CCSAE-Based Un-Cooperative Communication Behavior Recognition Scheme

Cheng, Kaixin; Zhu, Lei; Wang, Wenyu; Chen, Pu

doi:10.1109/icccs55155.2022.9846728

Cited by 1 publication

(3 citation statements)

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“…It begins by taking in a signal that goes by the name s(t) at the input, and then it proceeds to make a decision in the binary form regarding whether or not the PU is present. It is conceivable to represent it using [33] as a model.…”

Section: Uncooperative Cognitive Radio Network (Un-ccrn)mentioning

confidence: 99%

“…The generalized Marcum Q Function Qu. The chance of detection for a Rayleigh distribution is expressed as [33].…”

Section: ( ) ( ) ( ) ( )mentioning

confidence: 99%

“…The overall detection and false alarm probability when employing the k-out-of-N fusion rule is provided in the reference [33][34]. The reason why this is the case is because the rule takes into account every potential combination of the inputs, which is the reason why this is the case.…”

Section: Cooperative Cognitive Radio Network (Ccrn)mentioning

confidence: 99%

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Improving 6G Network Spectrum Efficiency with Non-Cooperative and Cooperative Spectrum Sharing Using NOMA and Massive-MIMO

Hassan,

Singh,

Bilal

et al. 2023

ICST Transactions on Mobile Communications and Applications

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There is an expectation that standards for sixth-generation (6G) wireless communication networks in the future would give previously unheard-of speeds for the flow of information as well as spectrum optimization. This will present new issues for 6G networks. Non-orthogonal multiple access (NOMA) is one of the most efficient ways to boost the spectrum efficiency (SE) of a 6G network. The most promising contemporary technologies, such as cognitive radio (CR) and multiple access, can be used to improve SE. When NOMA's network-oriented multi-access capabilities are combined with those of the Cognitive Radio Network (CRN), a new era of efficient communication is expected to dawn. To improve the spectral efficiency (SE) of the NOMA DL power domain (PD), this work presents two distinctive strategies that are used in conjunction with un-cooperative and cooperative CRN (Un-CCRN and CCRN) in the event that one primary user (PU) is unable to receive through the dedicated channel due to interference or noise. Users' distances, power placement coefficients, and transmit powers (TPs) vary across the proposed three network topologies, and over the proposed three network sizes of 128x128, 256x256, and 512x512 Massive Multiple Input Multiple Output (M-MIMO). Performance is analyzed while simultaneously considering channel instability and successive interference cancellation (SIC). The channels of fading are modelled after frequency-dependent Rayleigh fading. MATLAB is used to determine the proposed model's SE. With 128x128, 256x256, and 512x512 M-MIMO integrated into the DL NOMA system, the system's SE performance is improved by 73%, 82%, and 87%, respectively; with the Un-CCRN NOMA model, the improvement is 75%, 83%, and 88%; and with the CCRN-NOMA model, the improvement is 75.8%, 84%, and 88.3%. The SE is significantly improved by employing M-MIMO technology. The acquired expressions agree with the outcomes of the provided Monte Carlo simulations, providing further evidence for the validity of our investigation.

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Section: Uncooperative Cognitive Radio Network (Un-ccrn)mentioning

confidence: 99%

“…The generalized Marcum Q Function Qu. The chance of detection for a Rayleigh distribution is expressed as [33].…”

Section: ( ) ( ) ( ) ( )mentioning

confidence: 99%