Analogne modulacije / Analog modulations
|
|
- Juliana Ellis
- 5 years ago
- Views:
Transcription
1 Analogne modulacije / Analog modulations Zadatak: Na slici 1 je prikazana blok ²ema prijemnika AM-1B0 signala sa sinhronom demodulacijom. Moduli²u i signal m(t) ima spektar u opsegu ( f m f m ) i snagu P m. U estanost nosioca je f c, a amplituda k. Osim AM-1B0 modulisanog signala u(t) (sa donjim bo nim opsegom) na ulazu prijemnika postoji i aditivni beli Gausov ²um n(t) ija je spektralna gustina snage p n = N 0 /2. Odrediti odnos signal/²um na izlazu prijemnika. Figure 1: Sinhroni prijemnik / Synchronous receiver Problem: Figure 1 depicts the receiver of the SSB modulated signal with synchronous demodulation. The power of the modulating signal m(t) is P m and its spectrum is contained in the band ( f m f m ). The carrier has frequency f c, and amplitude k. Together with the SSB modulated signal u(t) (with lower sideband), at the input of the receiver there is also an additive white Gaussian noise n(t) with spectral power density p n = N 0 /2. Determine the signal to noise ratio at the output of the receiver.
2 Re²enje: Modulisani signal je: u(t) = k m(t) cos(2πf c t) + k ˆm(t) sin(2πf c t). Korisni signal na izlazu mnoºa a je: u 1 (t) = u(t) cos(2πf c t) = k m(t) cos(2πf c t) cos(2πf c t) + k ˆm(t) sin(2πf c t) cos(2πf c t) = 1 2 k m(t) k m(t) cos(2π2f ct) k ˆm(t) sin(2π2f ct) tako da je korisni signal na izlazu NF ltra: u d (t) = 1 2 k m(t) a njegova snaga: P d = u 2 d (t) = 1 4 k2 m 2 (t) = 1 4 k2 P m. Uskopojasni ²um na ulazu mnoºa a je: n 1 (t) = n c (t) cos(2πf 0 t) + n s (t) sin(2πf 0 t) a snaga: fc P n1 = 2 p N df = 2p N f m = N 0 f m f c f m i jednaka je snazi svake od niskofrekvencijskih komponenata ²uma n c (t) i n s (t). U estanost f 0 predstavlja centralnu u estanost propusnika opsega i u ovom zadatku iznosi f 0 = f c f m /2. um na izlazu mnoºa a je: n 2 (t) = n 1 (t) cos(2πf c t) = n c (t) cos(2πf 0 t) cos(2πf c t) + n s (t) sin(2πf 0 t) cos(2πf c t) = 1 2 n c(t) cos(2π(f 0 f c )t) n c(t) cos(2π(f 0 + f c )t) n s(t) sin(2π(f 0 f c )t) n s(t) sin(2π(f 0 + f c )t) = 1 2 n c(t) cos(πf m t) n c(t) cos(2π(2f c f m /2)t) n s(t) sin(πf m t) n s(t) sin(2π(2f c f m /2)t). um na izlazu NF ltra je takodje uskopojasni ²um sa centralnom u estano² u f m /2: tako da je njegova snaga: n 3 (t) = 1 2 n c(t) cos(πf m t) 1 2 n s(t) sin(πf m t) P n3 = 1 4 fm p N df = 1 f m 4 p N2f m = N 0f m 4. Traºeni odnos signal/²um je: SNR = P d P n3 = k2 P m N 0 f m.
3 Signali i sistemi / Signals and systems Zadatak: Signal s(t) ima spektar S(f) ograni en na interval u estanosti ( f m f m ). Odabiranjem signala s(t) dobijaju se dva signala odbiraka: s 1 (t) = s(t) s 2 (t) = s(t) n= n= δ(t kt s ) i δ(t kt s τ 0 ), pri emu je T s = 1 i f m 0 < τ 0 < T s /4. Smatrati da je signal s(t) realan. Da li je na osnovu spektara signala odbiraka s 1 (t) i s 2 (t) mogu e rekonstruisati spektar originalnog signala? Odgovor potkrepiti odgovaraju im dokazom. Problem: The signal s(t) is real, with a spectrum S(f) contained in the band ( f m f m ). By sampling s(t), two new signals are obtained as follows: s 1 (t) = s(t) s 2 (t) = s(t) n= n= δ(t kt s ) and δ(t kt s τ 0 ), where T s = 1 and f m 0 < τ 0 < T s /4. Is it possible to reconstruct perfectly the spectrum of the original signal S(f) from the spectrums of the signals s 1 (t) and s 2 (t)? Prove your answer.
