As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next...As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next-generation (NextG) broadband mobile wireless multimedia communications. The symbols detection problem of the V-BLAST OFDM system is investigated under the frequency-selective fading environment. The joint space-frequency demultiplexing operation is proposed in the V-BLAST OFDM system. Successively, one novel half-rate rotational invariance joint space-frequency coding scheme for the V-BLAST OFDM system is proposed. By elegantly exploiting the above rotational invariance property, we derive one direct symbols detection scheme without knowing channels state information (CSI) for the frequency-selective V-BLAST OFDM system. Extensive simulation results demonstrate the validity of the novel half-rate rotational invariance joint space-frequency coding scheme and the performance of the direct symbols detection scheme.展开更多
为提高OFDM(Orthogonal Frequency Division Multiplexing)系统通信衰落信道估计性能,提出一种基于深度神经网络的信道估计模型DeReNet。通过串联深度密集网络和深度残差网络,抑制网络训练中出现的梯度爆炸和消失问题。将该模型与LS(Lea...为提高OFDM(Orthogonal Frequency Division Multiplexing)系统通信衰落信道估计性能,提出一种基于深度神经网络的信道估计模型DeReNet。通过串联深度密集网络和深度残差网络,抑制网络训练中出现的梯度爆炸和消失问题。将该模型与LS(Least Square)、FC-DNN(Full Connection Dense Neural Network)和SimNet(Simplified Deep Neural Networks)模型进行仿真实验对比,结果表明,在莱斯衰落环境下该模型的信道估计性能更好,能有效提高信道衰落的估计精度。展开更多
本文给出了瑞利衰落信道感知通信一体化(Integrated sensing and communication,ISAC)系统模型,提出感知通信一体化概率融合(Probability fusion after integrated sensing and communication,PF‐ISAC)方法,推导出PF‐ISAC信道模型。...本文给出了瑞利衰落信道感知通信一体化(Integrated sensing and communication,ISAC)系统模型,提出感知通信一体化概率融合(Probability fusion after integrated sensing and communication,PF‐ISAC)方法,推导出PF‐ISAC信道模型。从理论上证明,当感知信噪比(Signal to noise ratio,SNR)趋于无穷时,ISAC模型将退化为理想信道状态信息(Channel state information,CSI)的场景;当感知SNR趋于零时,ISAC模型将退化为CSI未知的场景。给出了PF‐ISAC系统的互信息与SNR的变化关系,随着SNR的增加,互信息从CSI未知时的信道容量逐渐逼近于理想CSI的容量。本文提出最大后验概率融合(Probability fusion after maximum a posteriori,PF‐MAP)检测方法、最大似然概率融合(Probability fusion after maximum likelihood,PF‐ML)检测方法,并与最小均方误差(Minimum mean square error,MMSE)估计‐MMSE检测(MMSE‐MMSE)方法进行比较,结果表明PF‐MAP在中低SNR时与MMSE‐MMSE性能相当,而在高SNR时,PF‐MAP优于MMSE‐MMSE;用熵误差(Entropy error,EE)评价PF‐ISAC系统的误差性能,结果表明MMSE‐MMSE、PF‐MAP、PF‐ML与理论性能极限EE均有较大差距。最后,给出感知和通信两阶段功率分配方案,当总功率给定时,感知和通信两阶段等功率分配,性能接近最佳。展开更多
文摘As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next-generation (NextG) broadband mobile wireless multimedia communications. The symbols detection problem of the V-BLAST OFDM system is investigated under the frequency-selective fading environment. The joint space-frequency demultiplexing operation is proposed in the V-BLAST OFDM system. Successively, one novel half-rate rotational invariance joint space-frequency coding scheme for the V-BLAST OFDM system is proposed. By elegantly exploiting the above rotational invariance property, we derive one direct symbols detection scheme without knowing channels state information (CSI) for the frequency-selective V-BLAST OFDM system. Extensive simulation results demonstrate the validity of the novel half-rate rotational invariance joint space-frequency coding scheme and the performance of the direct symbols detection scheme.
文摘本文给出了瑞利衰落信道感知通信一体化(Integrated sensing and communication,ISAC)系统模型,提出感知通信一体化概率融合(Probability fusion after integrated sensing and communication,PF‐ISAC)方法,推导出PF‐ISAC信道模型。从理论上证明,当感知信噪比(Signal to noise ratio,SNR)趋于无穷时,ISAC模型将退化为理想信道状态信息(Channel state information,CSI)的场景;当感知SNR趋于零时,ISAC模型将退化为CSI未知的场景。给出了PF‐ISAC系统的互信息与SNR的变化关系,随着SNR的增加,互信息从CSI未知时的信道容量逐渐逼近于理想CSI的容量。本文提出最大后验概率融合(Probability fusion after maximum a posteriori,PF‐MAP)检测方法、最大似然概率融合(Probability fusion after maximum likelihood,PF‐ML)检测方法,并与最小均方误差(Minimum mean square error,MMSE)估计‐MMSE检测(MMSE‐MMSE)方法进行比较,结果表明PF‐MAP在中低SNR时与MMSE‐MMSE性能相当,而在高SNR时,PF‐MAP优于MMSE‐MMSE;用熵误差(Entropy error,EE)评价PF‐ISAC系统的误差性能,结果表明MMSE‐MMSE、PF‐MAP、PF‐ML与理论性能极限EE均有较大差距。最后,给出感知和通信两阶段功率分配方案,当总功率给定时,感知和通信两阶段等功率分配,性能接近最佳。
