iris_py.m File Reference

Functions
Parameters to feed	python (pun very intended!) serial_ids
obj	py_obj_array (ipy,:)
The trigger is set only on the fist node end function	sdrsync (obj, is_bs) obj.py_obj_array
	sync_delays (pyargs('is_bs', is_bs))
end function	sdr_setcorr (obj) obj.py_obj_array
	set_corr ()
end function	sdr_txgainctrl (obj) obj.py_obj_array
	tx_gain_ctrl ()
end function	sdr_txbeacon (obj, prefix_len) % Assume Beacon only from the first Iris board in the BS array obj.py_obj_array
	burn_beacon (pyargs('prefix_len', prefix_len))
end function	set_config (obj, chained_tx_rx, is_bs) if chained_tx_rx sched
	if (obj.n_chain > 1) sched2
end end end end function	sdrrxsetup (obj) for ipy
end end function	sdr_activate_rx (obj) for ipy
data	real ()
	data_raw (ipy,(jf-1) *n_samp+1:jf *n_samp)
end function	sdr_close (obj) for ipy
	delete (obj.py_obj_array{ipy})
	lts_corr_dr (:, nc)
id	peaks: (lts_corr_sum< 0.2 lts_corr_sum)
	elseif (obj.n_chain > 1) &&length(lts_corr_frm >
obj.n_samp, obj.n_chain, obj.n_frame	zeros ()
	data_split (:,:, nf)
	fprintf ('Returning frame number %d with max mean correlation=%f \n', m_idx, max_corr)

Variables
Author(s) pyhton object array This array decribes Iris board or a collection of Iris boards that belong to the same entity E	g
Author(s) pyhton object array This array decribes Iris board or a collection of Iris boards that belong to the same entity E a BS	py_obj_array = {}
	n_chain = sdr_params.n_chain
number of Iris boards in a chain	sample_rate = sdr_params.sample_rate
	tx_freq = sdr_params.txfreq
q	rx_freq = sdr_params.rxfreq
	bw
	tx_gain = sdr_params.txgain
	rx_gain = sdr_params.rxgain
	n_samp = sdr_params.n_samp
	tdd_sched = sdr_params.tdd_sched
	n_zpad_samp = sdr_params.n_zpad_samp
end methods function	obj
obj	serial_ids = sdr_params.id
obj	n_frame = sdr_params.n_frame
for	ipy
	max_frames
end end end	NB
else	sched = convertStringsToChars(obj.tdd_sched(1))
	im = imag(data).'
end end Read n_frame x n_samp data	function [data, len]
Change this to max frame ! for	jf
end end	data = obj.get_best_frame(data_raw.', n_samp)
	len = length(data)
TD LTS	lts_t = ifft(lts_f, 64)
Add CP and a second TD LTS	lts_cp = lts_t(33:end)
Correlation through filtering	lts_corr_dr = zeros(size(data_frame))
	unos = ones(size(lts_t))
	a = 1
for	nc
	v1 = filter(unos,a,abs(data_frame(:,nc)).^2)
normalized correlation end Sum accross antennas	lts_corr_sum = sum(lts_corr_dr,2)
Assume peak in the first samples	lts_corr_frm = reshape(lts_corr_sum, [], obj.n_frame)
end save my_data mat lts_corr_dr lts_corr_frm Avg corr value per frame	frm_avg_corr
for	nf
	end_idx = nf*obj.n_samp
end	data_frame = reshape(data_frame,n_samp, [], obj.n_frame )

Function Documentation

burn_beacon()

burn_beacon

(

pyargs( 'prefix_len', prefix_len)

)

data_raw()

data_raw	(	ipy	,
		(jf-1) *n_samp+1:jf *	n_samp
	)

data_split()

data_split	(	:	,
		:	,
		nf
	)

delete()

delete

(

obj.py_obj_array{ipy}

)

elseif()

elseif	(	obj.	n_chain,
		1
	)		&&

fprintf()

fprintf	(	'Returning frame number %d with max mean	correlation = %f \n',
		m_idx	,
		max_corr
	)

if()

if	(	obj.	n_chain,
		1
	)

lts_corr_dr()

lts_corr_dr	(	:	,
		nc
	)

peaks:()

id peaks: ( )

virtual

py_obj_array()

obj py_obj_array	(	ipy	,
		:
	)

python()

Parameters to feed python

(

pun very intended!

