ered pro- cess. If the process is Gaussian then the random variables Zk are Gaussian and stochastically independent. This result generalizes the Karhunen-Loève transform. An important example of a centered real stochastic process on [0, 1] is the Wiener process, where the Karhunen - Loève theorem can be used to provide a canon- ical orthogonal representation for it. In this case, the expansion consists of sinusoidal functions [22]. 582 Chapter 4 can be implemented using low-complexity hybrid analog/digital architectures, that is, with a small number of RF chains. The main idea of multi-layer precoding is to design the preco- der matrix as a product of three precoding matrices (layers) where each layer is designed to achieve only one precoding objective, for example, maximizing desired signal power, mini- mizing inter-cell interference, or minimizing multi-user interference [48]. Inter - cell interference management Desired signal Multi - user interference management beamforming Minimize inter - cell interference Minimizing multi - user interference 4.1.9.5 Large-Scale (Massive) MIMO Systems Massive MIMO is the generalization of a multi-user MIMO system that serves multiple users through spatial multiplexing over a channel with favorable propagation 18 conditions using time-division duplex scheme and relying on channel reciprocity and uplink refer- ence signals to obtain CSI of each user. The base station is equipped with Nantennas anten- nas to communicate with Nuser (typically modeled with single-antenna) UEs on each time/ frequency resource, where Nuser <<Nantennas. Each base station in the network operates indi- vidually and processes its signals using linear transmit precoding and linear receive com- bining [16,52]. By coherent processing of the signals over the array, transmit precoding can be used in the downlink to focus each signal at its target user, and receive combining can be used in the uplink to distinguish between signals received from different user term- inals, thus the larger the number of anten