《通信原理》课程教学资源（PPT课件讲稿，英文版）Chapter 7 Performance of Communication Systems Corrupted by Noise

Chapter 7 Performance of Communication Systems Corrupted by Noise

1 Chapter 7 Performance of Communication Systems Corrupted by Noise

Introduction (chapter objectives) Bit error rate for binary systems(unipolar, polar, bipolar, ooK, BPSK, FSK, and Msk) Output signal-to-noise ratio for analog syster mS(AM, SSB, PM, and Fm) Information modulator Demod channel Source lator Oraeptanh Transmitting Re eceIving d noise end e How much bandwidth occupied in the air How many AM stations can be? Bandwidth ofhz L: What is the listening-performance? L> SNR e Is it very noisy to the listener? Money and time s. Equipment cost and complexity?

2 Introduction (chapter objectives) • Bit error rate for binary systems (unipolar, polar , bipolar, OOK, BPSK, FSK, and MSK) • Output signal-to-noise ratio for analog systems(AM, SSB, PM, and FM) Transmitting end Information source modulator channel Demodu￾lator Sink Or(acceptant) Receiving end noise • How much bandwidth occupied in the air? How many AM stations can be? • What is the listening-performance? Is it very noisy to the listener? • Equipment cost and complexity? Bandwidth of Hz SNR Money and time

Introduction Eample 2: A digital communication case: e How much bandwidth occupied in the air? Data-rate of bsp or How many AM stations can be? Bandwidth ofhe What is the listening-performance? Error rate(probability of Is it very noisy to the listener? error)or SNR Equipment cost and complexity? Money and time

3 Introduction Eample 2: A digital communication case: • How much bandwidth occupied in the air? How many AM stations can be? • What is the listening-performance? Is it very noisy to the listener? • Equipment cost and complexity? Data-rate of bsp or Bandwidth of Hz Error rate (probability of error) or SNR Money and time

Introduction (chapter objectives) · Channel:nota“ free lunch” Always having a limited bandwidth With noise and even interference Often with distortion We mainly concern the most common awgn channel: Additive noise Noise is white and gaussian No distortion, maybe some amplifying ofA, maybe some delay of to Bandwidth is enough for a certain transmission

4 Introduction (chapter objectives) • Channel: not a “free lunch” – Always having a limited bandwidth – With noise, and even interference – Often, with distortion We mainly concern the most common AWGN channel: – Additive noise – Noise is White and Gaussian – No distortion, maybe some amplifying of A, maybe some delay of t0 – Bandwidth is enough for a certain transmission

Introduction In this chapter, we discuss the "performance-and-cost of communication systems SNR and/or Pe(probability of error), bit-error-rate Bandwidth and/or data-rate For digital system, For PCM For analog system, bit-error-rate system, ignal-to- of: Noise-Ratio of: OOK Signal-to- noise AM BPSK SSB R atio FSK PM QPSK FM MSK

5 Introduction In this chapter, we discuss the “performance-and-cost” of communication systems – SNR and/or Pe (probability of error), bit-error-rate – Bandwidth and/or data-rate For analog system, Signal-to￾Noise-Ratio of: AM SSB PM FM For digital system, bit-error-rate of: OOK BPSK FSK QPSK MSK For PCM system, Signal-to￾Noise￾Ratio

Introduction Error probability for binary signaling 1. General process and bit error rate 2. Results for gaussian noise 3.O ptimal reception with Ma Itch-filters Performance of baseband binary systems Unipolar/Polar/Bipolar signaling Coherent detection of bandpass binary system Non-coherent detection of bandpass binary system QPSK and msK Comparison of digital signaling systems Output SNR for PCM systems Output SNR for analog systems Comparison of analog systems

6 Introduction Error probability for binary signaling 1. General process and bit error rate 2. Results for Gaussian Noise 3. Optimal reception with Match-filters Performance of baseband binary systems Unipolar/Polar/Bipolar signaling Coherent detection of bandpass binary system Non-coherent detection of bandpass binary system QPSK and MSK Comparison of digital signaling systems Output SNR for PCM systems Output SNR for analog systems Comparison of analog systems