4 Re²enje: Originalni signal je mogu e rekonstruisati. Spektar signala s 2 (t) je: S 2 (f) = = s(t) s(t) 1 T s n= 2π jk e Ts τ 0 = 1 T s = 1 T s Pri traºenju spektra S 2 (f) iskori² ena je jednakost: δ(t kt s τ 0 )e j2πft dt 2π jk e Ts (t τ0) e j2πft dt 2π jk e Ts τ 0 S(f k ). T s δ(t kt s τ 0 ) = 1 T s s(t)e j2π(f k Ts )t dt 2π jk e Ts (t τ 0) do koje se moºe do i razvojem povorke delta impulsa u Furijeov red. Spektar signala s 1 (t) se dobija na osnovu spektra signala s 2 (t) jednostavnom zamenom τ 0 = 0, odnosno S 1 (f) = 1 T s S(f k T s ). Zbog hermitske simetrije spektra signala s(t) i periodi nosti spektara signala odbiraka dovoljno je posmatrati opseg u estanosti (0 f m ). Na tom opsegu S 1 (f) = f m (S(f) + S(f f m )) ( ) S 2 (f) = f m S(f) + e j2π τ 0 Ts S(f f m ) odakle sledi: S(f) = 1 1 ( S2 (f) e j2πfmτ 0 S 1 (f) ). f m 1 exp( j2πf m τ 0 )
5 Digitalne telekomunikacije / Digital communications Zadatak: Na slici je prikazan sistem za prenos podataka u osnovnom opsegu u estanosti. Signal s(t) na ulazu u sistem ima oblik: s(t) = a k δ(t kt ) pri emu simboli a k uzimaju vrednosti iz skupa U, U} sa jednakim verovatno ama. Predajni i prijemni ltri su denisani funkcijama prenosa: H T (f) = T, f < 1 T 0, ina e, H R (f) = 1, f < 1 2T 0, ina e Odluka o n-tom simbolu na prijemu se vr²i na osnovu odbirka signala s R (t) u trenutku t = (n + ɛ)t, gde je ɛ gre²ka u sinhronizaciji, ɛ < 1/2. Odrediti maksimalnu dozvoljenu gre²ku u sinhronizaciji ɛ max tako da je pouzdan prenos i dalje mogu. Problem: The gure depicts a system for baseband transmission of information. the input is of the form: s(t) = a k δ(t kt ) The signal s(t) at where a k takes on the values U, U} with equal probabilities. Transfer functions of the transmitting and receiving lters are given by: T, f < 1 T H T (f) = 0, elsewhere, H R (f) = 1, f < 1 2T 0, elsewhere Decision about the n'th symbol at the receiver is made based on the sample of the signal s R (t) at t = (n + ɛ)t, where ɛ is the synchronization error, ɛ < 1/2. Determine the maximal synchronization error ɛ max so that reliable transmission is still possible.
6 Re²enje: Ekvivalentna funkcija prenosa sistema je: a odgovaraju i impulsni odziv: pa signal s R (t) ima oblik: H(f) = H T (f)h R (f) = s R (t) = h(t) = sin( π t) T π t, T U trenutku odlu ivanja o n-tom simbolu imamo: s R ((n + ɛ)t ) = = a n sin(πɛ) πɛ T, f < 1 2T 0, ina e a k h(t kt ). a k h((ɛ + n k)t ) = + m 0 sin(π(ɛ + m)) a n m. π(ɛ + m) a k sin(π(ɛ + n k)) π(ɛ + n k) Drugi sabirak u gornjem izrazu predstavlja intersimbolsku interferenciju i nepoºeljan je pri odlu ivanju o simbolu a n. Da bi prenos informacija bio pouzdan, treba da vaºi: m 0 a n m sin(π(ɛ + m)) π(ɛ + m) < U sin(πɛ) πɛ (prag odlu ivanja je na nuli jer su simboli jednako verovatni). U najgorem slu aju imamo: I max = U sin(π(ɛ + m)) π(ɛ + m) = U sin(πɛ) 1 π m + ɛ m 0 m 0 = U sin(πɛ) ( 1 π m + ɛ + 1 ) = U sin(πɛ) 2m m ɛ π m 2 ɛ = 2 m>0 1 ²to sledi iz injenice da m 0 =. Kako maksimalna ISI divergira za sve ɛ > 0 (uslov (1) m nije zadovoljen), ne sme se dozvoliti gre²ka u sinhronizaciji, tj. ɛ max = 0. m>0 (1)
7 Statisti ka teorija telekomunikacija / Statistical theory of communications Zadatak: Neka su h 0 (t), h 1 (t), h 2 (t),... vremenske funkcije denisane sa h n (t) = n h(t), pri emu je: 1, 0 t < T h(t) = 0, ina e Neka je..., a 1, a 0, a 1,...} niz nezavisnih slu ajnih promenljivih, raspodeljenih prema Poasonovoj raspodeli sa parametrom λ. Odrediti srednju vrednost i autokorelaciju slu ajnog procesa X(t), denisanog na slede i na in: X(t) = h ak (t kt ). Da li je ovaj proces stacionaran u ²irem smislu? Problem: Let h 0 (t), h 1 (t), h 2 (t),... be functions dened by h n (t) = n h(t), where: 1, 0 t < T h(t) = 0, elsewhere. Let..., a 1, a 0, a 1,...} be a sequence of independent random variables with Poisson distribution with parameter λ. Find the mean and the autocorrelation function of the random process X(t), dened by: X(t) = Is this process wide-sense stationary? h ak (t kt ).
8 Re²enje: Slu ajne promenljive a k imaju Poasonovu raspodelu: P[a k = n] = e λ λn, n 0, 1, 2,...}, n! ija srednja vrednost i varijansa se mogu lako izra unati: E[a k ] = np[a k = n] = e λ = e λ λ n=1 n λn λ n 1 (n 1)! = e λ λ = λ E[a 2 k] = n 2 P[a k = n] = e λ = e λ λ = λ 2 + λ n! = e λ m=0 (m + 1) λm (m)! = e λ λ m=0 D[a k ] = E[a 2 k] E[a k ] 2 = λ. n=1 n λn n! λ m m! = e λ λe λ n 2 λn n! = e λ λ m=0 n λn 1 (n 1)! n=1 m λm (m)! + e λ λ m=0 λ m (m)! Posmatrajmo sada slu ajni proces X(t). U svakom intervalu kt t < (k +1)T proces X(t) je jednak nekoj od funkcija h n (t), izabranoj prema Poasonovoj raspodeli, tj. sa verovatno om P[a k = n]. Rezonovanje je isto za svaki interval, pa nadalje moºemo posmatrati prvi: 0 t < T. Srednja vrednost procesa X(t) je: E[X(t)] = P[a 0 = n]h n (t) = P[a 0 = n]nh(t) = E[a 0 ] h(t) = E[a 0 ] = λ. Srednja vrednost je dakle konstantna (ne zavisi od t). Prilikom izra unavanja autokorelacije procesa X, posmatra emo dva slu aja. 1. Kada t i t + τ pripadaju istom "signalizacionom" intervalu, tj. 0 t, t + τ < T : R X (t, t + τ) = E[X(t)X(t + τ)] = = P[a 0 = n]h n (t)h n (t + τ) P[a 0 = n]n 2 h(t)h(t + τ) = E[a 2 0] = λ + λ Kada t i t + τ pripadaju razli itim intervalima, tj. 0 t < T, T t + τ < 2T (moºe se uzeti da t + τ pripada bilo kom drugom intervalu, zaklju ak e biti isti): R X (t, t + τ) = E[X(t)X(t + τ)] = = m=0 m=0 P[a 0 = n, a 1 = m]h n (t)h m (t + τ T ) P[a 0 = n]p[a 1 = m]nmh(t)h(t + τ T ) = E[a 0 ]E[a 1 ] = λ 2.
