《MIMO-OFDM无线通信技术及MATLAB实现》随书源码-第11章 空间复用的MIMO系统的信号检测.rar

function [X_hat]=SD_detector(y,H,nT) % Input parameters % y : received signal, nRx1 % H : Channel matrix, nRxnT % nT : number of Tx antennas % Output parameter % X_hat : estimated signal, nTx1 global x_list; % candidate symbols in real constellations global x_now; % temporary x_vector elements global x_hat; % inv(H)*y global x_sliced; % sliced x_hat global x_pre; % x vectors obtained in the previous stage global real_constellation; % real constellation global R; % R in the QR decomposition global radius_squared; % radius^2 global x_metric; % ML metrics of previous stage candidates global len; % nT*2 QAM_table2 = [-3-3j, -3-j, -3+3j, -3+j, -1-3j, -1-j, -1+3j, -1+j,3-3j, ... 3-j, 3+3j, 3+j, 1-3j, 1-j, 1+3j, 1+j]/sqrt(10); % 16-QAM real_constellation = [-3 -1 1 3]/sqrt(10); y =[real(y); imag(y)]; % y : complex vector -> real vector H =[real(H) -(imag(H)) ; imag(H) real(H)]; % H : complex vector -> real vector len = nT*2; % complex -> real x_list = zeros(len,4); % 4 : real constellation length, 16-QAM x_now = zeros(len,1); x_hat = zeros(len,1); x_pre = zeros(len,1); x_metric = 0; [Q,R] = qr(H); % nR x nT QR decomposition x_hat = inv(H)*y; % zero forcing equalization x_sliced = QAM16_real_slicer(x_hat,len)'; % slicing radius_squared = norm(R*(x_sliced-x_hat))^2; % Radious^2 transition = 1; % meaning of transition % 0 : radius*2, 1~len : stage number % len+1 : compare two vectors in terms of norm values % len+2 : finish flag = 1; % transition tracing 0 : stage index increases by +1 %1 : stage index decreases by -1 %2 : 1->len+2 or len+1->1 while (transition<len+2) if transition==0 % radius_squared*2 [flag,transition,radius_squared,x_list]= radius_control(radius_squared,transition); elseif transition <= len [flag,transition] = stage_processing(flag,transition); elseif transition == len+1 % [flag,transition] = compare_vector_norm(transition); end end ML = x_pre; for i=1:len/2 X_hat(i) = ML(i)+j*ML(i+len/2); end end function [flag,transition] = stage_processing(flag,transition) % Input parameters % flag : previous stage index % flag = 0 : stage index decreased -> x_now empty -> new x_now % flag = 1 : stage index decreased -> new x_now % flag = 2 : previous stage index =len+1 -> If R>R'? start from the first stage % transition : stage number % Output parameters % flag : stage number is calculated from flag % transition : next stage number, 0 : R*2, 1: next stage, len+2: finish global x_list x_metric x_now x_hat real_constellation R radius_squared x_sliced; global x_list; global x_metric; global x_now; global x_hat; global real_constellation; global R; global radius_squared; global x_sliced; stage_index = length(R(1,:))-(transition-1); if flag == 2 % previous stage=len+1 : recalculate radius R' radius_squared = norm(R*(x_sliced-x_hat))^2; end if flag ~= 0 % previous stage=len+1 or 0 -> upper and lower bound calculation, x_list(stage_index,:) [bound_lower bound_upper] = bound(transition); for i =1:4 % search for a candidate in x_now(stage_index), % 4=size(real_constellation), 16-QAM assumed if bound_lower <= real_constellation(i) && real_constellation(i) <= bound_upper list_len = list_length(x_list(stage_index,:)); x_list(stage_index,list_len+1) = real_constellation(i); end end end list_len = list_length(x_list(stage_index,:)); if list_len == 0 % no candidate in x_now if x_metric == 0 || transition ~= 1 % transition >=2 ? if no candidate ? decrease stage index flag = 0; transition = transition-1; elseif x_metric ~= 0 && transition == 1 % above two conditions are met? ML solution found transition = length(R(1,:))+2; % finish stage end else % candidate exist in x_now ? increase stage index flag = 1; transition = transition+1; x_now(stage_index) = x_list(stage_index,1); x_list(stage_index,:) = [x_list(stage_index,[2:4]) 0]; end function [bound_lower bound_upper]=bound(transition) % Input parameters % R : [Q R] = qr(H) % radius_squared : R^2 % transition : stage number % x_hat : inv(H)*y % x_now : slicing x_hat % Output parameters % bound_lower : bound lower % bound_upper : bound upper global R radius_squared x_now x_hat; len = length(x_hat); temp_sqrt = radius_squared; temp_k=0; for i=1:1:transition-1 temp_abs=0; for k=1:1:i index_1 = len-(i-1); index_2 = index_1+ (k-1); temp_k = R(index_1,index_2)*(x_now(index_2)-x_hat(index_2)); temp_abs=temp_abs+temp_k; end temp_sqrt = temp_sqrt - abs(temp_abs)^2; end temp_sqrt = sqrt(temp_sqrt); temp_no_sqrt = 0; index_1 = len-(transition-1); index_2 = index_1; for i=1:1:transition-1 index_2 = index_2+1; temp_i = R(index_1,index_2)*(x_now(index_2)-x_hat(index_2)); temp_no_sqrt = temp_no_sqrt - temp_i; end temp_lower = -temp_sqrt + temp_no_sqrt; temp_upper = temp_sqrt + temp_no_sqrt; index = len-(transition-1); bound_lower = temp_lower/R(index,index) + x_hat(index); bound_upper = temp_upper/R(index,index) + x_hat(index); bound_upper = fix(bound_upper*sqrt(10))/sqrt(10); bound_lower = ceil(bound_lower*sqrt(10))/sqrt(10); end function [len]=list_length(list) % Input parameter % list : vector type % Output parameter % len : index number len = 0; for i=1:4 if list(i)==0, break; else len = len+1; end end end function [flag,transition,radius_squared,x_list] =radius_control(radius_squared,transition) % Input parameters % radius_squared : current radius % transition : current stage number % Output parameters % radius_squared : doubled radius % transition : next stage number % flag : next stage number is calculated from flag global len; radius_squared = radius_squared*2; transition = transition+1; flag = 1; x_list(len,:)=zeros(1,4); end function [check]=vector_comparison(vector_1,vector_2) % check if the two vectors are the same % Input parameters % pre_x : vector 1 % now_x : vector 2 % Output parameters % check : 1-> same vectors, 0-> different vectors check = 0; len1 = length(vector_1); len2 = length(vector_2); if len1 ~= len2 error('vector size is different'); end for column_num = 1:len1 if vector_1(column_num,1) == vector_2(column_num,1) check = check + 1; end end if check == len1, check = 1; else check = 0; end end function [flag,transition]=compare_vector_norm(transition) % stage index increased(flag = 1) : recalculate x_list(index,:) % stage index decreased(flag = 0) : in the previous stage, no candidate x_now in x_list % Input parameters % flag : previous stage % transition : stage number % Output parameters % flag : next stage number is calculated from flag % transition : next stage number global x_list x_pre x_metric x_now x_hat R radius_squared x_sliced len; vector_identity = vector_comparison(x_pre,x_now); % check if the new candidate is among the ones we found before if vector_identity == 1 % if 1 ? ML solution found len_total = 0; for i=1:len % if the vector is unique ? len_total = 0