ched filter and symbol spreading can also be used. For random symbol interleaver cases, the ESE multi-user detector can be applicable assuming that = The ESE multi-user detector is also applicable to bit-level interleaving scenarios. It can be shown that the computational complexity of ESE multi-user detector scales as O(K²) for J UEs and spreading factor K [23]. Unlike ESE multi-user detector, the LMMSE estimator treats the received signal as a vector and applies LMMSE estimation matrix to the transmitted signal estimation of each UE. Let ,ndvp vectors denote the priori mean and variance of each UE transmitted signal derived from LLRs. The receiver applies the following LMMSE filter to the received vector where the output of the LMMSE filter is the mean and variance vectors. mH(H)y-p As mentioned earlier, up is the a priori mean vector and Vp is a diagonal matrix whose diag- onal entries are a priori variance values for the corresponding transmitted symbols. A priori mean and variance values can be computed using the bit LLRs computed at the channel decoder. Based on the LMMSE output, the receiver generates extrinsic bit LLR values for channel decoder by marginalization. It can be shown that the computational complexity of LMMSE multi-user detector scales as O(K3 + K2.J + KJ2) for J UEs and spreading factor of K [23]. Table 3.6 summarizes the use cases and operation modes of Rel-16 NOMA candidates. The features in the characteristics column reflect the potential benefits of NOMA over Rel-15 NR MA scheme [6], which are considered in the design, evaluation and comparison of NOMA transmitter and receiver schemes. The studies conducted in 3GPP suggest that when In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. It gives the probabilities of various values of the variables in the subset without reference to the values of the other variables. This contrasts with a conditional distribut