正交频分复用技术(OFDM,Orthogonal Frequency Division Multiplexing)是一种有效的并行多载波传输方案,然而,OFDM技术存在一个主要缺点,即发送信号存在较高的峰值平均功率比(PAPR,Peak-to-Average Power Ratio)。研究表明高带宽效率的...正交频分复用技术(OFDM,Orthogonal Frequency Division Multiplexing)是一种有效的并行多载波传输方案,然而,OFDM技术存在一个主要缺点,即发送信号存在较高的峰值平均功率比(PAPR,Peak-to-Average Power Ratio)。研究表明高带宽效率的限幅类技术着眼于直接降低OFDM信号的峰均比,而幅度和相位预失真方法则是从放大器的角度出发,对放大器进行预失真处理,增大功率放大器的线性范围。这里提出将限幅类技术和预失真处理进行联合实现,从而在系统PAPR减小、失真补偿和误码率之间取得较好的折中。展开更多
In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Divi...In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing(OFDM)ssystem.One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio(PAPR).The clipping is a simple method for the PAPR reduction.However,an effect of the clipping is nonlinear distortion,and estimations for transmitting symbols are difficult despite a Maximum Likelihood(ML)detection at the receiver.The DNN based online signal detection uses the offline learning model where all weights and biases at fully-connected layers are set to overcome nonlinear distortions by using training data sets.Thus,this paper introduces the required processes for the online signal detection and offline learning,and compares error performances with the ML detection in the clipping-based OFDM systems.In simulation results,the DNN based signal detection has better error performance than the conventional ML detection in multi-path fading wireless channel.The performance improvement is large as the complexity of system is increased such as huge Multiple Input Multiple Output(MIMO)system and high clipping rate.展开更多
正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)信号具有较高的峰均功率比(Peak to Average Power Ratio,PAPR),不仅影响功率放大器(High Power Amplifier,HPA)的工作效率,而且HPA使得OFDM信号产生严重的非线性失真,...正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)信号具有较高的峰均功率比(Peak to Average Power Ratio,PAPR),不仅影响功率放大器(High Power Amplifier,HPA)的工作效率,而且HPA使得OFDM信号产生严重的非线性失真,导致系统的误比特率(Bite Error Rate,BER)增大.本文基于限幅和压缩感知(Compressive Sensing,CS)提出了改进的补偿算法,发送端采用限幅降低信号的PAPR,接收端首先采用改进的逆模型方式减小HPA引入的非线性失真,再采用CS抵消由限幅引入的信号失真.仿真表明,所提方法不仅明显降低了OFDM信号的PAPR,而且有效提高了系统的BER性能.展开更多
正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的主要缺点之一就是有较高的峰均功率比(Peak to Average Power Ratio,PAPR),降低了功率放大器(High Power Amplifier,HPA)的工作效率,同时HPA引入的非线性失真,恶...正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的主要缺点之一就是有较高的峰均功率比(Peak to Average Power Ratio,PAPR),降低了功率放大器(High Power Amplifier,HPA)的工作效率,同时HPA引入的非线性失真,恶化了系统的误比特率(Bite Error Rate,BER)性能.本文所提算法将限幅和HPA引入的非线性失真视为一个整体来考虑,利用与限幅噪声在时域上的近似稀疏性,对整个非线性过程进行建模.发送端通过限幅降低了OFDM信号的PAPR,在接收端,选取受噪声干扰小的可靠性观测向量,最小化信道噪声的影响,基于非线性模型计算得到的参数,利用压缩感知(Compressive Sensing,CS)算法能有效地恢复总的非线性失真信号,提升了系统的BER性能.展开更多
文摘正交频分复用技术(OFDM,Orthogonal Frequency Division Multiplexing)是一种有效的并行多载波传输方案,然而,OFDM技术存在一个主要缺点,即发送信号存在较高的峰值平均功率比(PAPR,Peak-to-Average Power Ratio)。研究表明高带宽效率的限幅类技术着眼于直接降低OFDM信号的峰均比,而幅度和相位预失真方法则是从放大器的角度出发,对放大器进行预失真处理,增大功率放大器的线性范围。这里提出将限幅类技术和预失真处理进行联合实现,从而在系统PAPR减小、失真补偿和误码率之间取得较好的折中。
基金This work was supported by Institute for Information&communications Technology Promotion(IITP)grant funded by the Korea government(MSIT)(No.2017-0-00217,Development of Immersive Signage Based on Variable Transparency and Multiple Layers)was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2018-0-01423)supervised by the IITP(Institute for Information&communications Technology Promotion).
文摘In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing(OFDM)ssystem.One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio(PAPR).The clipping is a simple method for the PAPR reduction.However,an effect of the clipping is nonlinear distortion,and estimations for transmitting symbols are difficult despite a Maximum Likelihood(ML)detection at the receiver.The DNN based online signal detection uses the offline learning model where all weights and biases at fully-connected layers are set to overcome nonlinear distortions by using training data sets.Thus,this paper introduces the required processes for the online signal detection and offline learning,and compares error performances with the ML detection in the clipping-based OFDM systems.In simulation results,the DNN based signal detection has better error performance than the conventional ML detection in multi-path fading wireless channel.The performance improvement is large as the complexity of system is increased such as huge Multiple Input Multiple Output(MIMO)system and high clipping rate.
文摘正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)信号具有较高的峰均功率比(Peak to Average Power Ratio,PAPR),不仅影响功率放大器(High Power Amplifier,HPA)的工作效率,而且HPA使得OFDM信号产生严重的非线性失真,导致系统的误比特率(Bite Error Rate,BER)增大.本文基于限幅和压缩感知(Compressive Sensing,CS)提出了改进的补偿算法,发送端采用限幅降低信号的PAPR,接收端首先采用改进的逆模型方式减小HPA引入的非线性失真,再采用CS抵消由限幅引入的信号失真.仿真表明,所提方法不仅明显降低了OFDM信号的PAPR,而且有效提高了系统的BER性能.
文摘正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的主要缺点之一就是有较高的峰均功率比(Peak to Average Power Ratio,PAPR),降低了功率放大器(High Power Amplifier,HPA)的工作效率,同时HPA引入的非线性失真,恶化了系统的误比特率(Bite Error Rate,BER)性能.本文所提算法将限幅和HPA引入的非线性失真视为一个整体来考虑,利用与限幅噪声在时域上的近似稀疏性,对整个非线性过程进行建模.发送端通过限幅降低了OFDM信号的PAPR,在接收端,选取受噪声干扰小的可靠性观测向量,最小化信道噪声的影响,基于非线性模型计算得到的参数,利用压缩感知(Compressive Sensing,CS)算法能有效地恢复总的非线性失真信号,提升了系统的BER性能.
