Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are use...Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are used in reality.It is almost impossible to use a CW signal to predict system performances,such as error vector magnitude(EVM),bit error rate(BER),etc.,of a transceiver front-end when dealing with complex modulated signals.This paper develops an integrated system evaluation engine(ISEE)to evaluate the system performances of a transceiver front-end or its sub-circuits.This crossdomain simulation platform is based on Matlab,advanced design system(ADS),and Cadence simulators to link the baseband signals and transceiver frond-end.An orthogonal frequency division multiplex(OFDM)modem is implemented in Matlab for evaluating the system performances.The modulated baseband signal from Matlab is dynamically fed into ADS,which includes transceiver front-end for co-simulation.The sub-block circuits of the transceiver front-end can be implemented using ADS and Cadence simulators.After system-level circuit simulation in ADS,the output signal is dynamically delivered to Matlab for demodulation.To simplify the use of the co-simulation platform,a graphical user interface(GUI)is constructed using Matlab.The parameters of the OFDM signals can be easily reconfigured on the GUI to simulate RF circuits with different modulation schemes.To demonstrate the effectiveness of the ISEE,a 3.5 GHz power amplifier is simulated and characterized using 20 MHz 16-and 64-QAM OFDM signals.展开更多
In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replac...In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.展开更多
In this paper,using cyclostationarity-based sensing method to detect the presence of Orthogonal Frequency Division Multiplexing(OFDM) signal over doubly-selective fading channels is studied.By approximating the channe...In this paper,using cyclostationarity-based sensing method to detect the presence of Orthogonal Frequency Division Multiplexing(OFDM) signal over doubly-selective fading channels is studied.By approximating the channel with Basis Expansion Model(BEM),we derive the second-order cyclostationary statistics of the received OFDM signal over doubly-selective fading channels.Theoretical analysis indicates that new cyclostationary signatures produced by Doppler spread and multipath delay can be further exploited in the detecting process.Simulation examples demonstrate that the sensing methods using channel-induced cyclostationary features provide substantial improvements on detection performance.展开更多
Considering the Gaussian asymptotic features of OFDM signals, the identification meth-od of it is proposed in this paper by using the cu-mulants of the wavelet transform coefficients in different layer in a low SNR ci...Considering the Gaussian asymptotic features of OFDM signals, the identification meth-od of it is proposed in this paper by using the cu-mulants of the wavelet transform coefficients in different layer in a low SNR circumstance. Further-more, taking the coexistence of the OFDM and Frequency Hopping （FH） signals into account, a new way to separate FH and OFDM signals is pro- posed based on SPWVD spectrum cancellation, and it can be used to estimate the FH parameters. The simulation resuks show that the OFDM and single-carrier signals can be identified with a high correct rate of 95% even at-6 dB SNR; mean-while, the separation of mixed OFDM and FH sig-nals can be achieved with a low SNR of-6 dB, and FH parameters can be estirmted accurately.It shows that the recognition performance is improved by about 5 dB compared with the traditional method.展开更多
In this paper, the design of signal constellations parameters is studied for Differential Unitary Space-Time Modulation (DUSTM) based on the design criterion of maximizing the diversity product. Further, noninteger se...In this paper, the design of signal constellations parameters is studied for Differential Unitary Space-Time Modulation (DUSTM) based on the design criterion of maximizing the diversity product. Further, noninteger searching method for the signal constellation parameters design is proposed in order to get better codes. Experimental results show that under the different Doppler spread and data transmission rate, the proposed design performs better than the previous design using integer parameters in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system over frequency-selective fading channels.展开更多
Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbule...Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.展开更多
正交频分复用(OFDM,Orthogonal Frequency Division Multiplexing)与单载波信号广泛应用于短波通信领域。针对低信噪比和多径环境下OFDM与单载波信号识别效率低的问题,本文提出了基于小波脊线的信号识别算法。本文推导了常用信号对应的...正交频分复用(OFDM,Orthogonal Frequency Division Multiplexing)与单载波信号广泛应用于短波通信领域。针对低信噪比和多径环境下OFDM与单载波信号识别效率低的问题,本文提出了基于小波脊线的信号识别算法。本文推导了常用信号对应的小波脊线幅度和脊点位置,并分析了小波脊线幅度和脊点形态。通过理论推导和仿真测试证明了OFDM与单载波信号对应小波脊线具有不同特征,对小波脊线差分、中值滤波、并利用其熵作为特征值能够有效的进行OFDM信号与单载波信号的识别。仿真结果证明该算法对输入信号点数要求低,在低信噪比和短波中等信道下识别效果具有稳健性和有效性。展开更多
基金supported by the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(No.HZQB-KCZYB-2020083).
