A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decompo...A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.展开更多
This paper considers a massive single-input multiple-output(SIMO)system,where multiple singleantenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas.Different from the ...This paper considers a massive single-input multiple-output(SIMO)system,where multiple singleantenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas.Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading,we propose a joint transceiver design method based on machine learning,requiring a limited number of channel realizations.In the proposed method,the multiple transmitters,the channel,and the receiver are represented with a deep neural network(NN),and an autoencoder is adopted to minimize the end-to-end transmission error probability.Besides,the relationship between the number of training samples and the transmission error probability is analyzed based on the confidence interval method.Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios,and is more robust against the channel parameters variation compared with the existing methods.展开更多
An integrated single-inductor dual-output （SIDO） switching DC-DC converter is presented. The outputs are specified with 1.2 V/400 mA and 1.8 V/200 mA. A decoupling small signal model is proposed to analyze the multi...An integrated single-inductor dual-output （SIDO） switching DC-DC converter is presented. The outputs are specified with 1.2 V/400 mA and 1.8 V/200 mA. A decoupling small signal model is proposed to analyze the multi-loop system and to design the on-chip compensators. An average current control mode is introduced with lossless, continuous current detection. The converter has been fabricated in a 0.25μm 2P4M CMOS process. The power efficiency is 86% at a total output power of 840 mW while the output ripples are about 40 mV at an oscillator frequency of 600 kHz.展开更多
文摘A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.
基金The work was supported in part by the Key Area R&D Program of Guangdong Province with Grant No.2018B030338001by the National Key R&D Program of China with Grant No.2018YFB1800800+2 种基金y Natural Science Foundation of China with grant NSFC-61629101by Guangdong Research Project No.2017ZT07X152by Shenzhen Key Lab Fund No.ZDSYS201707251409055.
文摘This paper considers a massive single-input multiple-output(SIMO)system,where multiple singleantenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas.Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading,we propose a joint transceiver design method based on machine learning,requiring a limited number of channel realizations.In the proposed method,the multiple transmitters,the channel,and the receiver are represented with a deep neural network(NN),and an autoencoder is adopted to minimize the end-to-end transmission error probability.Besides,the relationship between the number of training samples and the transmission error probability is analyzed based on the confidence interval method.Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios,and is more robust against the channel parameters variation compared with the existing methods.
文摘An integrated single-inductor dual-output （SIDO） switching DC-DC converter is presented. The outputs are specified with 1.2 V/400 mA and 1.8 V/200 mA. A decoupling small signal model is proposed to analyze the multi-loop system and to design the on-chip compensators. An average current control mode is introduced with lossless, continuous current detection. The converter has been fabricated in a 0.25μm 2P4M CMOS process. The power efficiency is 86% at a total output power of 840 mW while the output ripples are about 40 mV at an oscillator frequency of 600 kHz.
