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Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification
(Institute of Electrical and Electronics Engineers Inc., 2019)
Despite the well-known success of sub-Nyquist sampling in reducing the hardware and computational costs of spectrum sensing, it still has the shortcoming of requiring a pre-determined spectrum sparsity level. This paper ...
Selection of waveform parameters using machine learning for 5G and beyond
(Institute of Electrical and Electronics Engineers Inc., 2019)
Flexibility is one of the essential requirements for future cellular communications technologies. Providing customized communications solutions for each user and service type cannot be possible without the flexibility in ...
Primary user emulation and jamming attack detection in cognitive radio via sparse coding
(Springer, 2020)
Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade ...
Reliability enhancement in multi-numerology-based 5G new radio using INI-aware scheduling
(Springeropen, 2019)
Multi-numerology waveform-based 5G new radio (NR) systems offer great flexibility for different requirements of users and services. However, there is a new type of problem that is defined as inter-numerology interference ...
A waveform parameter assignment framework for 6G with the role of machine learning
(IEEE-Institute of Electrical and Electronics Engineers Inc., 2020)
5G enables a wide variety of wireless communications applications and use cases. There are different requirements associated with the applications, use cases, channel structure, network and user. To meet all of the ...
Blind signal analysis
(Wiley, 2021)
Blind signal analysis (BSA) plays an essential role in wireless communication when the receiver does not know most or all of the received signal parameters. This chapter provides an in-depth understanding of BSA with ...
Estimating multi-dimensional sparsity level for spectrum sensing
(Institute of Electrical and Electronics Engineers Inc., 2023)
Identifying spectrum opportunities is a crucial element of efficient spectrum utilization for future wireless networks. Spectrum sensing offers a convenient means for revealing such opportunities. Studies showed that usage ...
Machine learning for physical layer security
(Institution of Engineering and Technology, 2023)
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