id	sid	tid	token	lemma	pos
kw52j676c5n	1	1	this	this	DET
kw52j676c5n	1	2	thesis	thesis	NOUN
kw52j676c5n	1	3	analyzes	analyze	VERB
kw52j676c5n	1	4	the	the	DET
kw52j676c5n	1	5	effect	effect	NOUN
kw52j676c5n	1	6	of	of	ADP
kw52j676c5n	1	7	training	training	NOUN
kw52j676c5n	1	8	on	on	ADP
kw52j676c5n	1	9	the	the	DET
kw52j676c5n	1	10	mutual	mutual	ADJ
kw52j676c5n	1	11	information	information	NOUN
kw52j676c5n	1	12	between	between	ADP
kw52j676c5n	1	13	the	the	DET
kw52j676c5n	1	14	input	input	NOUN
kw52j676c5n	1	15	and	and	CCONJ
kw52j676c5n	1	16	output	output	NOUN
kw52j676c5n	1	17	of	of	ADP
kw52j676c5n	1	18	a	a	DET
kw52j676c5n	1	19	system	system	NOUN
kw52j676c5n	1	20	with	with	ADP
kw52j676c5n	1	21	unknown	unknown	ADJ
kw52j676c5n	1	22	parameters	parameter	NOUN
kw52j676c5n	1	23	.	.	PUNCT
kw52j676c5n	2	1	the	the	DET
kw52j676c5n	2	2	work	work	NOUN
kw52j676c5n	2	3	consists	consist	VERB
kw52j676c5n	2	4	of	of	ADP
kw52j676c5n	2	5	two	two	NUM
kw52j676c5n	2	6	parts	part	NOUN
kw52j676c5n	2	7	including	include	VERB
kw52j676c5n	2	8	a	a	DET
kw52j676c5n	2	9	theory	theory	NOUN
kw52j676c5n	2	10	in	in	ADP
kw52j676c5n	2	11	the	the	DET
kw52j676c5n	2	12	computation	computation	NOUN
kw52j676c5n	2	13	of	of	ADP
kw52j676c5n	2	14	mutual	mutual	ADJ
kw52j676c5n	2	15	information	information	NOUN
kw52j676c5n	2	16	with	with	ADP
kw52j676c5n	2	17	training	training	NOUN
kw52j676c5n	2	18	,	,	PUNCT
kw52j676c5n	2	19	and	and	CCONJ
kw52j676c5n	2	20	its	its	PRON
kw52j676c5n	2	21	applications	application	NOUN
kw52j676c5n	2	22	in	in	ADP
kw52j676c5n	2	23	wireless	wireless	ADJ
kw52j676c5n	2	24	communication	communication	NOUN
kw52j676c5n	2	25	,	,	PUNCT
kw52j676c5n	2	26	signal	signal	NOUN
kw52j676c5n	2	27	processing	processing	NOUN
kw52j676c5n	2	28	,	,	PUNCT
kw52j676c5n	2	29	and	and	CCONJ
kw52j676c5n	2	30	machine	machine	NOUN
kw52j676c5n	2	31	learning	learning	NOUN
kw52j676c5n	2	32	which	which	PRON
kw52j676c5n	2	33	can	can	AUX
kw52j676c5n	2	34	be	be	AUX
kw52j676c5n	2	35	modeled	model	VERB
kw52j676c5n	2	36	by	by	ADP
kw52j676c5n	2	37	quantized	quantize	VERB
kw52j676c5n	2	38	large	large	ADJ
kw52j676c5n	2	39	-	-	PUNCT
kw52j676c5n	2	40	scale	scale	NOUN
kw52j676c5n	2	41	systems	system	NOUN
kw52j676c5n	2	42	and	and	CCONJ
kw52j676c5n	2	43	employ	employ	VERB
kw52j676c5n	2	44	training	training	NOUN
kw52j676c5n	2	45	as	as	ADP
kw52j676c5n	2	46	part	part	NOUN
kw52j676c5n	2	47	of	of	ADP
kw52j676c5n	2	48	their	their	PRON
kw52j676c5n	2	49	operation.in	operation.in	NUM
kw52j676c5n	2	50	the	the	DET
kw52j676c5n	2	51	first	first	ADJ
kw52j676c5n	2	52	part	part	NOUN
kw52j676c5n	2	53	,	,	PUNCT
kw52j676c5n	2	54	we	we	PRON
kw52j676c5n	2	55	develop	develop	VERB
kw52j676c5n	2	56	a	a	DET
kw52j676c5n	2	57	theory	theory	NOUN
kw52j676c5n	2	58	in	in	ADP
kw52j676c5n	2	59	computing	compute	VERB
