lecture 1: introduction to wired and wireless communications
- 1. Lecture 2: Modern Communications Systems John M Pauly September 19, 2021
- 2. Communication Systems Overview L&D Chapter 1 I Information representation I Communication system block diagrams I Analog versus digital systems I Performance metrics I Data rate limits Next week: signals and signal space (L&D chapter 2) Based on Notes from John Gill
- 3. Types of Information I Major classification of data: analog vs. digital I Analog signals I speech (but words are discrete) I music (closer to a continuous signal) I temperature readings, barometric pressure, wind speed I images stored on film I Analog signals can be represented (approximately) using bits I digitized images (can be compressed using JPEG) I digitized video (can be compressed to MPEG) I Bits: text, computer data I Analog signals can be converted into bits by quantizing/digitizing The word “bit” was coined in the late 1940s by John Tukey
- 4. Analog Messages I Early analog communication I telephone (1876) I phonograph (1877) I film soundtrack (1923, Lee De Forest, Joseph Tykociński-Tykociner) I Key to analog communication is the amplifier (1908, Lee De Forest, triode vacuum tube) I Broadcast radio (AM, FM) is still analog I Broadcast television was analog until 2009
- 5. Digital Messages I Early long-distance communication was digital I semaphores, white flag, smoke signals, bugle calls, telegraph I Teletypewriters (stock quotations) I Baudot (1874) created 5-unit code for alphabet. Today baud is a unit meaning one symbol per second. I Working teleprinters were in service by 1924 at 65 words per minute I Fax machines: Group 3 (voice lines) and Group 4 (ISDN) I In 1990s the accounted for majority of transPacific telephone use. Sadly, fax machines are still in use. I First fax machine was Alexander Bain 1843 device required conductive ink I Pantelegraph (Caselli, 1865) set up telefax between Paris and Lyon I Ethernet, Internet There is no name for the unit bit/second. I have proposed claude.
- 6. Communication System Block Diagram (Basic) I Source encoder converts message into message signal (bits) I Transmitter converts message signal into format appropriate for channel transmission (analog/digital signal) I Channel conveys signal but may introduce attenuation, distortion, noise, interference I Receiver decodes received signal back to message signal I Source decoder decodes message signal back into original message
- 7. Communication System Block Diagram (Advanced) Encoder Channel Modulator Encrypt Demodulator Decrypt Decoder Channel Source Encoder Sink Source Source Decoder Noise Channel I Source encoder compresses message to remove redundancy I Encryption protects against eavesdroppers and false messages I Channel encoder adds redundancy for error protection I Modulator converts digital inputs to signals suitable for physical channel
- 8. Examples of Communication Channels I Communication systems convert information into a format appropriate for the transmission medium I Some channels convey electromagnetic waves (signals). I Radio (20 KHz to 20+ GHz) I Optical fiber (200 THz or 1550 nm) I Laser line-of-sight (e.g., from Mars) I Other channels use sound, smell, pressure, chemical reactions I smell: ants I chemical reactions: neuron dendrites I dance: bees I Analog communication systems convert (modulate) analog signals into modulated (analog) signals I Digital communication systems convert information in the form of bits into binary/digital signals
- 9. Physical Channels I Physical channels have constraints on what kinds of signals can be transmitted I Radio uses E&M waves at various frequencies I Submarine communication at about 20 KHz I Cordless telephones: 45 MHz, 900 MHz, 2.4 GHz, 5.8 GHz, 1.9 GHz I Wired links may require DC balanced codes to prevent voltage build up I Fiber optic channels use 4B5B modulation to accommodate time-varying attenuation I CD and DVD media require minimum spot size but position can be more precise I The process of creating a signal suitable for transmission is called modulation (modulate from Latin to regulate)
- 10. AM and FM Modulation (a) Carrier (b) Signal (c) Amplitude modulated (d) Frequency modulated
- 11. Analog vs. Digital Systems I Analog signals Values varies continously I Digital signals Value limited to a finite set Digital systems are more robust I Binary signals Have 2 possible values Used to represent bit values Bit time T needed to send 1 bit Data rate R = 1/T bits per second
- 12. Sampling and Quantization, I To transmit analog signals over a digital communication link, we must discretize both time and values. Quantization spacing is 2mp L ; sampling interval is T, not shown in figure.
- 13. Sampling and Quantization, II I Usually sample times are uniformly spaced (although, this is not always true). Higher frequency content requires faster sampling. (Soprano must be sampled twice as fast as a tenor.) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 −0.2 −0.1 0 0.1 0.2 I Quantization levels can be uniformly spaced, but nonuniform (logarithmic) spacing is often used for voice.
- 14. Digital Transmission and Regeneration Simplest digital communication is binary amplitude-shift keying (ASK) (a) binary signal input to channel; (b) signal altered by channel; (c) signal + noise; (d) signal after detection by receiver
- 15. Channel Errors If there is too much channel distortion or noise, receiver may make a mistake, and the regenerated signal will be incorrect. Channel coding is needed to detect and correct the message. 0 1 2 3 4 5 6 7 8 9 10 −2 −1 0 1 2 A 0 1 2 3 4 5 6 7 8 9 10 −4 −2 0 2 4 B 0 1 2 3 4 5 6 7 8 9 10 −2 −1 0 1 2 t C
- 16. Pulse Code Modulation (PCM) To communicate sampled values, we send a sequence of bits that represent the quantized value. For 16 quantization levels, 4 bits suffice. PCM can use binary representation of value. The PSTN uses companded PCM (similar to floating point)
- 17. Performance Metrics I Analog communication systems I Metric is fidelity, closeness to original signal I We want m̂(t) ≈ m(t) I A common measure of infidelity is energy of difference signal: Z T 0 |m̂(t) − m(t)|2 dt I Digital communication systems I Metrics are data rate R in bits/sec and probability of bit error Pe = P{b̂ 6= b} I Without noise, never make bit errors I With noise, Pe depends on signal and noise power, data rate, and channel characteristics.
- 18. Data Rate Limits I Data rate R is limited by signal power, noise power, distortion I Without distortion or noise, we could transmit at R = ∞ and error probably Pe = 0 I The Shannon capacity is the maximum possible data rate for a system with noise and distortion I This maximum rate can be approached with bit probability close to 0 I For additive white Gaussian noise (AWGN) channels, C = B log2(1 + SNR) I The theoretical result does not tell how to design real systems I Shannon obtained C = 32 Kbps for telephone channels I Get higher rates with modems/DSL (use much more bandwidth) I Nowhere near capacity in wireless systems
- 19. Next RTL SDR Lab Friday I We will give you your RTL SDR’s I Bring your laptops, and headphones I We’ll get you up and running! Next week I (Very brief) review of EE102A I Fourier series and Fourier transforms in 2πf I Vector space perspective of signal processing I L&D Chapter 2 (skim this, most of this should look very familiar)