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페이스북 트위터 카카오톡 네이버밴드 링크복사

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공학 >컴퓨터ㆍ통신 >정보통신공학
강의학기

2016년 2학기

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14,735
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5/5.0 (2)

강의계획서: 강의계획서

통신이론의 기초와 최근의 디지털통신에 대해 이해하고 시스템 설계 및 응용할 수 있는 능력을 기른다. 특히 기저대역에서의 신호전송 및 Modem, channel coding의 기법의 기초를 다진다.

- 신호의 종류 및 랜덤 process

- 기저대역에서의 신호전송 및 복원기술

- Modulation/Demodulation 기술

- ASK,PSK,FSK

- Channel coding 기술

- Convolutional, block coding

수강안내 및 수강신청
※ 수강확인증 발급을 위해서는 수강신청이 필요합니다

Probability and Random variables(1)

차시별 강의

1.	Probability and Random variables(1)	1. Distribution of Random Variables 2. Properties of independent RVs 3. Mean and Variance 4. Scaling, constant addition, and sum of RVs
	Probability and Random variables(2)	5. Weighted sum of RVs 6. Widely used RVs 7. Integration of Gaussian PDF and the Q-function 8. Gaussian RV
2.	Random Signals(1)	1. Random process 2. Mean of the random process 3. Autocorrelation and wide sense stationary(WSS) process 4. Property of autocorrelation for WSS process 5. Power spectral density (PSD) for WSS process
	Random Signals(2)	1. Power spectral density (PSD) for WSS process 2. Gaussian random process 3. White process 4. Additive white Gaussian noise (AWGN)
3.	Maximum Likelihood Detection for Binary Transmission	1. Received signal 2. Decision variable 3. Maximum likelihood detection
	Maximum Likelihood Detection for Binary Transmission (part 2)	4. Probability of ML detection error 5. Non equally-probable binary case 6. Non-Gaussian noise case
4.	SIGNAL VECTOR SPACE AND MAXIMUM LIKELIHOOD	1. Orthogonal signal set 2. Signal generation using orthonormal set 3. Signal vector space 4. Waveform to signal vector space
	SIGNAL VECTOR SPACE AND MAXIMUM LIKELIHOOD	1. Equivalence between waveform and vector spaces 2. Noise signal in the vector space 3. Received signal in the vector space 4. ML detection in vector space
5.	CORRELATOR-BASED MAXIMUM LIKELIHOOD DETECTION(part 1)	1. Equivalence between waveform and vector spaces 2. AWGN in signal vector space
	CORRELATOR-BASED MAXIMUM LIKELIHOOD DETECTION(part 2)	1. Equivalence between waveform and vector spaces 2. AWGN in signal vector space 3. ML detection in waveform space 4. Correlator-based ML detection
6.	CORRELATOR-BASED MAXIMUM LIKELIHOOD DETECTION (part 3)	1. Correlator-based ML detection 2. ML detection of binary signals
	PULSE SHAPING AND MATCHED FILTER (part 1)	1. Matched filter
7.	PULSE SHAPING AND MATCHED FILTER (part 2)	2. Pulse shaping process 3. Received signal and ISI 4. Pulse shape design 5. Raised cosine pulse
	PULSE SHAPING AND MATCHED FILTER (part 3)	4. Pulse shape design 5. Raised cosine pulse 6. Square root raised cosine pulse 7. Eye diagram
8.	BER Simulation at the Waveform Level (part 1)	1. Overview on BPSK 2. Why waveform-level simulation? 3. Waveform-level BER simulation steps
	BER Simulation at the Waveform Level (part 2)	4. Sampled waveforms 5. Sample interval and noise variance 6. Steps 6~7 in BER simulation code
9.	QPSK and Offset-QPSK (part 1)	1. QPSK signals 2. QPSK transmitter 3. QPSK receiver
	QPSK and Offset-QPSK (part 2)	4. Pulse shaped QP SK
10.	M-ary modulations: MPSK, QAM and MFSK (part 1-1)	1. MPSK signals 2. Symbol error of MPSK
	M-ary modulations: MPSK, QAM and MFSK (part 1-2)	2. Symbol error of MPSK 3. BER of MPSK 4. Bandwidth of MPSK
11.	M-ary modulations: MPSK, QAM and MFSK (part 1-3)	5. MPSK demodulator 6. QAM signal 7. QAM demodulator
	M-ary modulations: MPSK, QAM and MFSK (part 2-1)	1. PDF of z1 and z2 2. BER of QAM 3. Bandwidth of QAM
12.	M-ary modulations: MPSK, QAM and MFSK (part 2-2)	3. Bandwidth of QAM 4. MFSK signal 5. MFSK demodulator 6. Error performance and BW of MFSK 7. Comparisons among MPSK, QAM and MFSK
	Fading, Diversity and Combining (part 1)	1. QPSK signals 2. Fading 3. Rayleigh fading 4. Instantaneous symbol energy
13.	Fading, Diversity and Combining (part 2-1)	5. Average BER under Rayleigh fading 6. Diversity
	Fading, Diversity and Combining (part 2-2)	6. Diversity 7. Combining 8. Selection diversity combining 9. Equal gain combining 10. Maximum ratio combining 11. BER comparison among the combining schemes