New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity
In NOMA achieving fairness in data transmission for all users is one of the key challenges. It implies a fair allocation of resources among users according to a given criterion. NOMA systems are based on the discreteness of the data source, since the specific nature of data allows to extract informa...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824006811 |
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| author | Mikhail Bakulin Taoufik Ben Rejeb Vitaly Kreyndelin Denis Pankratov Aleksei Smirnov |
| author_facet | Mikhail Bakulin Taoufik Ben Rejeb Vitaly Kreyndelin Denis Pankratov Aleksei Smirnov |
| author_sort | Mikhail Bakulin |
| collection | DOAJ |
| description | In NOMA achieving fairness in data transmission for all users is one of the key challenges. It implies a fair allocation of resources among users according to a given criterion. NOMA systems are based on the discreteness of the data source, since the specific nature of data allows to extract information in overloaded systems. Well-known C.E. Shannon capacity equation does not take into account the discrete nature of NOMA group signals, considering them as Gaussian source of information. Therefore, it does not take into account the characteristics of discrete signals used in NOMA systems, which makes it difficult to solve the optimization problem, especially for types of NOMA that use code division. For this task it is necessary to use DCMC capacity. When optimizing total capacity of DCMC, individual properties of users are not taken into account, as a result of which the allocation of resources with such optimization might not be fair. This paper proposes an approach to optimizing NOMA based on the analysis of an-individual mutual information (capacity). The proposed optimization approach allows analyzing the individual characteristics of each user in order to improve NOMA system performance by optimizing NOMA signals parameters. |
| format | Article |
| id | doaj-art-7524d60b1755476786e3739a7abf87aa |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-7524d60b1755476786e3739a7abf87aa2024-11-15T06:11:05ZengElsevierAlexandria Engineering Journal1110-01682024-11-01107215224New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacityMikhail Bakulin0Taoufik Ben Rejeb1Vitaly Kreyndelin2Denis Pankratov3Aleksei Smirnov4Moscow Technical University of Communications and Informatics (MTUCI), Moscow 111024, RussiaMoscow Technical University of Communications and Informatics (MTUCI), Moscow 111024, RussiaMoscow Technical University of Communications and Informatics (MTUCI), Moscow 111024, RussiaMoscow Technical University of Communications and Informatics (MTUCI), Moscow 111024, RussiaCorresponding author.; Moscow Technical University of Communications and Informatics (MTUCI), Moscow 111024, RussiaIn NOMA achieving fairness in data transmission for all users is one of the key challenges. It implies a fair allocation of resources among users according to a given criterion. NOMA systems are based on the discreteness of the data source, since the specific nature of data allows to extract information in overloaded systems. Well-known C.E. Shannon capacity equation does not take into account the discrete nature of NOMA group signals, considering them as Gaussian source of information. Therefore, it does not take into account the characteristics of discrete signals used in NOMA systems, which makes it difficult to solve the optimization problem, especially for types of NOMA that use code division. For this task it is necessary to use DCMC capacity. When optimizing total capacity of DCMC, individual properties of users are not taken into account, as a result of which the allocation of resources with such optimization might not be fair. This paper proposes an approach to optimizing NOMA based on the analysis of an-individual mutual information (capacity). The proposed optimization approach allows analyzing the individual characteristics of each user in order to improve NOMA system performance by optimizing NOMA signals parameters.http://www.sciencedirect.com/science/article/pii/S1110016824006811B5G6GNOMAMutual informationDiscrete-continuous channel |
| spellingShingle | Mikhail Bakulin Taoufik Ben Rejeb Vitaly Kreyndelin Denis Pankratov Aleksei Smirnov New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity Alexandria Engineering Journal B5G 6G NOMA Mutual information Discrete-continuous channel |
| title | New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity |
| title_full | New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity |
| title_fullStr | New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity |
| title_full_unstemmed | New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity |
| title_short | New approach to NOMA optimization based on individual discrete input continuous output memoryless channel capacity |
| title_sort | new approach to noma optimization based on individual discrete input continuous output memoryless channel capacity |
| topic | B5G 6G NOMA Mutual information Discrete-continuous channel |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824006811 |
| work_keys_str_mv | AT mikhailbakulin newapproachtonomaoptimizationbasedonindividualdiscreteinputcontinuousoutputmemorylesschannelcapacity AT taoufikbenrejeb newapproachtonomaoptimizationbasedonindividualdiscreteinputcontinuousoutputmemorylesschannelcapacity AT vitalykreyndelin newapproachtonomaoptimizationbasedonindividualdiscreteinputcontinuousoutputmemorylesschannelcapacity AT denispankratov newapproachtonomaoptimizationbasedonindividualdiscreteinputcontinuousoutputmemorylesschannelcapacity AT alekseismirnov newapproachtonomaoptimizationbasedonindividualdiscreteinputcontinuousoutputmemorylesschannelcapacity |