搜索资源列表
mimo_bpsk_rayleigh_channel
- MIMO检测算法ML，ZF，MMSE等。采用BPSK调制，平坦瑞利信道-MIMO detection algorithm ML, ZF, MMSE, etc. Using BPSK modulation, flat Rayleigh channel
MIMOwithMLequalization
- 2X2 MIMO系统使用ML（最大似然）接受。（验证可用）-2X2 MIMO system using the ML (maximum likelihood) to accept. (Verification can be used)
Pro
- vblastpic1:tx=2,rx=2时ZF,ZF-SIC,MMSE -SICvblastpic2:tx=4,rx=4时ZF,ZF-SIC,MMSE -SIC vblastpic3 vblastpic4:MIMO vblastpic6:tx=4,rx=4, vblastpic7ZF-SIC(unordered) vblastpic8: vblastpic9: vblastpic10: vblastpic11:能 vblastpic12:tx=2,rx=2,M
ml
- ml detection mimo good
W4
- mimo code testing for the testing of a mimo system in the context to ML ZF MMSE
HW3P1
- code to test the mimo system for the ML and MZ
W5
- code to test the mimo system for the ML and MZ
icdtool
- code to test the mimo system for the ML and MZ
ML
- MIMO 最大似然算法代码 自己写的 仅做参考-MIMO maximum likelihood algorithm code
MP_DOA
- 针对多径效应的影响，提出了一种基于矩阵束的MIMO 雷达低仰角快速估计方法。该方法同时考虑了发射多径信号和接收多径信号，采用单样本数信号矢量构造了一个前后向矩阵束，并利用两个酉矩阵对该矩阵束进行降维处理，最后采用广义特征值分解的总体最小二乘法来估计目标角度。算法不需要估计数据协方差矩阵，可在低 信噪比和单样本数情况下，有效地克服多径效应，实现同时多目标低仰角估计，相比最大似然算法，避免了谱峰搜索，计算量小。仿真结果验证了该算法的有效性。-To overcome the multipath e
ML_Equalizer_2X2
- ML Receiver for 2x2 MIMO
2moreqamcodes
- the code is about ML detector of mimo technology ,,,,we need of vhdl code for all detectors
ML_VP
- 基于穷搜算法的向量扰动预编码脚本，用于测试该算法的误比特率性能。系统模型为4×4的MIMO系统。调制方式为4QAM。参数可以更改。-vector perturbation(VP) precoding algorithm based on exhaustive search (ML). 4*4 MIMO system with 4QAM.
mimo-different-detection
- 此代码展示了在mimo系统中分别用ML MMSE ZF和MMSE-SIC检测算法的不同误码率曲线-This code shows the mimo system respectively ML MMSE ZF and MMSE-SIC detection algorithm different BER curves
ML-detector
- ml detection code for MIMO system
PIC_SDR_LIANG
- 基于窄带mimo系统的最大似然检测算法，基于窄带mimo系统的最大似然检测算法-ML detection
ML.m
- Maximum likelihood receiver for MIMO spatial multiplexing system
MUD
- MIMO系统基于导频的信道估计及多用户检测，检测算法为：ZF/MMSE/ML-MIMO system multiple user detection base channel estimation.
ML-equalization-in-MIMO
- 在平稳瑞利多径信道下，用BPSK调制，用matlab 仿真由最大似然译码的性能。-It discuss a receiver structure called Maximum Likelihood (ML) decoding which gives us a better performance. We will assume that the channel is a flat fading Rayleigh multipath channel and the modulation is BP
MiMOOfdm
- STBC OFDM (included ML MAP estimation),Alamouti_FD, CIR_estimation, Tx_data_signal_generator_Alamouti_FD