kw52j676c5n	2	60	the	the	DET
kw52j676c5n	2	61	mutual	mutual	ADJ
kw52j676c5n	2	62	information	information	NOUN
kw52j676c5n	2	63	between	between	ADP
kw52j676c5n	2	64	the	the	DET
kw52j676c5n	2	65	input	input	NOUN
kw52j676c5n	2	66	and	and	CCONJ
kw52j676c5n	2	67	output	output	NOUN
kw52j676c5n	2	68	conditioned	condition	VERB
kw52j676c5n	2	69	on	on	ADP
kw52j676c5n	2	70	training	training	NOUN
kw52j676c5n	2	71	for	for	ADP
kw52j676c5n	2	72	large	large	ADJ
kw52j676c5n	2	73	-	-	PUNCT
kw52j676c5n	2	74	scale	scale	NOUN
kw52j676c5n	2	75	systems	system	NOUN
kw52j676c5n	2	76	that	that	PRON
kw52j676c5n	2	77	do	do	AUX
kw52j676c5n	2	78	not	not	PART
kw52j676c5n	2	79	necessarily	necessarily	ADV
kw52j676c5n	2	80	have	have	VERB
kw52j676c5n	2	81	gaussianity	gaussianity	NOUN
kw52j676c5n	2	82	or	or	CCONJ
kw52j676c5n	2	83	linearity	linearity	NOUN
kw52j676c5n	2	84	in	in	ADP
kw52j676c5n	2	85	the	the	DET
kw52j676c5n	2	86	model	model	NOUN
kw52j676c5n	2	87	,	,	PUNCT
kw52j676c5n	2	88	without	without	ADP
kw52j676c5n	2	89	considering	consider	VERB
kw52j676c5n	2	90	any	any	DET
kw52j676c5n	2	91	particular	particular	ADJ
kw52j676c5n	2	92	parameter	parameter	NOUN
kw52j676c5n	2	93	estimate	estimate	NOUN
kw52j676c5n	2	94	,	,	PUNCT
kw52j676c5n	2	95	and	and	CCONJ
kw52j676c5n	2	96	without	without	ADP
kw52j676c5n	2	97	resorting	resort	VERB
kw52j676c5n	2	98	to	to	ADP
kw52j676c5n	2	99	linearization	linearization	NOUN
kw52j676c5n	2	100	or	or	CCONJ
kw52j676c5n	2	101	any	any	DET
kw52j676c5n	2	102	worst	bad	ADJ
kw52j676c5n	2	103	-	-	PUNCT
kw52j676c5n	2	104	case	case	NOUN
kw52j676c5n	2	105	noise	noise	NOUN
kw52j676c5n	2	106	analysis	analysis	NOUN
kw52j676c5n	2	107	.	.	PUNCT
kw52j676c5n	3	1	such	such	ADJ
kw52j676c5n	3	2	mutual	mutual	ADJ
kw52j676c5n	3	3	information	information	NOUN
kw52j676c5n	3	4	can	can	AUX
kw52j676c5n	3	5	be	be	AUX
kw52j676c5n	3	6	computed	compute	VERB
kw52j676c5n	3	7	as	as	ADP
kw52j676c5n	3	8	the	the	DET
kw52j676c5n	3	9	difference	difference	NOUN
kw52j676c5n	3	10	between	between	ADP
kw52j676c5n	3	11	two	two	NUM
kw52j676c5n	3	12	derivatives	derivative	NOUN
kw52j676c5n	3	13	of	of	ADP
kw52j676c5n	3	14	a	a	DET
kw52j676c5n	3	15	single	single	ADJ
kw52j676c5n	3	16	function	function	NOUN
kw52j676c5n	3	17	.	.	PUNCT
kw52j676c5n	4	1	in	in	ADP
kw52j676c5n	4	2	the	the	DET
kw52j676c5n	4	3	second	second	ADJ
kw52j676c5n	4	4	part	part	NOUN
kw52j676c5n	4	5	,	,	PUNCT
kw52j676c5n	4	6	we	we	PRON
kw52j676c5n	4	7	show	show	VERB
kw52j676c5n	4	8	that	that	SCONJ
kw52j676c5n	4	9	a	a	DET
kw52j676c5n	4	10	quantized	quantize	VERB
kw52j676c5n	4	11	large	large	ADJ
kw52j676c5n	4	12	-	-	PUNCT
kw52j676c5n	4	13	scale	scale	NOUN
kw52j676c5n	4	14	system	system	NOUN
kw52j676c5n	4	15	with	with	ADP
kw52j676c5n	4	16	unknown	unknown	ADJ
