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Non-orthogonal multiple access (NOMA) is a much hopeful scheme, which is deployed to enhance the spectral efficiency (SE) significantly, and it also enhances the massive access that has attained substantial concern from industrial and academic domains. However, the deployment of superposition coding (SC) at the receiver side resulted in interference. For reducing this interference, “multi-antenna NOMA” seems to be an emerging solution. Particularly, by using the channel state information at the transmitter, spatial beam forming could be deployed that eliminates the interference in an effective manner.

This survey analyzes the literature review and diverse techniques regarding the NOMA-based spatial modulation (SM) environment. It reviews a bunch of research papers and states a significant analysis. Initially, the analysis depicts various transmit antenna selection techniques that are contributed in different papers. This survey offers a comprehensive study regarding the chronological review and performance achievements in each contribution. The analytical review also concerns on the amplitude phase modulation (APM) selection schemes adopted in several contributions. Moreover, the objective functions adopted in the reviewed works are also analyzed. Finally, the survey extends with various research issues and its gaps that can be useful for the researchers to promote improved future works on NOMA-based SM.

This paper contributes to a review related to NOMA-based SM systems. Various techniques and performance measures adopted in each paper are analyzed and described in this survey. More particularly, the selection of transmission antenna and APM are also examined in this review work. Moreover, the defined objective function of each paper is also observed and made a chronological review as well. Finally, the research challenges along with the gaps on NOMA-based SM systems are also elaborated.

This paper presents a brief analysis of NOMA-based SM systems. To the best of the authors’ knowledge, this is the first work that uses NOMA-based SM systems to enhance SE.

Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency.

DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction.

The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels.

The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.