基于Matalb实现MIMO-OFDM系统容量仿真

文章介绍了在发送端未知信息状态下MIMO系统的容量仿真,包括平均容量和中断容量,并探讨了瑞利分布和莱斯分布下的多径信道仿真。此外,详细展示了16QAM信号星座图的绘制以及不同条件下的解调效果,同时分析了误码率与信噪比的关系。

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⛄ 内容介绍

1.发送端未知信息状态情况下MIMO系统容量仿真

2.MIMO系统平均容量与中断容量仿真

3.瑞利分布,莱斯分布

4.多径信道仿真

5.指数信道PDP

6.绘制发送端16QAM信号星座图

7.制解调后16QAM星座图

8.绘制加窗/不加窗,不同滚降系数的解调后16QAM星座图

9.误码率计算

⛄ 部分代码

clf;

clear;

%% 定义参数

carrier_count = 200;

symbols_per_carrier = 20;

bits_per_symbol = 4;

IFFT_bin_length = 512;

%%=================== OFDM信号产生======================

baseband_out_length = carrier_count * symbols_per_carrier * bits_per_symbol;

%计算传输数据总的比特数,为200*20*4=16000比特。16000比特的构成为20个OFDM符号,每个OFDM符号200个子载波,每个子载波传输4比特信息。

carriers=(1:carrier_count)+(floor(IFFT_bin_length/4)-floor(carrier_count/2));

%计算OFDM符号子载波的序号,carriers中存放的序号是29~228。

conjugate_carriers = IFFT_bin_length - carriers + 2;

%计算OFDM符号子载波的序号,conjugate_carriers中存放的序号是282~481。

rand( 'twister',0);

baseband_out=round(rand(1,baseband_out_length));

%产生16000比特待传输的二进制比特流。

%这里存放的是发送的二进制信号与后面解调后的二进制信号比较,可以计算误码率。

⛄ 运行结果

P中断容量表示信道可靠性为(1-p)的信息传输速率。如上图所示,当p值越高时,即信道可靠性(1-p)越小,能达到的容量就越大。中断容量越大,当信道以一定的信道速率传输时,发生中断的概率就越小。

如上图所示,误码率随着信噪比SNR变大而变小。信噪比越大,证明信号与噪声的比值越大,证明信号在信道上受到噪声的干扰就越小,得到的误码率就会越低。

如上图所示,当信噪比SNR越大时,解调后16QAM星座图,与理想的16QAM星座图(发送端16QAM星座图)越接近。信噪比SNR越大,当信号功率值一定时,噪声值越小,证明受到的干扰越小。

⛄ 参考文献

⛳️ 完整代码

❤️部分理论引用网络文献,若有侵权联系博主删除

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MIMO OFDM Simulator: OFDM.m: OFDM Simulator (outer function) create_channel.m: Generates a Rayleigh fading frequency-selective channel, parametrized by the antenna configuration, the OFDM configuration, and the power-delay profile. svd_decompose_channel.m: Since full channel knowledge is assumed, transmission is across parallel singular value modes. This function decomposes the channel into these modes. BitLoad.m: Apply the bit-loading algorithm to achieve the desired bit and energy allocation for the current channel instance. ComputeSNR.m: Given the subcarrier gains, this simple function generates the SNR values of each channel (each singular value on each tone is a separate channel). chow_algo.m: Apply Chow's algorithm to generate a particular bit and energy allocation. EnergyTableInit.m: Given the SNR values, form a table of energy increments for each channel. campello_algo.m: Apply Campello's algorithm to converge to the optimal bit and energy allocation for the given channel conditions. ResolvetheLastBit.m: An optimal bit-loading of the last bit requires a unique optimization. modulate.m: Modulate the random input sequence according to the bit allocations for each channel. ENC2.mat: BPSK Modulator ENC4.mat: 4-QAM Modulator (Gray coded) ENC16.mat: 16-QAM Modulator (Gray coded) ENC64.mat: 64-QAM Modulator (Gray coded) ENC256.mat: 256-QAM Modulator (Gray coded) precode.m: Precode the transmitted vector at each time instance by filtering the modulated vector with the right-inverse of the channel's right singluar matrix. ifft_cp_tx_blk.m: IFFT block of the OFDM system. channel.m: Apply the channel to the OFDM frame. fft_cp_rx_blk.m: FFT block of the OFDM system. shape.m: Complete the diagonalization of the channel by filtering the received vector with the left-inverse of the channel's left singular matrix. demodulate.m: Perform a nearest neighbor search knowing the transmit constellation used.
OFDMMIMOMATLAB仿真程序MIT研究人员贡献-MIMO_OFDM.rar OFDM MIMO MATLAB仿真程序 MIT研究人员贡献: Adaptive Loading in MIMO/OFDM Systems Prateek Bansal Andrew Brzezinski prateek@stanford.edu brzezin@stanford.edu December 13, 2001 Abstract Orthogonal Frequency Division Multiplexing is a powerful technique employed in communications systems suering from frequency selectivity. Combined with multiple antennas at the transmitter and receiver as well as adaptive modulation, OFDM proves to be robust against channel delay spread. Furthermore, it leads to signicant data rates with improved bit error performance over links having only a single antenna at both the transmitter and receiver. This project demonstrates OFDM with adaptive modulation applied to Multiple-Input Multiple- Output systems. We apply an optimization algorithm to obtain a bit and power allocation for each subcarrier assuming instantaneous channel knowledge. The analysis and simulation is considered in two stages. The rst stage involves the application of a variable-rate variable-power MQAM technique for a Single-Input Single-Output OFDM system. This is compared with the performance of xed OFDM transmission where a constant rate is applied to each subcarrier. The second stage applies adaptive modulation to a general MIMO system by making use of the Singular Value Decomposition to separate the MIMO channel into parallel subchannels. For a two-input antenna, two-output antenna system, the performance is compared with the performance of a system using selection diversity at the transmitter and maximal ratio combining at the receiver.
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