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
Femtocell networks have emerged as a key technology in residential, office building or hotspot deployments that can sig- nificantly fulfill high data demands in order to offioad indoor traffic from outdoor macro cells...Femtocell networks have emerged as a key technology in residential, office building or hotspot deployments that can sig- nificantly fulfill high data demands in order to offioad indoor traffic from outdoor macro cells. However, as one of the major challenges, inter-femtocell interference gets worse in 3D in-building scenarios because of the presence of numerous interfering sources and then needs to be considered in the early network planning phase. The indoor network planning and optimization tool suite, Ranplan Small- cell~, makes accurate prediction of indoor wireless RF signal propagation possible to guide actual indoor femtocell deployments. In this paper, a new adaptive soft frequency reuse scheme in the dense femtocell networks is proposed, where multiple dense femtocells are classified into a number of groups according to the dominant interference strength to others, then the minimum subchannels with different frequency reuse factors for these groups are determined and transmit powers of the group- ing sub-channels are adaptively adjusted based on the strength to mitigate the mutual inter- ference. Simulation results show the proposed scheme yields great performance gains in terms of the spectrum efficiency relative to the legacy soft frequency reuse and universal fre- quency reuse.展开更多
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.
基金supported by the EU-FP7 iPLAN under Grant No.230745EU-FP7 IAPP@RANPLAN under Grant No.218309
文摘Femtocell networks have emerged as a key technology in residential, office building or hotspot deployments that can sig- nificantly fulfill high data demands in order to offioad indoor traffic from outdoor macro cells. However, as one of the major challenges, inter-femtocell interference gets worse in 3D in-building scenarios because of the presence of numerous interfering sources and then needs to be considered in the early network planning phase. The indoor network planning and optimization tool suite, Ranplan Small- cell~, makes accurate prediction of indoor wireless RF signal propagation possible to guide actual indoor femtocell deployments. In this paper, a new adaptive soft frequency reuse scheme in the dense femtocell networks is proposed, where multiple dense femtocells are classified into a number of groups according to the dominant interference strength to others, then the minimum subchannels with different frequency reuse factors for these groups are determined and transmit powers of the group- ing sub-channels are adaptively adjusted based on the strength to mitigate the mutual inter- ference. Simulation results show the proposed scheme yields great performance gains in terms of the spectrum efficiency relative to the legacy soft frequency reuse and universal fre- quency reuse.