9 Dakle, R X (t, t + τ) = λ 2 + λ λ 2, kada t i t + τ pripadaju istom sign. intervalu, kada t i t + τ pripadaju razli itim sign. intervalima. Funkcija R X (t, t + τ) je za ksno τ periodi na po t sa periodom T, kao ²to se moglo i o ekivati. Za 0 τ < T jedna njena perioda je: R X (t, t + τ) = Za τ > T, R X (t, t + τ) = λ 2 i ne zavisi od t. λ 2 + λ, t [0, T τ) λ 2, t [T τ, T ). Kao ²to se vidi iz gornjeg, R X (t, t + τ) zavisi od t pa proces X nije stacionaran u ²irem smislu.
10 Teorija informacija / Information theory Zadatak: Kaskadno je povezano n istih nezavisnih binarnih kanala sa uslovnim verovatno ama P [0 0] = 1 u, P [1 0] = u, P [0 1] = v, P [1 1] = 1 v, gde je u < 1/2 i v < 1/2. Ako se u kaskadnu vezu kanala simboli 0 ili 1 ²alju sa verovatno ama 1/2, odrediti verovatno u gre²ke optimalnog odlu ivanja o pojedina nom simbolu poslatom kroz vezu. Problem: A channel is formed by concatenating n identical independent binary channels with transition probabilities P [0 0] = 1 u, P [1 0] = u, P [0 1] = v, P [1 1] = 1 v, where u < 1/2 and v < 1/2. If symbols 0 or 1 are fed to the input of this channel with probabilities 1/2, nd the probability of error achieved by the optimum receiver when a single symbol is transmitted.
11 Re²enje: Ako je X = Y 0 simbol na ulazu u vezu kanala, a Y k simbol na izlazu iz k-tog kanala u vezi, vaºi P [Y k = 0] = P [Y k 1 = 0]P [0 0] + P [Y k 1 = 1]P [0 1], P [Y k = 1] = P [Y k 1 = 0]P [1 0] + P [Y k 1 = 1]P [1 1], za k 1,..., n}. Odavde matrica uslovnih verovatno a pojedina nog kanala u vezi [ ] [ ] P [0 0] P [1 0] 1 u u Π = = P [0 1] P [1 1] v 1 v i vektor verovatno a simbola na izlazu iz k-tog kanala u vezi p k = [ P [Y k = 0] P [Y k = 1] ] zadovoljavaju jedna inu p k = p k 1 Π. Uzastopnom primenom prethodnog izraza, dobija se p k = p 0 Π k, odakle sledi da je Π n matrica uslovnih verovatno a cele veze. Neka su matrice [ ] Π k ak b = k. Kako je Π k = Π k 1 Π, elementi prethodnih matrica zadovoljavaju diferencne jedna ine c k d k a k = (1 u)a k 1 + vb k 1, b k = ua k 1 + (1 v)b k 1, c k = (1 u)c k 1 + vd k 1, d k = uc k 1 + (1 v)d k 1, a sabiranjem prve dve i druge dve od njih, dobija se a k + b k = a k 1 + b k 1 = 1, c k + d k = c k 1 + d k 1 = 1, gde poslednje jednakosti induktivno slede iz a 0 = 1, b 0 = 0, c 0 = 0 i d 0 = 1. Zamenom b k = 1 a k u prvu jedna inu, sledi da je a k = (1 u v)a k 1 + v = λa k 1 + v, gde je λ = 1 u v. Uzastopnom primenom prethodne jedna ine dobija se a 1 = λa 0 + v = λ + v, a 2 = λa 1 + v = λ(λ + v) + v = λ 2 + (λ + 1)v, a 3 = λa 2 + v = λ(λ 2 + (λ + 1)v) + v = λ 3 + (λ 2 + λ + 1)v,. a n = λ n + (λ n 1 + λ n )v = λ n + λn 1 λ 1 v,
12 ²to nakon sreživanja postaje a n = v u + v + u u + v (1 u v)n, odakle je i Na sli an na in se dobija i b n = c n = d n = u u + v u u + v (1 u v)n. v u + v v (1 u v)n u + v u u + v + v u + v (1 u v)n. Matrica uslovnih verovatno a cele veze, tj. od X do Y = Y n, jeste Π n = [ v + u u+v u+v v v u+v u+v (1 u v)n u (1 u v)n u u u+v + u+v v u+v u+v (1 u v)n (1 u v)n ]. Kako su verovatno e simbola na predaji jednake, optimalni prijemnik odlu uje po maksimalnoj verodostojnosti, ˆx(y) = arg max P [Y = y X = x], x pa, s obzirom da je u + v < 1, tj. a n > c n i d n > b n, vaºi ˆx(0) = 0, ˆx(1) = 1, odnosno ˆx(y) = y. Verovatno a gre²ke odlu ivanja jeste P E = P [ˆx(Y ) X] = = P [Y X] = = P [X = 0]P [Y = 1 X = 0] + P [X = 1]P [Y = 0 X = 1] = = 1 2 (1 (1 u v)n ) i ako je u + v > 0, P E 1/2 kada n, jer svaki novi kanal u vezi smanjuje pouzdanost prenosa.
