175 lines
5.3 KiB
Matlab
175 lines
5.3 KiB
Matlab
classdef Pulseformer
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%Pulseformer Summary of this class goes here
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% Detailed explanation goes here
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properties(Access=public)
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end
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properties(Access=public)
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fdac
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fsym
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pulse
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pulselength
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alpha
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matched
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end
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methods (Access=public)
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function obj = Pulseformer(options)
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%NAME Construct an instance of this class
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% Detailed explanation goes here
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arguments
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options.fdac double
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options.fsym double
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options.pulse pulseform = pulseform.rc
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options.pulselength double {mustBeInteger} = 32
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options.alpha double = 0.05
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options.matched = 0;
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end
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%
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fn = fieldnames(options);
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for n = 1:numel(fn)
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try
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obj.(fn{n}) = options.(fn{n});
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end
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end
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% do more stuff
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end
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function signalclass_out = process(obj,signalclass_in)
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% actual processing of the signal (steps 1. - 3.)
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signalclass_in.signal = obj.process_(signalclass_in.signal);
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% append to logbook
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lbdesc = 'Applied Pulseshaping';
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signalclass_in = signalclass_in.logbookentry(lbdesc);
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% write fs to signal
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signalclass_in.fs = obj.fdac;%.* (obj.fdac./obj.fsym);
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% write to output
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signalclass_out = signalclass_in;
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end
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end
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methods (Access=private)
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% Cant be seen from outside! So put all your functions here that can/
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% shall not be called from outside
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function data_out = process_(obj,data_in)
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%METHOD1 Summary of this method goes here
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% Detailed explanation goes here
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arguments(Input)
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obj
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data_in
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end
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arguments(Output)
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data_out
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end
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if ~rem(obj.fdac,obj.fsym)
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%ist ein Vielfaches
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sps = obj.fdac / obj.fsym;
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p = sps;
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q = 1;
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else
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%ist kein Vielfaches
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p = obj.fsym / gcd(obj.fdac, obj.fsym); %upsampling p->->->
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q = obj.fdac/ gcd(obj.fdac, obj.fsym); %downsampling <-q
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sps= q; %sps während dem pulse shaping
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end
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if obj.pulse == pulseform.rc
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filtertype = 'normal';
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elseif pulseform.rrc
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filtertype = 'sqrt';
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end
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%Bau das Filter (hier rc)
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racos_len = obj.pulselength*2;
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h = rcosdesign(obj.alpha,racos_len,sps,filtertype);
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% h = h./ max(h);
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if obj.matched
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h = conj(fliplr(h));
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end
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manual_cyclic_convolution = 0;
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upfirdn_convolution = 1;
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if manual_cyclic_convolution
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% Apply filter the long way (from move_it)
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data_in = data_in';
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blen = length(data_in)*sps;
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% oversample symbol sequence
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symbolov=zeros(size(data_in,1),blen);
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symbolov(:,1:sps:blen-sps+1)=data_in;
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H=fft(h,blen);
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% Convolution of Bit sequence with impulse response
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data_out=ifft( fft(symbolov.') .* repmat( H,size(data_in,1),1 ).' ).';
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data_out = circshift(data_out,[0 -(obj.pulselength*sps)]);
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if rem(obj.fdac,obj.fsym)
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data_out = data_out(1:q:end);
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end
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end
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if upfirdn_convolution
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%Apply Filter using Matlab build in fctn.
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data_out_ = upfirdn(data_in,h,p,q);
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%cut signal, which is longer due to fir filter
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st = round(p/q*racos_len/2); %we need to cut y_out
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en = round(st + (length(data_in)*p/q));
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data_out = data_out_(st:en);
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end
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if upfirdn_convolution && manual_cyclic_convolution
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figure()
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subplot(2,1,1)
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title("Convolution vs. Upfirdn and Cut")
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hold on
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% plot(data_out_(1:200),'DisplayName','Matlab upfirdn');
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plot(data_out(1:200),'DisplayName','By Hand cyclic convolution')
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subplot(2,1,2)
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hold on
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plot(data_out(1:2000),'DisplayName','OUT');
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plot(data_in(1:2000),'DisplayName','IN');
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end
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%scaling?! see pulsef module line 696
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% scale = max(max([abs(real(data_out)) abs(imag(data_out))])); %find max value from real and imag part
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% data_out = data_out./scale;
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data_out = data_out';
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%Check output integrity
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if abs(round(p/q * length(data_in)) - length(data_out)) > 4
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warning('Check signal length after pulse shaping');
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%disp('Check signal length after pulse shaping');
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end
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end
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end
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end
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