Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spec...Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.展开更多
The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail...The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high cost.This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation.To this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband spectrum.We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum.Ultimately,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.展开更多
基金supported by the Key Projects of the 2022 National Defense Science and Technology Foundation Strengthening Plan 173 (Grant No.2022-173ZD-010)the Equipment PreResearch Foundation of The State Key Laboratory (Grant No.6142101200204)。
文摘Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.
文摘The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high cost.This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation.To this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband spectrum.We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum.Ultimately,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.