Signal s(t) ima spektar S(f) ograničen na opseg učestanosti (0 f m ). Odabiranjem signala s(t) dobijaju se 4 signala odbiraka: δ(t kt s τ 2 ),
Signali i sistemi Signal st ima spektar Sf ograničen na opseg učestanosti 0 f m. Odabiranjem signala st dobijaju se signala odbiraka: s t = st s t = st s t = st s t = st δt k, δt k τ 0, δt k τ i δt k τ,
More information5 Analog carrier modulation with noise
5 Analog carrier modulation with noise 5. Noisy receiver model Assume that the modulated signal x(t) is passed through an additive White Gaussian noise channel. A noisy receiver model is illustrated in
More informationEE401: Advanced Communication Theory
EE401: Advanced Communication Theory Professor A. Manikas Chair of Communications and Array Processing Imperial College London Introductory Concepts Prof. A. Manikas (Imperial College) EE.401: Introductory
More informationA First Course in Digital Communications
A First Course in Digital Communications Ha H. Nguyen and E. Shwedyk February 9 A First Course in Digital Communications 1/46 Introduction There are benefits to be gained when M-ary (M = 4 signaling methods
More informationZANIMLJIVI ALGEBARSKI ZADACI SA BROJEM 2013 (Interesting algebraic problems with number 2013)
MAT-KOL (Banja Luka) ISSN 0354-6969 (p), ISSN 1986-5228 (o) Vol. XIX (3)(2013), 35-44 ZANIMLJIVI ALGEBARSKI ZADACI SA BROJEM 2013 (Interesting algebraic problems with number 2013) Nenad O. Vesi 1 Du²an
More informationDigital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10
Digital Band-pass Modulation PROF. MICHAEL TSAI 211/11/1 Band-pass Signal Representation a t g t General form: 2πf c t + φ t g t = a t cos 2πf c t + φ t Envelope Phase Envelope is always non-negative,
More informationSummary II: Modulation and Demodulation
Summary II: Modulation and Demodulation Instructor : Jun Chen Department of Electrical and Computer Engineering, McMaster University Room: ITB A1, ext. 0163 Email: junchen@mail.ece.mcmaster.ca Website:
More informationENSC327 Communications Systems 2: Fourier Representations. Jie Liang School of Engineering Science Simon Fraser University
ENSC327 Communications Systems 2: Fourier Representations Jie Liang School of Engineering Science Simon Fraser University 1 Outline Chap 2.1 2.5: Signal Classifications Fourier Transform Dirac Delta Function
More informationKLASIFIKACIJA NAIVNI BAJES. NIKOLA MILIKIĆ URL:
KLASIFIKACIJA NAIVNI BAJES NIKOLA MILIKIĆ EMAIL: nikola.milikic@fon.bg.ac.rs URL: http://nikola.milikic.info ŠTA JE KLASIFIKACIJA? Zadatak određivanja klase kojoj neka instanca pripada instanca je opisana
More informationEE303: Communication Systems
EE303: Communication Systems Professor A. Manikas Chair of Communications and Array Processing Imperial College London Introductory Concepts Prof. A. Manikas (Imperial College) EE303: Introductory Concepts
More informationTEORIJA SKUPOVA Zadaci
TEORIJA SKUPOVA Zadai LOGIKA 1 I. godina 1. Zapišite simbolima: ( x nije element skupa S (b) d je član skupa S () F je podskup slupa S (d) Skup S sadrži skup R 2. Neka je S { x;2x 6} = = i neka je b =
More informationNORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATIONS
page 1 of 5 (+ appendix) NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATIONS Contact during examination: Name: Magne H. Johnsen Tel.: 73 59 26 78/930 25 534
More informationRed veze za benzen. Slika 1.
Red veze za benzen Benzen C 6 H 6 je aromatično ciklično jedinjenje. Njegove dve rezonantne forme (ili Kekuléove structure), prema teoriji valentne veze (VB) prikazuju se uobičajeno kao na slici 1 a),
More informationExample: Bipolar NRZ (non-return-to-zero) signaling
Baseand Data Transmission Data are sent without using a carrier signal Example: Bipolar NRZ (non-return-to-zero signaling is represented y is represented y T A -A T : it duration is represented y BT. Passand
More informationModulation & Coding for the Gaussian Channel
Modulation & Coding for the Gaussian Channel Trivandrum School on Communication, Coding & Networking January 27 30, 2017 Lakshmi Prasad Natarajan Dept. of Electrical Engineering Indian Institute of Technology
More informationECE6604 PERSONAL & MOBILE COMMUNICATIONS. Week 3. Flat Fading Channels Envelope Distribution Autocorrelation of a Random Process
1 ECE6604 PERSONAL & MOBILE COMMUNICATIONS Week 3 Flat Fading Channels Envelope Distribution Autocorrelation of a Random Process 2 Multipath-Fading Mechanism local scatterers mobile subscriber base station
More informationThis examination consists of 10 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS
THE UNIVERSITY OF BRITISH COLUMBIA Department of Electrical and Computer Engineering EECE 564 Detection and Estimation of Signals in Noise Final Examination 08 December 2009 This examination consists of
More informationSignal Design for Band-Limited Channels
Wireless Information Transmission System Lab. Signal Design for Band-Limited Channels Institute of Communications Engineering National Sun Yat-sen University Introduction We consider the problem of signal
More informationThis examination consists of 11 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS
THE UNIVERSITY OF BRITISH COLUMBIA Department of Electrical and Computer Engineering EECE 564 Detection and Estimation of Signals in Noise Final Examination 6 December 2006 This examination consists of
