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2.部分仿真图预览
3.算法概述
车载通信系统是指在车辆之间或车辆与基础设施之间进行通信的技术。随着5G新无线通信技术（5G-NR）和多输入多输出（MIMO）技术的发展，车载通信系统的传输速率和传输可靠性得到了显著提高。本文将详细介绍基于5G-NR和MIMO的车载通信系统的MATLAB性能仿真，包括数学原理、实现过程和应用领域。
4.部分源码
ofdm_modulated_data = ofdm_mod(reshaped_modulated_data, pilot_data); %% OFDM modulation
[faded_data, channel_path_gain] = mimo_fading_channel(ofdm_modulated_data); %% Adding fading effect on the data symbols
transmitted_data = faded_data;
signal_power = 10*log10(var(transmitted_data)); %% Calculating signal power
noise_variance = (10.^(0.1.*(signal_power - snr_dB))) * noise_factor; %% Calculating noise variance
recevied_data = awgn_channel(transmitted_data, noise_variance); %% Passing the transmitted data symbols through AWGN channel
%%% OFDM Demodulation
ofdm_demodulated_data = ofdm_demod(recevied_data);
[len, ~, ~] = size(ofdm_demodulated_data);
ofdm_demodulated_data = ofdm_demodulated_data((margin + 1):(len - margin), :, :);
%%% OFDM Demodulation
%%% Initializing channel estimation parameter
channel_estimation_parameter.N_r_blk = N_r_blk;
channel_estimation_parameter.N_subc = N_subc;
channel_estimation_parameter.N_sym_sub = N_sym_sub;
channel_estimation_parameter.N_tant = N_tant;
channel_estimation_parameter.N_rant = N_rant;
channel_estimation_parameter.fft_length = fft_length;
channel_estimation_parameter.cyclic_prefix_length = cyclic_prefix_length;
channel_estimation_parameter.path_delay = path_delay;
channel_estimation_parameter.sampling_frequency = sampling_frequency;
channel_estimation_parameter.channel_path_gain = channel_path_gain;
channel_estimation_parameter.number_of_paths = number_of_paths;
channel_estimation_parameter.data_subcarrier_indices = data_subcarrier_indices;
%%% Initializing channel estimation parameter
channel_estimation_matrix = Ideal_Channel_Estimation(channel_estimation_parameter); %% Getting channel estimation matrix
%%% Preparing the ofdm demodulated data symbols for equalization purpose
processed_ofdm_demodulated_data = complex(zeros(N_r_blk * N_subc * N_sym_sub, N_rant));
for i=1:N_rant
tmp = ofdm_demodulated_data(:, :, i);
tmp = reshape(tmp, N_r_blk * N_subc * N_sym_sub, 1);
processed_ofdm_demodulated_data(:, i) = tmp;
end
%%% Preparing the ofdm demodulated data symbols for equalization purpose
if eq_mode == 1
equalized_data = ZF_Equalize(processed_ofdm_demodulated_data, channel_estimation_matrix);
elseif eq_mode == 2
equalized_data = MMSE_Equalize(processed_ofdm_demodulated_data, channel_estimation_matrix, noise_variance);
end
reshaped_equalized_data = equalized_data(:); %% Collapsing OFDM demodulated data symbols
demodulated_data = demodulator_hard(reshaped_equalized_data); %% Demodulating
viterbi_decoded_data = viterbi_decoder(demodulated_data); %% Decoding the data bits using convolutional decoder
viterbi_useful_data = viterbi_decoded_data(1:(N_bits_pframe + crc_bit)); %% Filtering the decoded data bits
[crc_decoded_data, frame_error] = step(crc_24_detector, viterbi_useful_data); %% Detecting frame error using CRC detector
%%% Counting error frames as well as total frames
if frame_error == 1
cnt2 = cnt2 + 1;
end
cnt1 = cnt1 + 1;
%%% Counting error frames as well as total frames
0X_017m
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