In this paper,a 5G wideband power amplifier(PA)with bandpass filtering response is synthesized using a bandwidth-extended bandpass filter as the matching network(MN).In this structure,the bandwidth(θ_(C))is defined a...In this paper,a 5G wideband power amplifier(PA)with bandpass filtering response is synthesized using a bandwidth-extended bandpass filter as the matching network(MN).In this structure,the bandwidth(θ_(C))is defined as a variable in the closedform equations provided by the microstrip bandpass filter.It can be extended over a wide range only by changing the characteristic impedances of the structure.Different from the other wideband MNs,the extension of bandwidth does not increase the complexity of the structure(order n is fixed).In addition,based on the bandwidth-extended structure,the wideband design of bandpass filtering PA is not limited to the fixed bandwidth of the specific filter structure.The theoretical analysis of the MN and the design flow of the PA are provided in this design.The fabricated bandpass filtering PA can support almost one-octave bandwidth(2-3.8 GHz),covering the two 5G bands(n41 and n78).The drain efficiency of 47%-60%and output power higher than 40 dBm are measured.Good frequency selectivity in S-parameter measurements can be observed.展开更多
In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended spee...In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.展开更多
基金supported by National Natural Science Foundations of China (No.61971052 and No.U20A20203)Key Research and Development Project of Guangdong Province (2020B0101080001)
文摘In this paper,a 5G wideband power amplifier(PA)with bandpass filtering response is synthesized using a bandwidth-extended bandpass filter as the matching network(MN).In this structure,the bandwidth(θ_(C))is defined as a variable in the closedform equations provided by the microstrip bandpass filter.It can be extended over a wide range only by changing the characteristic impedances of the structure.Different from the other wideband MNs,the extension of bandwidth does not increase the complexity of the structure(order n is fixed).In addition,based on the bandwidth-extended structure,the wideband design of bandpass filtering PA is not limited to the fixed bandwidth of the specific filter structure.The theoretical analysis of the MN and the design flow of the PA are provided in this design.The fabricated bandpass filtering PA can support almost one-octave bandwidth(2-3.8 GHz),covering the two 5G bands(n41 and n78).The drain efficiency of 47%-60%and output power higher than 40 dBm are measured.Good frequency selectivity in S-parameter measurements can be observed.
文摘In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.