More informationEE4061 Communication Systems
EE4061 Communication Systems Week 11 Intersymbol Interference Nyquist Pulse Shaping 0 c 2015, Georgia Institute of Technology (lect10 1) Intersymbol Interference (ISI) Tx filter channel Rx filter a δ(t-nt)
More informationAnalog Electronics 2 ICS905
Analog Electronics 2 ICS905 G. Rodriguez-Guisantes Dépt. COMELEC http://perso.telecom-paristech.fr/ rodrigez/ens/cycle_master/ November 2016 2/ 67 Schedule Radio channel characteristics ; Analysis and
More informationSquare Root Raised Cosine Filter
Wireless Information Transmission System Lab. Square Root Raised Cosine Filter Institute of Communications Engineering National Sun Yat-sen University Introduction We consider the problem of signal design
More informationProjektovanje paralelnih algoritama II
Projektovanje paralelnih algoritama II Primeri paralelnih algoritama, I deo Paralelni algoritmi za množenje matrica 1 Algoritmi za množenje matrica Ovde su data tri paralelna algoritma: Direktan algoritam
More informationDigital Communications
Digital Communications Chapter 5 Carrier and Symbol Synchronization Po-Ning Chen, Professor Institute of Communications Engineering National Chiao-Tung University, Taiwan Digital Communications Ver 218.7.26
More informationMulti User Detection I
January 12, 2005 Outline Overview Multiple Access Communication Motivation: What is MU Detection? Overview of DS/CDMA systems Concept and Codes used in CDMA CDMA Channels Models Synchronous and Asynchronous
More informationOsnove telekomunikacija Osnove obrade signala potrebne za analizu modulacijskih tehnika prof. dr. Nermin Suljanović
Osnove telekomunikacija Osnove obrade signala potrebne za analizu modulacijskih tehnika prof. dr. Nermin Suljanović Osnovni pojmovi Kontinualna modulacija je sistematična promjena signala nosioca u skladu
More informationFunkcijske jednadºbe
MEMO pripreme 2015. Marin Petkovi, 9. 6. 2015. Funkcijske jednadºbe Uvod i osnovne ideje U ovom predavanju obradit emo neke poznate funkcijske jednadºbe i osnovne ideje rje²avanja takvih jednadºbi. Uobi
More informationZANIMLJIV NAČIN IZRAČUNAVANJA NEKIH GRANIČNIH VRIJEDNOSTI FUNKCIJA. Šefket Arslanagić, Sarajevo, BiH
MAT-KOL (Banja Luka) XXIII ()(7), -7 http://wwwimviblorg/dmbl/dmblhtm DOI: 75/МК7A ISSN 5-6969 (o) ISSN 986-588 (o) ZANIMLJIV NAČIN IZRAČUNAVANJA NEKIH GRANIČNIH VRIJEDNOSTI FUNKCIJA Šefket Arslanagić,
More information7 The Waveform Channel
7 The Waveform Channel The waveform transmitted by the digital demodulator will be corrupted by the channel before it reaches the digital demodulator in the receiver. One important part of the channel
More informationParameter Estimation
1 / 44 Parameter Estimation Saravanan Vijayakumaran sarva@ee.iitb.ac.in Department of Electrical Engineering Indian Institute of Technology Bombay October 25, 2012 Motivation System Model used to Derive
More informationthat efficiently utilizes the total available channel bandwidth W.
Signal Design for Band-Limited Channels Wireless Information Transmission System Lab. Institute of Communications Engineering g National Sun Yat-sen University Introduction We consider the problem of signal
More informationLOPE3202: Communication Systems 10/18/2017 2
By Lecturer Ahmed Wael Academic Year 2017-2018 LOPE3202: Communication Systems 10/18/2017 We need tools to build any communication system. Mathematics is our premium tool to do work with signals and systems.
More informationP a g e 3 6 of R e p o r t P B 4 / 0 9
P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J
More informationPRIPADNOST RJEŠENJA KVADRATNE JEDNAČINE DANOM INTERVALU
MAT KOL Banja Luka) ISSN 0354 6969 p) ISSN 1986 58 o) Vol. XXI )015) 105 115 http://www.imvibl.org/dmbl/dmbl.htm PRIPADNOST RJEŠENJA KVADRATNE JEDNAČINE DANOM INTERVALU Bernadin Ibrahimpašić 1 Senka Ibrahimpašić
More informationGEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING Final Examination - Fall 2015 EE 4601: Communication Systems
GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING Final Examination - Fall 2015 EE 4601: Communication Systems Aids Allowed: 2 8 1/2 X11 crib sheets, calculator DATE: Tuesday
More informationDirect-Sequence Spread-Spectrum
Chapter 3 Direct-Sequence Spread-Spectrum In this chapter we consider direct-sequence spread-spectrum systems. Unlike frequency-hopping, a direct-sequence signal occupies the entire bandwidth continuously.
More informationII - Baseband pulse transmission
II - Baseband pulse transmission 1 Introduction We discuss how to transmit digital data symbols, which have to be converted into material form before they are sent or stored. In the sequel, we associate
More informationIntroduction to Probability and Stochastic Processes I
Introduction to Probability and Stochastic Processes I Lecture 3 Henrik Vie Christensen vie@control.auc.dk Department of Control Engineering Institute of Electronic Systems Aalborg University Denmark Slides
More informationPrinciples of Communications
Principles of Communications Chapter V: Representation and Transmission of Baseband Digital Signal Yongchao Wang Email: ychwang@mail.xidian.edu.cn Xidian University State Key Lab. on ISN November 18, 2012
More informationCHAPTER 14. Based on the info about the scattering function we know that the multipath spread is T m =1ms, and the Doppler spread is B d =0.2 Hz.