kw52j676c5n	4	17	parameters	parameter	NOUN
kw52j676c5n	4	18	and	and	CCONJ
kw52j676c5n	4	19	training	training	NOUN
kw52j676c5n	4	20	signals	signal	NOUN
kw52j676c5n	4	21	can	can	AUX
kw52j676c5n	4	22	be	be	AUX
kw52j676c5n	4	23	analyzed	analyze	VERB
kw52j676c5n	4	24	by	by	ADP
kw52j676c5n	4	25	examining	examine	VERB
kw52j676c5n	4	26	an	an	DET
kw52j676c5n	4	27	equivalent	equivalent	ADJ
kw52j676c5n	4	28	system	system	NOUN
kw52j676c5n	4	29	with	with	ADP
kw52j676c5n	4	30	known	know	VERB
kw52j676c5n	4	31	parameters	parameter	NOUN
kw52j676c5n	4	32	by	by	ADP
kw52j676c5n	4	33	modifying	modify	VERB
kw52j676c5n	4	34	the	the	DET
kw52j676c5n	4	35	signal	signal	NOUN
kw52j676c5n	4	36	power	power	NOUN
kw52j676c5n	4	37	and	and	CCONJ
kw52j676c5n	4	38	noise	noise	NOUN
kw52j676c5n	4	39	variance	variance	NOUN
kw52j676c5n	4	40	in	in	ADP
kw52j676c5n	4	41	a	a	DET
kw52j676c5n	4	42	prescribed	prescribed	ADJ
kw52j676c5n	4	43	manner	manner	NOUN
kw52j676c5n	4	44	.	.	PUNCT
kw52j676c5n	5	1	applications	application	NOUN
kw52j676c5n	5	2	to	to	ADP
kw52j676c5n	5	3	training	training	NOUN
kw52j676c5n	5	4	in	in	ADP
kw52j676c5n	5	5	wireless	wireless	ADJ
kw52j676c5n	5	6	communications	communication	NOUN
kw52j676c5n	5	7	,	,	PUNCT
kw52j676c5n	5	8	signal	signal	NOUN
kw52j676c5n	5	9	processing	processing	NOUN
kw52j676c5n	5	10	,	,	PUNCT
kw52j676c5n	5	11	and	and	CCONJ
kw52j676c5n	5	12	machine	machine	NOUN
kw52j676c5n	5	13	learning	learning	NOUN
kw52j676c5n	5	14	are	be	AUX
kw52j676c5n	5	15	shown	show	VERB
kw52j676c5n	5	16	.	.	PUNCT
kw52j676c5n	6	1	in	in	ADP
kw52j676c5n	6	2	wireless	wireless	ADJ
kw52j676c5n	6	3	communications	communication	NOUN
kw52j676c5n	6	4	,	,	PUNCT
kw52j676c5n	6	5	we	we	PRON
kw52j676c5n	6	6	show	show	VERB
kw52j676c5n	6	7	that	that	SCONJ
kw52j676c5n	6	8	the	the	DET
kw52j676c5n	6	9	number	number	NOUN
kw52j676c5n	6	10	of	of	ADP
kw52j676c5n	6	11	training	training	NOUN
kw52j676c5n	6	12	signals	signal	NOUN
kw52j676c5n	6	13	can	can	AUX
kw52j676c5n	6	14	be	be	AUX
kw52j676c5n	6	15	significantly	significantly	ADV
kw52j676c5n	6	16	smaller	small	ADJ
kw52j676c5n	6	17	than	than	ADP
kw52j676c5n	6	18	the	the	DET
kw52j676c5n	6	19	number	number	NOUN
kw52j676c5n	6	20	of	of	ADP
kw52j676c5n	6	21	transmitting	transmitting	NOUN
kw52j676c5n	6	22	elements	element	NOUN
kw52j676c5n	6	23	.	.	PUNCT
kw52j676c5n	7	1	similar	similar	ADJ
kw52j676c5n	7	2	conclusions	conclusion	NOUN
kw52j676c5n	7	3	can	can	AUX
kw52j676c5n	7	4	be	be	AUX
kw52j676c5n	7	5	drawn	draw	VERB
kw52j676c5n	7	6	when	when	SCONJ
kw52j676c5n	7	7	considering	consider	VERB
kw52j676c5n	7	8	the	the	DET
kw52j676c5n	7	9	symbol	symbol	NOUN
kw52j676c5n	7	10	error	error	NOUN
kw52j676c5n	7	11	rate	rate	NOUN
kw52j676c5n	7	12	in	in	ADP
kw52j676c5n	7	13	signal	signal	NOUN
kw52j676c5n	7	14	processing	processing	NOUN