文摘Traditionally,a continuous-wave(CW)signal is used to simulate RF circuits during the design procedure,while the fabricated circuits are measured by modulated signals in the test phase,because modulated signals are used in reality.It is almost impossible to use a CW signal to predict system performances,such as error vector magnitude(EVM),bit error rate(BER),etc.,of a transceiver front-end when dealing with complex modulated signals.This paper develops an integrated system evaluation engine(ISEE)to evaluate the system performances of a transceiver front-end or its sub-circuits.This crossdomain simulation platform is based on Matlab,advanced design system(ADS),and Cadence simulators to link the baseband signals and transceiver frond-end.An orthogonal frequency division multiplex(OFDM)modem is implemented in Matlab for evaluating the system performances.The modulated baseband signal from Matlab is dynamically fed into ADS,which includes transceiver front-end for co-simulation.The sub-block circuits of the transceiver front-end can be implemented using ADS and Cadence simulators.After system-level circuit simulation in ADS,the output signal is dynamically delivered to Matlab for demodulation.To simplify the use of the co-simulation platform,a graphical user interface(GUI)is constructed using Matlab.The parameters of the OFDM signals can be easily reconfigured on the GUI to simulate RF circuits with different modulation schemes.To demonstrate the effectiveness of the ISEE,a 3.5 GHz power amplifier is simulated and characterized using 20 MHz 16-and 64-QAM OFDM signals.
基金supported in part by the National Natural Science Foundation of China No.62001220the Natural Science Foundation of Jiangsu Province BK20200440the Fundamental Research Funds for the Central Universities No.1004-YAH20016,No.NT2020009。
文摘In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.
基金Supported by the National Natural Science Foundation of China(No.61002017 and No.61072076)the STCSM and Shanghai Rising-Star Program(10JC1414400)
文摘In this paper,using cyclostationarity-based sensing method to detect the presence of Orthogonal Frequency Division Multiplexing(OFDM) signal over doubly-selective fading channels is studied.By approximating the channel with Basis Expansion Model(BEM),we derive the second-order cyclostationary statistics of the received OFDM signal over doubly-selective fading channels.Theoretical analysis indicates that new cyclostationary signatures produced by Doppler spread and multipath delay can be further exploited in the detecting process.Simulation examples demonstrate that the sensing methods using channel-induced cyclostationary features provide substantial improvements on detection performance.
基金This paper was supported by the Fundamental Research Funds for the Central Universities （BUPT Project under Grant No.2009RC0316） the National Science Foundation of China under Ccant No. 60871081 Beijing Natural Science Foundation Design and fabrication of miniature smart antenna based on rnetarmterials under Crant No. 4112039, Nokia-BUPT Union Fund （2000009）.
文摘Considering the Gaussian asymptotic features of OFDM signals, the identification meth-od of it is proposed in this paper by using the cu-mulants of the wavelet transform coefficients in different layer in a low SNR circumstance. Further-more, taking the coexistence of the OFDM and Frequency Hopping （FH） signals into account, a new way to separate FH and OFDM signals is pro- posed based on SPWVD spectrum cancellation, and it can be used to estimate the FH parameters. The simulation resuks show that the OFDM and single-carrier signals can be identified with a high correct rate of 95% even at-6 dB SNR; mean-while, the separation of mixed OFDM and FH sig-nals can be achieved with a low SNR of-6 dB, and FH parameters can be estirmted accurately.It shows that the recognition performance is improved by about 5 dB compared with the traditional method.
基金Supported by the National Natural Science Foundation of China (No.60772062)the National Basic Research Pro-gram of China (No.2007CB310607)the Natural Science Research Fund of Jiangsu University (No. 05 KJB 510090)
文摘In this paper, the design of signal constellations parameters is studied for Differential Unitary Space-Time Modulation (DUSTM) based on the design criterion of maximizing the diversity product. Further, noninteger searching method for the signal constellation parameters design is proposed in order to get better codes. Experimental results show that under the different Doppler spread and data transmission rate, the proposed design performs better than the previous design using integer parameters in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system over frequency-selective fading channels.
基金supported by the National Natural Science Foundation of China(No.12104141).
文摘Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.
文摘正交频分复用(OFDM,Orthogonal Frequency Division Multiplexing)与单载波信号广泛应用于短波通信领域。针对低信噪比和多径环境下OFDM与单载波信号识别效率低的问题,本文提出了基于小波脊线的信号识别算法。本文推导了常用信号对应的小波脊线幅度和脊点位置,并分析了小波脊线幅度和脊点形态。通过理论推导和仿真测试证明了OFDM与单载波信号对应小波脊线具有不同特征,对小波脊线差分、中值滤波、并利用其熵作为特征值能够有效的进行OFDM信号与单载波信号的识别。仿真结果证明该算法对输入信号点数要求低,在低信噪比和短波中等信道下识别效果具有稳健性和有效性。