CHAPTER 4 Problem 4. : Based on the info about the scattering function we know that the multipath spread is T m =ms, and the Doppler spread is B d =. Hz. (a) (i) T m = 3 sec (ii) B d =. Hz (iii) ( t) c
More informationIskazna logika 1. Matematička logika u računarstvu. oktobar 2012
Matematička logika u računarstvu Department of Mathematics and Informatics, Faculty of Science,, Serbia oktobar 2012 Iskazi, istinitost, veznici Intuitivno, iskaz je rečenica koja je ima tačno jednu jednu
More informationEE4512 Analog and Digital Communications Chapter 4. Chapter 4 Receiver Design
Chapter 4 Receiver Design Chapter 4 Receiver Design Probability of Bit Error Pages 124-149 149 Probability of Bit Error The low pass filtered and sampled PAM signal results in an expression for the probability
More informationMathcad sa algoritmima
P R I M J E R I P R I M J E R I Mathcad sa algoritmima NAREDBE - elementarne obrade - sekvence Primjer 1 Napraviti algoritam za sabiranje dva broja. NAREDBE - elementarne obrade - sekvence Primjer 1 POČETAK
More informationTELEKOMUNIKACIONA MERENJA 1
UDšBENIK ELEKTROTEHNIƒKOG FAKULTETA U BEOGRADU Milan Bjelica TELEKOMUNIKACIONA MERENJA 1 zbirka re²enih zadataka Beograd, 2013. dr Milan Bjelica, Elektrotehni ki fakultet Univerziteta u Beogradu email:
More informationLecture 7: Wireless Channels and Diversity Advanced Digital Communications (EQ2410) 1
Wireless : Wireless Advanced Digital Communications (EQ2410) 1 Thursday, Feb. 11, 2016 10:00-12:00, B24 1 Textbook: U. Madhow, Fundamentals of Digital Communications, 2008 1 / 15 Wireless Lecture 1-6 Equalization
More informationCarrier Transmission. The transmitted signal is y(t) = k a kh(t kt ). What is the bandwidth? More generally, what is its Fourier transform?
The transmitted signal is y(t) = k a kh(t kt ). What is the bandwidth? More generally, what is its Fourier transform? The baseband signal is y(t) = k a kh(t kt ). The power spectral density of the transmission
More informationDigital Communications
Digital Communications Chapter 9 Digital Communications Through Band-Limited Channels Po-Ning Chen, Professor Institute of Communications Engineering National Chiao-Tung University, Taiwan Digital Communications:
More informationEs e j4φ +4N n. 16 KE s /N 0. σ 2ˆφ4 1 γ s. p(φ e )= exp 1 ( 2πσ φ b cos N 2 φ e 0
Problem 6.15 : he received signal-plus-noise vector at the output of the matched filter may be represented as (see (5-2-63) for example) : r n = E s e j(θn φ) + N n where θ n =0,π/2,π,3π/2 for QPSK, and
More informationSignals and Systems: Part 2
Signals and Systems: Part 2 The Fourier transform in 2πf Some important Fourier transforms Some important Fourier transform theorems Convolution and Modulation Ideal filters Fourier transform definitions
More informationCommunication Theory Summary of Important Definitions and Results
Signal and system theory Convolution of signals x(t) h(t) = y(t): Fourier Transform: Communication Theory Summary of Important Definitions and Results X(ω) = X(ω) = y(t) = X(ω) = j x(t) e jωt dt, 0 Properties
More information2016 Spring: The Final Exam of Digital Communications
2016 Spring: The Final Exam of Digital Communications The total number of points is 131. 1. Image of Transmitter Transmitter L 1 θ v 1 As shown in the figure above, a car is receiving a signal from a remote
More informationECE 541 Stochastic Signals and Systems Problem Set 11 Solution
ECE 54 Stochastic Signals and Systems Problem Set Solution Problem Solutions : Yates and Goodman,..4..7.3.3.4.3.8.3 and.8.0 Problem..4 Solution Since E[Y (t] R Y (0, we use Theorem.(a to evaluate R Y (τ
More informationPrinciples of Communications Lecture 8: Baseband Communication Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University
Principles of Communications Lecture 8: Baseband Communication Systems Chih-Wei Liu 劉志尉 National Chiao Tung University cwliu@twins.ee.nctu.edu.tw Outlines Introduction Line codes Effects of filtering Pulse
More informationEE6604 Personal & Mobile Communications. Week 15. OFDM on AWGN and ISI Channels
EE6604 Personal & Mobile Communications Week 15 OFDM on AWGN and ISI Channels 1 { x k } x 0 x 1 x x x N- 2 N- 1 IDFT X X X X 0 1 N- 2 N- 1 { X n } insert guard { g X n } g X I n { } D/A ~ si ( t) X g X
More informationMATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS
ch03.qxd 1/9/03 09:14 AM Page 35 CHAPTER 3 MATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS 3.1 INTRODUCTION The study of digital wireless transmission is in large measure the study of (a) the conversion
More informationSample Problems for the 9th Quiz
Sample Problems for the 9th Quiz. Draw the line coded signal waveform of the below line code for 0000. (a Unipolar nonreturn-to-zero (NRZ signaling (b Polar nonreturn-to-zero (NRZ signaling (c Unipolar
More informationFourier Analysis and Power Spectral Density
Chapter 4 Fourier Analysis and Power Spectral Density 4. Fourier Series and ransforms Recall Fourier series for periodic functions for x(t + ) = x(t), where x(t) = 2 a + a = 2 a n = 2 b n = 2 n= a n cos
More informationCommunications and Signal Processing Spring 2017 MSE Exam
Communications and Signal Processing Spring 2017 MSE Exam Please obtain your Test ID from the following table. You must write your Test ID and name on each of the pages of this exam. A page with missing
More information2A1H Time-Frequency Analysis II
