channel_model}
-------------

MmWave propagation is characterized by very high distance-based propagation losses that lead to sparse scattering multipath propagation. Furthermore, the use of mmWave transmitters and receivers with large tightly-packet antenna arrays results in high antenna correlation levels. These characteristics make most of the statistical channel models used in conventional sub-6 GHz 