simple single-user detection can be used to separate signals from different users. Theoretically, it is known that OMA does not achieve the maximum joint transmission rate in multiuser wireless systems [2]; in addition, in conventional OMA schemes, the maximum number of users is limited by the total number and the granularity of orthogonal resources. For fifth generation communication systems (5G) and beyond, Non-Orthogonal Multiple Access (NOMA) is used, which improves the performance of OMA systems. NOMA allows controllable interference by allocating non-orthogonal resources with increasing receiver complexity. Compared to OMA, the main advantages of NOMA include improving spectral efficiency and connecting a greater number of users, among others.  
There are different NOMA access techniques, among which is Sparse Code Multiple Access (SCMA) [3; 4], which directly encodes user data into multidimensional codewords using low-density codebooks. With this method, each user is assigned to a different codebook and the data within is used to map the user's bits directly to a low-density vector (sparse vector) called a codeword [5; 6; 7]. Each vector contains the zero value in the same two dimensions of all its elements, and the position of the zeros is determined randomly to prevent collisions between users [8; 9; 10]. There are different methods to design SCMA codebooks [5; 6; 7; 8; 9; 10]; in [11], we proposed using Singular Value decomposition (SVD) to improve signal detection and decoding even under adverse electromagnetic propagation conditions.  
Originally, SCMA transmits two bits on the uplink using one transmitting antenna and one receiving antenna. Once with SVD-SCMA the distances between the codewords are optimized, the next challenge is to increase the spectral efficiency by transmitting more bits simultaneously. Recently, spatial modulation [12; 13] has been used to improve the SE in NOMA systems [14; 15; 16]. SM increases the SE by allocating part of the input data stream (spatial symbol), to activate an antenna to transmit the modulation symbol. Another approach is to increase the number of bits that are encoded per codewords, which makes it more difficult to detect and decode signals at the receiver. Considering the traditional SCMA approach of six users, four radio carriers, and two bits encoded in four codewords, where each carrier carries data from three users and each user sends its information on two carriers, each pair of bits can be represented by up to 16 different combinations. If the number of bits to encode is increased to three, then each bit triplet could be represented by up to 64 combinations, producing a total IQ constellation of 512 different points to transmit 3 bits simultaneously.  
On the receiver side [17; 18; 19; 20], to detect and decode the SCMA codewords, two techniques are mainly used, on the one hand Message Passing Algorithm (MPA) and maximum likelihood (ML), and on the other hand the minimum Euclidean distance method is used. In [11], the authors proposed using a neural network-based receiver, using supervised learning for the detection and decoding of SCMA signals.  
Finally, we can mention that the authors in [14; 15] are using multiple antennas and spatial modulation to increase spectral efficiency. In [11] we showed that SVD creates codebooks with better performance than its predecessors; the motivation for this work is to demonstrate that it is possible to increase spectral efficiency by maintaining the configuration of one transmitting antenna and one receiving antenna, taking up and expanding the application of SVD to the design and construction of NOMA codebooks for communications systems beyond 5G.