2AH Time-Frequency Analysis II Bugs/queries to david.murray@eng.ox.ac.uk HT 209 For any corrections see the course page DW Murray at www.robots.ox.ac.uk/ dwm/courses/2tf. (a) A signal g(t) with period
More informationChapter 10. Timing Recovery. March 12, 2008
Chapter 10 Timing Recovery March 12, 2008 b[n] coder bit/ symbol transmit filter, pt(t) Modulator Channel, c(t) noise interference form other users LNA/ AGC Demodulator receive/matched filter, p R(t) sampler
More informationSRI VIDYA COLLEGE OF ENGINEERING AND TECHNOLOGY UNIT 3 RANDOM PROCESS TWO MARK QUESTIONS
UNIT 3 RANDOM PROCESS TWO MARK QUESTIONS 1. Define random process? The sample space composed of functions of time is called a random process. 2. Define Stationary process? If a random process is divided
More informationReview of Doppler Spread The response to exp[2πift] is ĥ(f, t) exp[2πift]. ĥ(f, t) = β j exp[ 2πifτ j (t)] = exp[2πid j t 2πifτ o j ]
Review of Doppler Spread The response to exp[2πift] is ĥ(f, t) exp[2πift]. ĥ(f, t) = β exp[ 2πifτ (t)] = exp[2πid t 2πifτ o ] Define D = max D min D ; The fading at f is ĥ(f, t) = 1 T coh = 2D exp[2πi(d
More informationP a g e 5 1 of R e p o r t P B 4 / 0 9
P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e
More informationEE5713 : Advanced Digital Communications
EE5713 : Advanced Digital Communications Week 12, 13: Inter Symbol Interference (ISI) Nyquist Criteria for ISI Pulse Shaping and Raised-Cosine Filter Eye Pattern Equalization (On Board) 20-May-15 Muhammad
More informationLecture 8 ELE 301: Signals and Systems
Lecture 8 ELE 30: Signals and Systems Prof. Paul Cuff Princeton University Fall 20-2 Cuff (Lecture 7) ELE 30: Signals and Systems Fall 20-2 / 37 Properties of the Fourier Transform Properties of the Fourier
More informationModule 4. Signal Representation and Baseband Processing. Version 2 ECE IIT, Kharagpur
Module Signal Representation and Baseband Processing Version ECE II, Kharagpur Lesson 8 Response of Linear System to Random Processes Version ECE II, Kharagpur After reading this lesson, you will learn
More information5 th INTERNATIONAL CONFERENCE Contemporary achievements in civil engineering 21. April Subotica, SERBIA
5 th INTERNATIONAL CONFERENCE Contemporary achievements in civil engineering 21. April 2017. Subotica, SERBIA COMPUTER SIMULATION OF THE ORDER FREQUENCIES AMPLITUDES EXCITATION ON RESPONSE DYNAMIC 1D MODELS
More informationDigital Baseband Systems. Reference: Digital Communications John G. Proakis
Digital Baseband Systems Reference: Digital Communications John G. Proais Baseband Pulse Transmission Baseband digital signals - signals whose spectrum extend down to or near zero frequency. Model of the
More informationLecture Notes 7 Stationary Random Processes. Strict-Sense and Wide-Sense Stationarity. Autocorrelation Function of a Stationary Process
Lecture Notes 7 Stationary Random Processes Strict-Sense and Wide-Sense Stationarity Autocorrelation Function of a Stationary Process Power Spectral Density Continuity and Integration of Random Processes
More information2.1 Basic Concepts Basic operations on signals Classication of signals
Haberle³me Sistemlerine Giri³ (ELE 361) 9 Eylül 2017 TOBB Ekonomi ve Teknoloji Üniversitesi, Güz 2017-18 Dr. A. Melda Yüksel Turgut & Tolga Girici Lecture Notes Chapter 2 Signals and Linear Systems 2.1
More informationEE6604 Personal & Mobile Communications. Week 12a. Power Spectrum of Digitally Modulated Signals
EE6604 Personal & Mobile Communications Week 12a Power Spectrum of Digitally Modulated Signals 1 POWER SPECTRUM OF BANDPASS SIGNALS A bandpass modulated signal can be written in the form s(t) = R { s(t)e
More informationT h e C S E T I P r o j e c t
T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T
More informationUCSD ECE250 Handout #27 Prof. Young-Han Kim Friday, June 8, Practice Final Examination (Winter 2017)
UCSD ECE250 Handout #27 Prof. Young-Han Kim Friday, June 8, 208 Practice Final Examination (Winter 207) There are 6 problems, each problem with multiple parts. Your answer should be as clear and readable
More informationA L A BA M A L A W R E V IE W
A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N
More information= 4. e t/a dt (2) = 4ae t/a. = 4a a = 1 4. (4) + a 2 e +j2πft 2
ECE 341: Probability and Random Processes for Engineers, Spring 2012 Homework 13 - Last homework Name: Assigned: 04.18.2012 Due: 04.25.2012 Problem 1. Let X(t) be the input to a linear time-invariant filter.
More informationS f s t j ft dt. S f s t j ft dt S f. s t = S f j ft df = ( ) ( ) exp( 2π
Reconfigurable stepped-frequency GPR prototype for civil-engineering and archaeological prospection, developed at the National Research Council of Italy. Examples of application and case studies. Raffaele
More informationUCSD ECE 153 Handout #46 Prof. Young-Han Kim Thursday, June 5, Solutions to Homework Set #8 (Prepared by TA Fatemeh Arbabjolfaei)
UCSD ECE 53 Handout #46 Prof. Young-Han Kim Thursday, June 5, 04 Solutions to Homework Set #8 (Prepared by TA Fatemeh Arbabjolfaei). Discrete-time Wiener process. Let Z n, n 0 be a discrete time white
More informationBROJEVNE KONGRUENCIJE
UNIVERZITET U NOVOM SADU PRIRODNO-MATEMATIČKI FAKULTET DEPARTMAN ZA MATEMATIKU I INFORMATIKU Vojko Nestorović BROJEVNE KONGRUENCIJE - MASTER RAD - Mentor, dr Siniša Crvenković Novi Sad, 2011. Sadržaj Predgovor...............................
