abstract

• This paper presents a neuromorphic spectrum occupancy learning using convolutional neural networks and long short-term memory, in a cognitive radio enabled cloud radio access system. Cloud radio access networks can prove useful in future generation wireless communication systems, because of their distributed signal processing capability. Owing to its excellent noise immunity property, cyclostationarity-based spectrum sensing is utilized as the method to detect the active sub-bands at the baseband unit. However, the accuracy of sensing is corrupted due to impairments, such as chromatic dispersion, caused by the optical fiber front-haul channel. Hence, application of deep learning techniques like convolutional neural networks and long short-term memory are necessary to compensate for the front-haul channel. Our simulation results indicate that the proposed method can lead to improved signal detection in presence of optical fiber impairment in cloud radio system. © 2022 IEEE.

authors

• Castillo Soria Francisco Rubén
• Datta J.

publication date

• 2022-01-01

published in

• 2022 IEEE 7th International conference for Convergence in Technology, I2CT 2022  Proceedings

keywords

• Active Interference Cancellation; Cloud Radio Access Network; Cyclostationarity; Deep Learning; Fast Convolution Filter Bank Chemical detection; Chromatic dispersion; Cognitive radio; Convolution; Convolutional neural networks; Learning systems; Long short-term memory; Optical fibers; Radio; Radio access networks; Radio interference; Signal detection; Active interference cancellations; Cloud radio access network; Convolution filters; Cyclostationarity; Deep learning; Fast convolution; Fast convolution filter bank; Filters bank; Neuromorphic; Radio access networks; Filter banks

Digital Object Identifier (DOI)

• 10.1109/I2CT54291.2022.9825459

PubMed ID

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