# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation
### Implementing Belch Sequences in NOMA Models  
Two examples of Welch sequence (WBE, Welch bound equality)-based NOMA variants are Qualcomm's RSMA and Ericsson's WSMA. Welch boundary equality is an equality used to create WBE sequences. \[B_{Welch}={{k^{2}}/{N}} \tag{2}\]  
The 3 GPP technical report [24] provides several examples of how to generate WBE sequences. Co-boundary of the cross-correlation of any of the \(K\) sequences of length \(N\) is equal to (2). The block diagram of the WSMA system's subscriber station's transmitter is depicted in Figure 5. It is optional to scramble the bits after they have been subjected to noise-correcting coding (bit-level processing). The data are then subjected to quadrature amplitude modulation (QAM), spread, and interleaved with code sequences. The WSMA system employs short WBE sequences with a low cross-correlation coefficient. Meanwhile, unlike the SCMA method, the WSMA scheme does not include sparse sequences [25]. The WSMA technique uses symbol-level signal formation, with individual vectors (sequences) of length \(L\) predetermined and assigned to individual subscribers. There are correlational features of these vectors. We will take a closer look at the WSMA scheme with \(K\) subscribers, specifically at the structure and features of the matrix of WBE sequence. Let there be \(K\) vectors of sequences \(\text{S}_{k}k=1,\ldots,K\), denoting user code sequences for which \(\|S_{k}\|=\sum_{l=1}^{L}{S_{k}}^{2}=1\), then the matrix of WBE sequences has the structure \(S=[S_{1}.S_{2},\ldots,S_{K}]\) and size \(L\ \times\ K\). The load factor of the WSMA system is determined by the \(K/L\) ratio; moreover, to support a large number of subscribers, it is necessary that \((K/L)>1\). Depending on the required characteristics of the sequence matrix \(S\), the efficiency indicator is selected, for the WSMA scheme, total square correlation (TSC) coefficient, which is bounded by equality (2) and specified by the relation  
\[TSC=\sum_{i=1}^{K}\sum_{j=1}^{K}\bigl{|}S_{i}\quad{{}^{\prime}}S_{j}\bigr{|}^{ 2}\geq{{K^{2}}/{L}} \tag{3}\]  
where the transposition and complex conjugation of elements of the vector \(S_{i}\). When this relation is satisfied, the set sequence \(S\) corresponds to the Belch boundary; however, the vectors of this set satisfy the Belch boundary not individually, but collectively. Thus, multiple sets \(S\) exist that correspond to the given performance metric and also satisfy the Welch boundary. This scheme's appeal is NOMA implementation untruths. In fact, its optimization guarantees a low correlation between vectors in the set \(S\) and optimizes the WSMA system capacity. Here, we see a sample of such a set \(S\) of WBE sequences (WSMA codebook) taken from the 3 GPP publication [26]. Up to four active subscribers (\(K=4\) sequences) of length \(L=8\) per one orthogonal resource are supported in a WSMA system with this matrix \(S\). With \(K/L=2\), the system operates at a load of 200%. Each codebook column corresponds to a subscriber and is represented by a complex WBE sequence with a unit norm \(\mu=max_{i\
eq j}P_{ij}\). Where \(P_{ij}\) the cross-correlation coefficients are potentially useful for creating WBE sequences. \(P_{ij}\) It's mentioned in locations we may find the correlation coefficients between variables. The Grassmann set is a collection of such WBE sequences, and the packing problem is the optimization issue [27]. It is worth  
Figure 5: Aspect of the WSMA scheme concerned with structural transmission. noting that subsets of WBE sequences with various correlation qualities are created by independent optimization of each efficiency indicator. Optimization of the parameter (3) yields WBE sequences with varying degrees of cross-correlation, while optimization of the parameter WBE yields sequences with a constant degree of cross-correlation [28].