linear processing. Since U is unitary, UHn still has the same variance as n, thus, the SVD precoding does not result in noise enhancement. Due to substantial feedback and the complexity of singular value decomposition of the channel matrix, the SVD precoding is not considered a viable precoding scheme. This procedure is illustrated in Figure 10-31. 10.4 Multi-antenna transmission schemes X = Vs MIMO Channel MIMO Receiver Unitary Precoding Feedback Channel Estimation and Quantization of V FIGURE 10-31 An illustration of unitary precoding procedures The precoding algorithms for multi-user MIMO can be classified into linear and nonlinear classes. Linear precoding schemes can achieve reasonable performance with lower complexity compared to nonlinear precoding techniques. Linear precoding includes unitary and zero-forcing precoding schemes. Nonlinear precoding can achieve near optimal capacity at the expense of increased complexity. Nonlinear precoding is designed based on the concept of dirty paper coding, where any known interference at the transmitter can be canceled, if the optimal precoding scheme is applied to the transmit signal. Unitary precoding includes unitary and semi-unitary precoding both of which are a simple extension of SVD precoding in single-user MIMO, with the addition of the SDMA-based user scheduling technique. The SDMA-based opportunistic user scheduling technique groups near orthogonal users to avoid intra-group interferences at the cost of minimal signaling overhead, which results in higher performance relative to the single-user MIMO. For example, it can increase diversity order to approximately the number of transmit antennas, even with simple linear decoding at the receiver. The zero-forcing precoding consists of zero-forcing and regularized zero-forcing precoding. If the transmitter has perfect knowledge of the downlink channel state information, ZF-based precoding can achieve near channel capacity performance when the number of users is large. With limited channel state information at the