Adaptive Compressed Spectrum Sensing Using Neural Networks in Cognitive Radio Networks

Abstract

In today's era of Internet of Things (IoT) applications, Cognitive Radio (CR) is recognized as the most promising technology to access the unused spectrum. Such CR networks can be easily built using Wide-band spectrum sensing. However, the processing of wide-band signals involves a high sampling rate. In such scenario, Compressed Spectrum Sensing (CSS) overcomes the challenges of real-time signal recovery and sampling. CSS uses Sparse signals which are widely used in many applications as they aid in processing large data. Beyond sparsity, all the real-world signals will have special structures (like Restricted Isometric property, Null space property). Moreover, it is very hard to determine the domain in which the signal is sparse. To find this we need a random measurement matrix which plays an important role in extracting the sparse coefficients of the signal. In this paper, a customized neural network is employed to identify the peculiar structures of sparse signals for efficient recovery in real time. The neural network learns and trains an adaptive measurement matrix from the sparse signals to reduce the sensing overhead. The transmitter and receiver systems are configured using Universal Software Radio Peripheral (USRP) boards with LabVIEW® and MATLAB® extensions for peer to peer communications. The implementation results depict the superior performance of neural network based recovery in assimilating the additional structures of real-time signals.

KEYWORDS:

• Compressed spectrum sensing (CSS)
• l₁-minimization
• Recovery
• Sparsity
• Universal filtered multi-carrier (UFMC)

Additional information

Notes on contributors

Kashavoina Kalyan

Kashavoina Kalyan is currently pursuing his bachelor's degree in electronics and communication engineering (graduating in 2021) at GRIET, Hyderabad. His areas of interest include machine learning and 5G communication. Email: [email protected]

K. V. D. S. N. K. Sai Pratheek

K V D S N K Sai Pratheek is currently pursuing his bachelor's degree in electronics and communication engineering (graduating in 2021) at GRIET, Hyderabad. He is a certified LabVIEW associate developer. His areas of interest include machine learning, cognitive radios and IoT devices. Email: [email protected]

Palumari Raju

Palumari Raju is currently pursuing his bachelor's degree in electronics and communication engineering (graduating in 2021) at GRIET, Hyderabad. His areas of interest include and embedded systems and cognitive radio networks. Email: [email protected]

Yadavalli Vivek

Yadavalli Vivek is currently pursuing his bachelor's degree in electronics and communication engineering (graduating in 2021) at GRIET, Hyderabad. His areas of interest include VLSI and cognitive radio networks. Email: [email protected]

Swetha Namburu

N Swetha is currently working as professor in department of Electronics and Communication Engineering, GRIET, Hyderabad. Her current research interests include compressed spectrum sensing in cognitive radio networks.