Estimating Multi-Dimensional Sparsity Level for Spectrum Sensing

Aygül M. A., Nazzal M., ARSLAN H.

2023 IEEE Wireless Communications and Networking Conference, WCNC 2023, Glasgow, England, 26 - 29 March 2023, vol.2023-March identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2023-March
  • Doi Number: 10.1109/wcnc55385.2023.10118987
  • City: Glasgow
  • Country: England
  • Keywords: Compressive sensing, machine learning, realworld measurements, sparsity level estimation, spectrum sensing
  • Istanbul Technical University Affiliated: Yes


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 of the spectrum has a high correlation over multi-dimensions, including time and frequency. However, multi-dimensional spectrum sensing requires high-cost processes. Applying compressive sensing allows for subNyquist sampling. This reduces associated training, feedback, and computation overheads of a spectrum sensing method. However, the accuracy of the signal sparsity assumption and knowledge of the precise sparsity level are necessary for the applicability of compressive sensing. It is common practice to assume a level of known sparsity. On the other hand, in reality, this presumption is incorrect. This paper proposes a method for estimating the multidimensional sparsity for spectrum sensing. By extrapolating it from its counterpart with respect to a compact discrete Fourier basis, the proposed method calculates the sparsity level over a dictionary. A machine learning estimation method achieves this inference. Extensive simulations validate a high-quality sparsity estimation. To validate this observation, real-world measurements are used, where one of the biggest Turkish telecom operators has private uplink bands in the frequency range between 852-856 MHz.