)

real()

data real ( )

virtual

Here is the caller graph for this function:

sdr_activate_rx()

end end function sdr_activate_rx

(

obj

)

sdr_close()

end function sdr_close

(

obj

)

sdr_setcorr()

end function sdr_setcorr

(

obj

)

sdr_txbeacon()

end function sdr_txbeacon	(	obj	,
		prefix_len
	)

sdr_txgainctrl()

end function sdr_txgainctrl

(

obj

)

sdrrxsetup()

end end end end function sdrrxsetup

(

obj

)

sdrsync()

The trigger is set only on the fist node end function sdrsync	(	obj	,
		is_bs
	)

set_config()

end function set_config	(	obj	,
		chained_tx_rx	,
		is_bs
	)

set_corr()

set_corr

(

)

sync_delays()

sync_delays

(

pyargs( 'is_bs', is_bs)

)

tx_gain_ctrl()

tx_gain_ctrl

(

)

zeros()

obj.n_samp, obj.n_chain,obj.n_frame zeros ( )

virtual

Variable Documentation

a

a = 1

bw

bw

data

Return the frame with the highest value data = obj.get_best_frame(data_raw.', n_samp)

data_frame

end data_frame = reshape(data_frame,n_samp, [], obj.n_frame )

end_idx

end_idx = nf*obj.n_samp

frm_avg_corr

end save my_data mat lts_corr_dr lts_corr_frm Avg corr value per frame frm_avg_corr

Initial value:

= sum(lts_corr_frm,1)./obj.n_chain
            % Take index of maximum corr. value
            [max_corr, m_idx] = max(frm_avg_corr)

function

end end function[data]

Initial value:

= sdrrx(obj, n_samp)
            data_raw = zeros(obj.n_chain, obj.n_frame*n_samp)

g

Author (s) pyhton object array This array decribes Iris board or a collection of Iris boards that belong to the same entity E g

im

im = imag(data).'

ipy

for ipy

Initial value:

=1:obj.n_chain
                    id_str = convertStringsToChars( obj.serial_ids(ipy))
                    py_obj = py.iris_py.Iris_py( pyargs('serial_id',id_str,...
                        'tx_freq', obj.tx_freq, 'rx_freq', obj.rx_freq,...
                        'tx_gain',obj.tx_gain,'rx_gain',obj.rx_gain,...
                        'sample_rate',obj.sample_rate, 'n_samp',...
                        obj.n_samp,'n_zpad_samp',obj.n_zpad_samp) )

jf

Change this to max frame ! for jf

Initial value:

=1:obj.n_frame
                %trigger beacon TX
                trig = 1

len

len = length(data)

lts_corr_dr

Correlation through filtering lts_corr_dr = zeros(size(data_frame))

lts_corr_frm

Assume peak in the first samples lts_corr_frm = reshape(lts_corr_sum, [], obj.n_frame)

lts_corr_sum

normalized correlation end Sum accross antennas lts_corr_sum = sum(lts_corr_dr,2)

lts_cp

Add CP and a second TD LTS lts_cp = lts_t(33:end)

lts_t

lts_t = ifft(lts_f, 64)

max_frames

max_frames

n_chain

if obj n_chain = sdr_params.n_chain

n_frame

obj n_frame = sdr_params.n_frame

n_samp

obj n_samp = sdr_params.n_samp

n_zpad_samp

This is an array obj n_zpad_samp = sdr_params.n_zpad_samp

NB

end end end NB

nc

for nc

Initial value:

=1:size(data_frame,2)
                v0 = filter(flipud(conj(lts_t)),a,data_frame(:,nc))

nf

for nf

Initial value:

= 1:obj.n_frame
                strt_idx = (nf-1)*obj.n_samp +1

obj

end methods function obj

Initial value:

= iris_py(sdr_params)
            if nargin > 0
                obj.sdr_params = sdr_params

py_obj_array

obj py_obj_array = {}

rx_freq

obj rx_freq = sdr_params.rxfreq

rx_gain

obj rx_gain = sdr_params.rxgain

sample_rate

obj sample_rate = sdr_params.sample_rate

sched

else sched = convertStringsToChars(obj.tdd_sched(1))

serial_ids

obj serial_ids = sdr_params.id

tdd_sched

obj tdd_sched = sdr_params.tdd_sched

tx_freq

obj tx_freq = sdr_params.txfreq

tx_gain

obj tx_gain = sdr_params.txgain

unos

unos = ones(size(lts_t))

v1

v1 = filter(unos,a,abs(data_frame(:,nc)).^2)