Presentation is loading. Please wait.

Presentation is loading. Please wait.

Introduction to Digital Communications

Similar presentations

Presentation on theme: "Introduction to Digital Communications"— Presentation transcript:

1 Introduction to Digital Communications
Based on prof. Moshe Nazarathy lectures on Digital Communications Overview of comm. channels and digital links Optimal Detection Matched Filters

2 A digital Communications Link: bitstream-> TX->Analog Medium with Noise->RX > bitstream
Bitstream: a finite or possibly infinite sequence of random bits out of the set {0,1}, representing the information to be carried All media in Nature are analog – A purely digital medium exists only in math. “Underneath every digital communications link there resides an analog medium” The TX: Digital->Analog The RX: Analog->Digital The objective of a communication link: Receiving a bitstream at the TX and faithfully reproducing it at the RX at maximum rate and with minimum power

3 Complete digital communication link
Redundant check-bits insertion Data compression A/D QUANTIZATION

4 Data Source Randomly Transmit M different messages ai every T sec.
The amount of information is measured using entropy : Maximum information is achieved when :

5 Data Encoder Source Encoder: Data Compression (Zip ..)
Channel Encoder: Redundant check-bits insertion (CRC, Turbo, etc)

6 Modulator Converts M digital messages to M analog signals :
Limitations for choosing : Energy Amplitude Bandwidth

7 P-MOD example: QPSK transmitter mapping pairs of bits to one of four signals

8 4-level PAM transmission

9 Communication Media - Fiber
In our course the channel can be described by: LTI transfer function of analog medium Additive Noise Mathematical model for this channel is :

10 Communication Media - Fiber
b(t) – Fiber Impulse response Optical mode propagation constants Disspersion n(t) – system noise Laser noise Modulator Amplifiers noise Photo-detector noise

11 Receiver Receives random analog signal R(t) and matches it to one of M possibilities Optimal decision is required. We choose Pr(Error) as our optimization parameter.

12 Scalar Detection Problem
We look at special case when M=2 and we transmit scalar amplitudes s1 and s2 with probability 1/2 :

13 Detection Princple The detector defines 2 areas A1 and A2 S1 S2 d
A1 area A2 area

14 Detection Princple Optimum performance is achieved for :
If we choose s1=-A and s2=A, then d=0 and Error probability of the detector is:

15 The gaussian Q-function
^ Gaussian integral function or Q-function =Prob. of “upper tail” of normalized gaussian r.v.

16 Time dependent Detection
Problem formulation: If Pr(s1)=Pr(S2) optimal detection rule is

17 Time dependent Detection
Detection rule can be written as : If we assume equal power symbols :

18 Error Probability Calculation
We assume 2 signals s1(t) and s(2) with correlation ρ: We define a new random processes X, n1 and n2 such as:

19 Error Probability Calculation
Z=n1-n2 is combination of Gaussian processes and therefore also Gaussian

20 Error Probability Calculation
Special cases: ρ=0 : Orthogonal signals ρ=-1 : Antipodal signals

21 Antipodal transmission operational point: For 10^-5 Error Probability, SNR must be 9.6 dB
Figure 1.41:

22 Matched Filters We already saw that our decision algorithm is :
It is more convenient to write it in form: is called matched filter for signal

23 Simple Detector Block Diagram
R(t) H1(t) H2(t) HM(t) Choose the biggest Ak Estimated Data Matched filter is chosen according to following parameters: Transmitter modulation format Channel transfer function b(t)

24 Matched Filter and SNR Lets assume general MF with following characteristics: In this case after MF the system SNR is: It can be noticed that when We achieve optimal performance with

Download ppt "Introduction to Digital Communications"

Similar presentations

Ads by Google