MIMO OFDM LSE CHAN ESTIMATION

In this code we consider the least square error channel estimation for a MIMO OFDM system.

%% Help
% In this code we consider the least square error channel estimation for a
% MIMO OFDM system. The user have access to the design parameters of
% the MIMO OFDM system and the channel state information. The L-tap
% Rayleigh fading channel is considered between any pair of the transmit and
% receive antenna. The mean squared error of the LSE channel obtained
% by the simulation result is compared with theory.
%
% source paper : "Optimal Training Design for MIMO OFDM Systems in Mobile Wireless Channels"
%
% Author : Hamid Ramezani
% Author's contact : http://ens.ewi.tudelft.nl/~ramezani/
% Matlab Vession : 7.13.0.564 (R2011b)
%
%======================================================================
% Inputs
%======================================================================
% Input parameters are (if not set the defalt value will be set)
% ofdm.Nb = 1e2; % number of blocks
% ofdm.Nt = 2; % number of transmit antennas
% ofdm.Nr = 4; % number of receive antennas
% ofdm.K = 128; % number of subcarriers
% ofdm.G = 1/4; % Guard interval percentage
% ofdm.Mod = 4; % QPSK Modulation
% ofdm.PSpace = 1; % subcarrier space between two pilots
% channel parameters
% chan.SNR_dB = 15; % signal to noise ratio
% chan.L = 6; % number of taps in each transmit-receive antenna
% control parameters
% ofdm.ifDemodulateData = 1; % (1,0) if 1, the code demodulates the transmitted via LS data and calculates the BER
% ofdm.ifDisplayResults = 1; % (1,0) if 1, display the results in the command window
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% Outputs
%======================================================================
% The main outputs are listed below
% chan.MSE_Theory % Minimum squared error of LSE channel estimation in theory
% chan.MSE_Simulation % Minimum squared error of LSE channel estimation in simulations
% ofdm.BER % Bit Error Rate if ofdm.ifDemodulateData = 1