kw52j676c5n	7	15	applications	application	NOUN
kw52j676c5n	7	16	,	,	PUNCT
kw52j676c5n	7	17	as	as	ADV
kw52j676c5n	7	18	long	long	ADV
kw52j676c5n	7	19	as	as	SCONJ
kw52j676c5n	7	20	the	the	DET
kw52j676c5n	7	21	number	number	NOUN
kw52j676c5n	7	22	of	of	ADP
kw52j676c5n	7	23	receiving	receive	VERB
kw52j676c5n	7	24	elements	element	NOUN
kw52j676c5n	7	25	is	be	AUX
kw52j676c5n	7	26	large	large	ADJ
kw52j676c5n	7	27	enough	enough	ADV
kw52j676c5n	7	28	.	.	PUNCT
kw52j676c5n	8	1	in	in	ADP
kw52j676c5n	8	2	machine	machine	NOUN
kw52j676c5n	8	3	learning	learning	PROPN
kw52j676c5n	8	4	,	,	PUNCT
kw52j676c5n	8	5	where	where	SCONJ
kw52j676c5n	8	6	a	a	DET
kw52j676c5n	8	7	linear	linear	ADJ
kw52j676c5n	8	8	classifier	classifier	NOUN
kw52j676c5n	8	9	is	be	AUX
kw52j676c5n	8	10	considered	consider	VERB
kw52j676c5n	8	11	,	,	PUNCT
kw52j676c5n	8	12	we	we	PRON
kw52j676c5n	8	13	show	show	VERB
kw52j676c5n	8	14	that	that	SCONJ
kw52j676c5n	8	15	the	the	DET
kw52j676c5n	8	16	misclassification	misclassification	NOUN
kw52j676c5n	8	17	rate	rate	NOUN
kw52j676c5n	8	18	is	be	AUX
kw52j676c5n	8	19	not	not	PART
kw52j676c5n	8	20	sensitive	sensitive	ADJ
kw52j676c5n	8	21	to	to	ADP
kw52j676c5n	8	22	the	the	DET
kw52j676c5n	8	23	number	number	NOUN
kw52j676c5n	8	24	of	of	ADP
kw52j676c5n	8	25	classes	class	NOUN
kw52j676c5n	8	26	when	when	SCONJ
kw52j676c5n	8	27	the	the	DET
kw52j676c5n	8	28	number	number	NOUN
kw52j676c5n	8	29	of	of	ADP
kw52j676c5n	8	30	training	training	NOUN
kw52j676c5n	8	31	examples	example	NOUN
kw52j676c5n	8	32	is	be	AUX
kw52j676c5n	8	33	large	large	ADJ
kw52j676c5n	8	34	,	,	PUNCT
kw52j676c5n	8	35	and	and	CCONJ
kw52j676c5n	8	36	is	be	AUX
kw52j676c5n	8	37	approximately	approximately	ADV
kw52j676c5n	8	38	inversely	inversely	ADV
kw52j676c5n	8	39	proportional	proportional	ADJ
kw52j676c5n	8	40	to	to	ADP
kw52j676c5n	8	41	the	the	DET
kw52j676c5n	8	42	size	size	NOUN
kw52j676c5n	8	43	of	of	ADP
kw52j676c5n	8	44	the	the	DET
kw52j676c5n	8	45	training	training	NOUN
kw52j676c5n	8	46	set	set	NOUN
kw52j676c5n	8	47	.	.	PUNCT
kw52j676c5n	9	1	we	we	PRON
kw52j676c5n	9	2	show	show	VERB
kw52j676c5n	9	3	that	that	SCONJ
kw52j676c5n	9	4	a	a	DET
kw52j676c5n	9	5	linear	linear	ADJ
kw52j676c5n	9	6	analysis	analysis	NOUN
kw52j676c5n	9	7	of	of	ADP
kw52j676c5n	9	8	this	this	DET
kw52j676c5n	9	9	nonlinear	nonlinear	ADJ
kw52j676c5n	9	10	training	training	NOUN
kw52j676c5n	9	11	problem	problem	NOUN
kw52j676c5n	9	12	can	can	AUX
kw52j676c5n	9	13	be	be	AUX
kw52j676c5n	9	14	accurate	accurate	ADJ
kw52j676c5n	9	15	when	when	SCONJ
kw52j676c5n	9	16	the	the	DET
kw52j676c5n	9	17	additive	additive	ADJ
kw52j676c5n	9	18	thermal	thermal	ADJ
kw52j676c5n	9	19	noise	noise	NOUN
kw52j676c5n	9	20	power	power	NOUN
kw52j676c5n	9	21	is	be	AUX
kw52j676c5n	9	22	high	high	ADJ
kw52j676c5n	9	23	.	.	PUNCT