Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of promi-nently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing(MIMO–OFDM).One among an effective solution for channel estimation in wireless communication system,spe-cifically in different environments is Deep Learning(DL)method.This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder(CNNAE)classifier for MIMO-OFDM systems.A CNNAE classi-fier is one among Deep Learning(DL)algorithm,in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another.Improved performances are achieved by using CNNAE based channel estimation,in which extension is done for channel selection as well as achieve enhanced performances numerically,when compared with conventional estimators in quite a lot of scenar-ios.Considering reduction in number of parameters involved and re-usability of weights,CNNAE based channel estimation is quite suitable and properlyfits to the video signal.CNNAE classifier weights updation are done with minimized Sig-nal to Noise Ratio(SNR),Bit Error Rate(BER)and Mean Square Error(MSE).

圆形渐变滤光片光谱辐射计辐射定标

基于分区线性法对圆形渐变滤光片型光谱辐射计开展辐射定标研究,以解决温度范围大、工作波段宽的测量目标对该类型光谱辐射计造成的非线性问题。所提光谱辐射计的主要技术原理是将待测目标的温度区间分为多个子区间,采集目标温度区间内多个不同温度黑体对应的测量光谱,并计算各个温度下的响应度函数。在进行红外光谱测量时,将目标光谱与区间内记录的不同温度点光谱进行比对,从而确定待测目标所属温度子区间的上下限。根据子区间计算的响应度函数,通过线性插值求得待测目标的响应度函数并进行辐射定标。基于该方法的实验结果表明,待测目标理论辐亮度与使用分区线性法进行辐射定标得到的辐亮度在波长范围内的平均偏差小于1%。通过定标结果反演测量黑体的等效温度,等效温度误差小于2%。