Deep learning-based sequential models for multi-user detection with M-PSK for downlink NOMA wireless communication systems

Non-orthogonal multiple access (NOMA) techniques have the potential to achieve large connectivity requirements for future-generation wireless communication. NOMA detection techniques require conventional successive interference cancellation (SIC) techniques for uplink and downlink transmissions on t...

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Vydáno v:	Annales des télécommunications Ročník 79; číslo 5-6; s. 327 - 341
Hlavní autoři:	Panda, Bibekananda, Singh, Poonam
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.06.2024 Springer Nature B.V
Témata:	Artificial intelligence Artificial neural networks Bit error rate Circuits Code Division Multiple Access Communications Engineering Communications networks Communications systems Computer Communication Networks Deep learning Downlinking Energy consumption Engineering Fading Fast Fourier transformations Fourier transforms Information and Communication Information Systems and Communication Service Internet of Things Machine learning Mean square errors Networks Neural networks Nonorthogonal multiple access Orthogonal Frequency Division Multiplexing Performance evaluation Phase shift keying R & D/Technology Policy Radio networks Receivers & amplifiers Sensors Signal detection Signal,Image and Speech Processing Spectrum allocation Wireless communication systems Wireless communications Deep learning Long short-term memory Gated recurrent unit NOMA Bi-directional long short-term memory Signal detection
ISSN:	0003-4347, 1958-9395
On-line přístup:	Získat plný text
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