More informationECE353: Probability and Random Processes. Lecture 18 - Stochastic Processes
ECE353: Probability and Random Processes Lecture 18 - Stochastic Processes Xiao Fu School of Electrical Engineering and Computer Science Oregon State University E-mail: xiao.fu@oregonstate.edu From RV
More informationEE456 Digital Communications
EE456 Digital Communications Professor Ha Nguyen September 5 EE456 Digital Communications Block Diagram of Binary Communication Systems m ( t { b k } b k = s( t b = s ( t k m ˆ ( t { bˆ } k r( t Bits in
More informationSECTION FOR DIGITAL SIGNAL PROCESSING DEPARTMENT OF MATHEMATICAL MODELLING TECHNICAL UNIVERSITY OF DENMARK Course 04362 Digital Signal Processing: Solutions to Problems in Proakis and Manolakis, Digital
More informationDigital Modulation 2
Digital Modulation 2 Lecture Notes Ingmar Land and Bernard H. Fleury Department of Electronic Systems Aalborg University Version: November 15, 2006 i Contents 1 Continuous-Phase Modulation 1 1.1 General
More informationLECTURE 16 AND 17. Digital signaling on frequency selective fading channels. Notes Prepared by: Abhishek Sood
ECE559:WIRELESS COMMUNICATION TECHNOLOGIES LECTURE 16 AND 17 Digital signaling on frequency selective fading channels 1 OUTLINE Notes Prepared by: Abhishek Sood In section 2 we discuss the receiver design
More informationELEN E4810: Digital Signal Processing Topic 11: Continuous Signals. 1. Sampling and Reconstruction 2. Quantization
ELEN E4810: Digital Signal Processing Topic 11: Continuous Signals 1. Sampling and Reconstruction 2. Quantization 1 1. Sampling & Reconstruction DSP must interact with an analog world: A to D D to A x(t)
More informationDetecting Parametric Signals in Noise Having Exactly Known Pdf/Pmf
Detecting Parametric Signals in Noise Having Exactly Known Pdf/Pmf Reading: Ch. 5 in Kay-II. (Part of) Ch. III.B in Poor. EE 527, Detection and Estimation Theory, # 5c Detecting Parametric Signals in Noise
More informationDigital Communications: A Discrete-Time Approach M. Rice. Errata. Page xiii, first paragraph, bare witness should be bear witness
Digital Communications: A Discrete-Time Approach M. Rice Errata Foreword Page xiii, first paragraph, bare witness should be bear witness Page xxi, last paragraph, You know who you. should be You know who
More informationFajl koji je korišćen može se naći na
Machine learning Tumačenje matrice konfuzije i podataka Fajl koji je korišćen može se naći na http://www.technologyforge.net/datasets/. Fajl se odnosi na pečurke (Edible mushrooms). Svaka instanca je definisana
More informationa) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics.
Digital Modulation and Coding Tutorial-1 1. Consider the signal set shown below in Fig.1 a) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics. b) What is the minimum Euclidean
More informationUCSD ECE153 Handout #40 Prof. Young-Han Kim Thursday, May 29, Homework Set #8 Due: Thursday, June 5, 2011
UCSD ECE53 Handout #40 Prof. Young-Han Kim Thursday, May 9, 04 Homework Set #8 Due: Thursday, June 5, 0. Discrete-time Wiener process. Let Z n, n 0 be a discrete time white Gaussian noise (WGN) process,
More information7.7 The Schottky Formula for Shot Noise
110CHAPTER 7. THE WIENER-KHINCHIN THEOREM AND APPLICATIONS 7.7 The Schottky Formula for Shot Noise On p. 51, we found that if one averages τ seconds of steady electron flow of constant current then the
More informationData Detection for Controlled ISI. h(nt) = 1 for n=0,1 and zero otherwise.
Data Detection for Controlled ISI *Symbol by symbol suboptimum detection For the duobinary signal pulse h(nt) = 1 for n=0,1 and zero otherwise. The samples at the output of the receiving filter(demodulator)
More informations o (t) = S(f)H(f; t)e j2πft df,
Sample Problems for Midterm. The sample problems for the fourth and fifth quizzes as well as Example on Slide 8-37 and Example on Slides 8-39 4) will also be a key part of the second midterm.. For a causal)
More informationE4702 HW#4-5 solutions by Anmo Kim
E70 HW#-5 solutions by Anmo Kim (ak63@columbia.edu). (P3.7) Midtread type uniform quantizer (figure 3.0(a) in Haykin) Gaussian-distributed random variable with zero mean and unit variance is applied to
More informationCommunication Systems Lecture 21, 22. Dong In Kim School of Information & Comm. Eng. Sungkyunkwan University
Communication Systems Lecture 1, Dong In Kim School of Information & Comm. Eng. Sungkyunkwan University 1 Outline Linear Systems with WSS Inputs Noise White noise, Gaussian noise, White Gaussian noise
More informationE&CE 358, Winter 2016: Solution #2. Prof. X. Shen
E&CE 358, Winter 16: Solution # Prof. X. Shen Email: xshen@bbcr.uwaterloo.ca Prof. X. Shen E&CE 358, Winter 16 ( 1:3-:5 PM: Solution # Problem 1 Problem 1 The signal g(t = e t, t T is corrupted by additive
More information2.1 Introduction 2.22 The Fourier Transform 2.3 Properties of The Fourier Transform 2.4 The Inverse Relationship between Time and Frequency 2.
Chapter2 Fourier Theory and Communication Signals Wireless Information Transmission System Lab. Institute of Communications Engineering g National Sun Yat-sen University Contents 2.1 Introduction 2.22
More informationSolution to Homework 1
Solution to Homework 1 1. Exercise 2.4 in Tse and Viswanath. 1. a) With the given information we can comopute the Doppler shift of the first and second path f 1 fv c cos θ 1, f 2 fv c cos θ 2 as well as
More informationEE 224 Signals and Systems I Review 1/10
EE 224 Signals and Systems I Review 1/10 Class Contents Signals and Systems Continuous-Time and Discrete-Time Time-Domain and Frequency Domain (all these dimensions are tightly coupled) SIGNALS SYSTEMS
More informationCarrier frequency estimation. ELEC-E5410 Signal processing for communications
Carrier frequency estimation ELEC-E54 Signal processing for communications Contents. Basic system assumptions. Data-aided DA: Maximum-lielihood ML estimation of carrier frequency 3. Data-aided: Practical
More informationUvod u analizu (M3-02) 05., 07. i 12. XI dr Nenad Teofanov. principle) ili Dirihleov princip (engl. Dirichlet box principle).
Uvod u analizu (M-0) 0., 07. i. XI 0. dr Nenad Teofanov. Kardinalni broj skupa R U ovom predavanju se razmatra veličina skupa realnih brojeva. Jasno, taj skup ima beskonačno mnogo elemenata. Pokazaće se,
More information