# Connexions

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Module by: Bryan Paul, Aditya Jain. E-mail the authors

Summary: This is a basic building block in most nontrivial communications schemes: mapping received symbols to bit words. For more thorough treatment on this concept, read up on "Bit-to-Symbol Mapping." NOTE: The MATLAB library was used in the initial version of the design but has since been replaced by an updated LabVIEW module. Please see the LabVIEW portion of the receiver for the most current version.


%  ------------------------------------------------------------------------
%  Description: Converts the received symbols from the OFDM symbol spectrum
%               into binary words. It does this by correlating the received
%               symbol with the entire symbol map through a minimization of
%               the absolute value of the difference with each. Note that
%               if one uses multiple constellation points in the same
%               quandrant of the complex plane, it may be necessary to add
%               additional logic here, or ensure the channel effect is
%               removed by equalization.
%
%  Inputs: symbols - Symbols extracted from the OFDM symbol
%          word_to_symbol_map - The complex symbols indexed by the binary
%                               word they represent.
%  Outputs: words - Closest matched binary words after comparison

function words = sym2w(symbols, word_to_symbol_map)
%%
num_subcarriers = size(symbols,2); % Implicit number of sub-carriers
bits_per_symbol = log2(size(word_to_symbol_map,2));
% Implicit number of bits per symbol to represent each word
words = zeros(num_subcarriers, bits_per_symbol); % Initialized array
for n=1:num_subcarriers
[v i] = min(abs(word_to_symbol_map-symbols(n)));
% Subtract each symbol with every symbol in the map, and find the
% minimum absolute value. This is a great estimate for finding the
word = dec2bin(i-1,bits_per_symbol); % Convert index to binary string
for m=1:bits_per_symbol
words(n,m) = str2num(word(m)); % Convert string to number
end
end
end


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Any individual member, a community, or a respected organization.

##### What are tags?

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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