7.1 Error Probabilities for Binary Signaling (General Results) Cha Noise Digital n(o Transmitter Recei Baseband Threshold device i Digit Processing output Sample and hold output r(t)=s(1)+n(t) ro(o) at 'o 0 ock Figure 7-1 General binary communication system Baseband signaling in Ch3, such as line-code processing= LPF+AMP Bandpass signaling in Ch4&5, as OOK, PSK, FSK, MSK, and etc. processing= superheterodyne receiver(mixer+ IF-amp+ detector) 7

7 7.1 Error Probabilities for Binary Signaling (General Results) – Baseband signaling in Ch3, such as line-code: processing = LPF+AMP – Bandpass signaling in Ch4&5, as OOK, PSK, FSK, MSK, and etc. processing = superheterodyne receiver (mixer + IF-amp + detector)

7.1 Error Probabilities for Binary Signaling (General Results) Ia data Transmitter At the input After sampling Data output signal of receiver received S,( r(t)=s, t)+n(t)ror(to)>ron Noise is o1(t sampled at to, Most added most likely close to I likely 1 0 S2(t) r()=s(t)+n(t)ro2(t)>r02 Noise is (t) sampled at to,Most added most likely close to I likely

8 7.1 Error Probabilities for Binary Signaling (General Results) data Transmitter output signal At the input of receiver After sampling Data received 1 s1 (t) r(t)=s1 (t)+n(t) Noise is added r01 (t0 ) → r01 r01 (t) sampled at t0 , most likely close to “1” 1, Most likely 0 s2 (t) r(t)=s2 (t)+n(t) Noise is added r02 (t0 ) → r02 r02 (t) sampled at t0 , most likely close to “2” 0, Most likely

7.1 Error Probabilities for Binary Signaling Src data of o and l at Source data: data=1 prob. of72 0, ald 0 randomly Transmitted signal A signal, up to src S data= l data is in the s(t)= ls,(t) 0 interval(0,T Noise added 3. Input of receiver S(t)+n(t),data=1 (t) r(t is then processed S2(t)+n(t),data=0 and ro(t)output Sampled at to, the After sampling roi(to), data=1 sync-timing; noise G i=to o lro(to), data=o is insideof ro s5. Data received ror and roz are diff, data= 1 then which data 0,≈"O"aata=0 could be told 9

9 7.1 Error Probabilities for Binary Signaling 1. Source data: 2. Transmitted signal 3. Input of receiver 4. After sampling 5. Data received 0 1 0, 1, = =    = data data m 0 1 ( ), ( ), ( ) 2 1 = =    = data data s t s t s t    = = + + = 0 1 ( ) ( ), ( ) ( ), ( ) 2 1 data data s t n t s t n t r t    = = = = 0 1 ( ), ( ), ( ) 02 0 01 0 0 0 0 data data r t r t r r t    = =    0 1 "0" "1" 0, 1, ~ 0 0 data data r r m Src data of 0 and 1 at prob. of ½ randomly A signal, up to src data is in the interval (0,T) Noise added r(t) is then processed and r0 (t) output. Sampled at t0, the sync-timing; noise is inside of r0 r01 and r02 are diff, then which data could be told

7.1 Error Probabilities for Binary Signaling rEceiver: detection+ decision In the receiver, detect-processing is used to convert the waveforms(t) intor 0 With no noise, S,(t and s2(t) are mapped to the apart"clean centers The noise corrupts the waveform and then the ro is "dirty and away from the center. dEcision is to obtain o or 1 from the r The rule is“ go to the nearest neighbor” Or equivalent to: use a threshold vr to seperate. Channel Digital n( Transmitter Receiver Threshold Digital Sample devic and hold r()=s()+n(t) at fo o(o)- 10 Figure 7-1 General binary communication system

10 7.1 Error Probabilities for Binary Signaling • Receiver: detection + decision In the receiver, detect-processing is used to convert the waveforms r(t) into r0 – With no noise, s1 (t) and s2 (t) are mapped to the apart “clean centers” – The noise corrupts the waveform and then the r0 is “dirty” and away from the center. Decision is to obtain 0 or 1 from the r0 – The rule is “go to the nearest neighbor” – Or equivalent to: use a threshold VT to seperate